# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations

import difflib
import pathlib
import platform
import sys
import time
import traceback
from typing import Any, Mapping

import numpy as np
import onnx
import onnxruntime as ort
import torch

_REPRODUCTION_TEMPLATE = '''\
import google.protobuf.text_format
import numpy as np
from numpy import array, float16, float32, float64, int32, int64
import onnx
import onnxruntime as ort

# Run n times
N = 1

onnx_model_text = """
{onnx_model_text}
"""

ort_inputs = {ort_inputs}

# Set up the inference session
session_options = ort.SessionOptions()
session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
onnx_model = onnx.ModelProto()
google.protobuf.text_format.Parse(onnx_model_text, onnx_model)

# Uncomment this line to save the model to a file for examination
# onnx.save_model(onnx_model, "{short_test_name}.onnx")

onnx.checker.check_model(onnx_model)
session = ort.InferenceSession(onnx_model.SerializeToString(), session_options, providers=("CPUExecutionProvider",))

# Run the model
for _ in range(N):
    ort_outputs = session.run(None, ort_inputs)
'''

_ISSUE_MARKDOWN_TEMPLATE = """
### Summary

ONNX Runtime raises `{error_text}` when executing test `{test_name}` in ONNX Script `TorchLib`.

To recreate this report, use

```bash
CREATE_REPRODUCTION_REPORT=1 python -m pytest onnxscript/tests/function_libs/torch_lib/ops_test.py -k {short_test_name}
```

### To reproduce

```python
{reproduction_code}
```

### Full error stack

```
{error_stack}
```

### The ONNX model text for visualization

```
{onnx_model_textual_representation}
```

### Environment

```
{sys_info}
```
"""


_MISMATCH_MARKDOWN_TEMPLATE = """\
### Summary

The output of ONNX Runtime does not match that of PyTorch when executing test
`{test_name}`, `sample {sample_num}` in ONNX Script `TorchLib`.

To recreate this report, use

```bash
CREATE_REPRODUCTION_REPORT=1 python -m pytest onnxscript/tests/function_libs/torch_lib/ops_test.py -k {short_test_name}
```

### Inputs

Shapes: `{input_shapes}`

<details><summary>Details</summary>
<p>

```python
kwargs = {kwargs}
inputs = {inputs}
```

</p>
</details>

### Expected output

Shape: `{expected_shape}`

<details><summary>Details</summary>
<p>

```python
expected = {expected}
```

</p>
</details>

### Actual output

Shape: `{actual_shape}`

<details><summary>Details</summary>
<p>

```python
actual = {actual}
```

</p>
</details>

### Difference

<details><summary>Details</summary>
<p>

```diff
{diff}
```

</p>
</details>

### Full error stack

```
{error_stack}
```
"""


def create_reproduction_report(
    test_name: str,
    onnx_model: onnx.ModelProto,
    ort_inputs: Mapping[str, Any],
    error: Exception,
) -> None:
    # NOTE: We choose to embed the ONNX model as a string in the report instead of
    # saving it to a file because it is easier to share the report with others.
    onnx_model_text = str(onnx_model)
    with np.printoptions(threshold=sys.maxsize):
        ort_inputs = dict(ort_inputs.items())
        input_text = str(ort_inputs)
    error_text = str(error)
    error_stack = error_text + "\n" + "".join(traceback.format_tb(error.__traceback__))
    sys_info = f"""\
OS: {platform.platform()}
Python version: {sys.version}
onnx=={onnx.__version__}
onnxruntime=={ort.__version__}
numpy=={np.__version__}
torch=={torch.__version__}"""
    short_test_name = test_name.split(".")[-1]
    reproduction_code = _REPRODUCTION_TEMPLATE.format(
        onnx_model_text=onnx_model_text,
        ort_inputs=input_text,
        short_test_name=short_test_name,
    )
    onnx_model_textual_representation = onnx.printer.to_text(onnx_model)

    markdown = _ISSUE_MARKDOWN_TEMPLATE.format(
        error_text=error_text,
        test_name=test_name,
        short_test_name=short_test_name,
        reproduction_code=reproduction_code,
        error_stack=error_stack,
        sys_info=sys_info,
        onnx_model_textual_representation=onnx_model_textual_representation,
    )

    # Turn test name into a valid file name
    markdown_file_name = f"{short_test_name.replace('/', '-').replace(':', '-')}-{str(time.time()).replace('.', '_')}.md"
    markdown_file_path = save_error_report(markdown_file_name, markdown)
    print(f"Created reproduction report at {markdown_file_path}")


def create_mismatch_report(
    test_name: str,
    sample_num: int,
    inputs,
    kwargs,
    actual,
    expected,
    error: Exception,
) -> None:
    torch.set_printoptions(threshold=sys.maxsize)

    error_text = str(error)
    error_stack = error_text + "\n" + "".join(traceback.format_tb(error.__traceback__))
    short_test_name = test_name.split(".")[-1]
    diff = difflib.unified_diff(
        str(actual).splitlines(),
        str(expected).splitlines(),
        fromfile="actual",
        tofile="expected",
        lineterm="",
    )
    input_shapes = repr(
        [
            f"Tensor<{inp.shape}, dtype={inp.dtype}>" if isinstance(inp, torch.Tensor) else inp
            for inp in inputs
        ]
    )
    markdown = _MISMATCH_MARKDOWN_TEMPLATE.format(
        test_name=test_name,
        short_test_name=short_test_name,
        sample_num=sample_num,
        input_shapes=input_shapes,
        inputs=inputs,
        kwargs=kwargs,
        expected=expected,
        expected_shape=expected.shape if isinstance(expected, torch.Tensor) else None,
        actual=actual,
        actual_shape=actual.shape if isinstance(actual, torch.Tensor) else None,
        diff="\n".join(diff),
        error_stack=error_stack,
    )

    markdown_file_name = f"mismatch-{short_test_name.replace('/', '-').replace(':', '-')}-{str(time.time()).replace('.', '_')}.md"
    markdown_file_path = save_error_report(markdown_file_name, markdown)
    print(f"Created reproduction report at {markdown_file_path}")


def save_error_report(file_name: str, text: str):
    reports_dir = pathlib.Path("error_reports")
    reports_dir.mkdir(parents=True, exist_ok=True)
    file_path = reports_dir / file_name
    with open(file_path, "w", encoding="utf-8") as f:
        f.write(text)

    return file_path
