
'''simple docstring'''

import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


UpperCamelCase_            =    {}
for task, _ in pipeline_test_mapping.items():
     UpperCamelCase_            =    {"""pt""": None, """tf""": None}


# DO **NOT** add item to this set (unless the reason is approved)
UpperCamelCase_            =    {
    """tests/models/esm/test_modeling_esmfold.py""",  # The pipeline test mapping is added to `test_modeling_esm.py`
}


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Tuple						)      ->  List[str]:

  if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
    return "pt"
  elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
    return "tf"
  elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
    return "flax"
  else:
    return None


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Dict					, _lowerCamelCase  :    Optional[Any]						)      ->  str:
  # Use the cached results
  if PIPELINE_TEST_MAPPING[task].get(_lowerCamelCase					, _lowerCamelCase						) is not None:
    return PIPELINE_TEST_MAPPING[task][framework]

  _lowerCAmelCase    :					Union[str, Any]							=   pipeline_test_mapping[task]["""test"""]
  _lowerCAmelCase    :					Optional[Any]							=   None

  if framework == "pt":
    _lowerCAmelCase    :					Optional[Any]							=   getattr(_lowerCamelCase					, """model_mapping"""					, _lowerCamelCase						)
  elif framework == "tf":
    _lowerCAmelCase    :					List[Any]							=   getattr(_lowerCamelCase					, """tf_model_mapping"""					, _lowerCamelCase						)

  if mapping is not None:
    _lowerCAmelCase    :					Any							=   dict(mapping.items()						)

  # cache the results
  _lowerCAmelCase    :					List[Any]							=   mapping
  return mapping


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    Tuple						)      ->  Dict:
  _lowerCAmelCase    :					Optional[int]							=   get_framework(_lowerCamelCase						)
  if framework is None:
    return None
  _lowerCAmelCase    :					List[Any]							=   get_mapping_for_task(_lowerCamelCase					, _lowerCamelCase						)
  if mapping is None:
    return None

  _lowerCAmelCase    :					Any							=   list({model_class.config_class for model_class in test_class.all_model_classes}						)
  if len(_lowerCamelCase						) != 1:
    raise ValueError("""There should be exactly one configuration class from `test_class.all_model_classes`."""						)

  # This could be a list/tuple of model classes, but it's rare.
  _lowerCAmelCase    :					Dict							=   mapping.get(config_classes[0]					, _lowerCamelCase						)
  if isinstance(_lowerCamelCase					, (tuple, list)						):
    _lowerCAmelCase    :					str							=   sorted(_lowerCamelCase					, key=lambda _lowerCamelCase						: x.__name__						)

  return model_class


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  int:
  _lowerCAmelCase    :					Tuple							=   [(task, get_model_for_pipeline_test(_lowerCamelCase					, _lowerCamelCase						)) for task in pipeline_test_mapping]
  _lowerCAmelCase    :					int							=   sorted([(task, model) for task, model in mapping if model is not None]					, key=lambda _lowerCamelCase						: x[0]						)

  return dict(_lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]						)      ->  int:
  _lowerCAmelCase    :					Any							=   get_framework(_lowerCamelCase						)
  if framework == "pt":
    _lowerCAmelCase    :					Dict							=   """torch"""
  _lowerCAmelCase    :					List[Any]							=   """{}"""

  _lowerCAmelCase    :					Optional[Any]							=   get_pipeline_model_mapping(_lowerCamelCase						)
  if len(_lowerCamelCase						) == 0:
    return ""

  _lowerCAmelCase    :					Tuple							=   []
  for task, model_classes in mapping.items():
    if isinstance(_lowerCamelCase					, (tuple, list)						):
      # A list/tuple of model classes
      _lowerCAmelCase    :					Optional[int]							=   """(""" + """, """.join([x.__name__ for x in model_classes]						) + """)"""
    else:
      # A single model class
      _lowerCAmelCase    :					Union[str, Any]							=   model_classes.__name__
    texts.append(f'"{task}": {value}'						)
  _lowerCAmelCase    :					str							=   """{""" + """, """.join(_lowerCamelCase						) + """}"""
  _lowerCAmelCase    :					Dict							=   f'pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}'

  return text


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[str]						)      ->  Union[str, Any]:
  _lowerCAmelCase    :					Tuple							=   {"""ModelTesterMixin""", """TFModelTesterMixin""", """FlaxModelTesterMixin"""}
  if not issubclass(_lowerCamelCase					, unittest.TestCase						):
    return False
  return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]						)						) > 0


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[Any]						)      ->  int:
  _lowerCAmelCase    :					Any							=   [x for x in get_test_classes(_lowerCamelCase						) if is_valid_test_class(_lowerCamelCase						)]

  _lowerCAmelCase    :					str							=   None
  for test_class in test_classes:
    # If a test class has defined `pipeline_model_mapping`, let's take it
    if getattr(_lowerCamelCase					, """pipeline_model_mapping"""					, _lowerCamelCase						) is not None:
      _lowerCAmelCase    :					str							=   test_class
      break
    # Take the test class with the shortest name (just a heuristic)
  if target_test_class is None and len(_lowerCamelCase						) > 0:
    _lowerCAmelCase    :					Optional[int]							=   sorted(_lowerCamelCase					, key=lambda _lowerCamelCase						: (len(x.__name__						), x.__name__)						)[0]

  return target_test_class


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    List[str]					, _lowerCamelCase  :    Dict						)      ->  Any:
  _lowerCAmelCase    :					str							=   start_idx
  for idx, line in enumerate(lines[start_idx:]						):
    _lowerCAmelCase    :					Tuple							=   len(_lowerCamelCase						) - len(line.lstrip()						)
    if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
      _lowerCAmelCase    :					Optional[Any]							=   start_idx + idx
    elif idx > 0 and indent <= indent_level:
      # Outside the definition block of `pipeline_model_mapping`
      break

  return end_idx


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int					, _lowerCamelCase  :    int=False						)      ->  Dict:
  if getattr(_lowerCamelCase					, """pipeline_model_mapping"""					, _lowerCamelCase						) is not None:
    if not overwrite:
      return "", -1

  _lowerCAmelCase    :					Any							=   get_pipeline_model_mapping_string(_lowerCamelCase						)
  if len(_lowerCamelCase						) == 0:
    return "", -1
  _lowerCAmelCase    :					Any							=   line_to_add + """\n"""

  # The code defined the class `test_class`
  _lowerCAmelCase			,   _lowerCAmelCase    :					Optional[Any]							=   inspect.getsourcelines(_lowerCamelCase						)
  # `inspect` gives the code for an object, including decorator(s) if any.
  # We (only) need the exact line of the class definition.
  for idx, line in enumerate(_lowerCamelCase						):
    if line.lstrip().startswith("""class """						):
      _lowerCAmelCase    :					List[Any]							=   class_lines[idx:]
      class_start_line_no += idx
      break
  _lowerCAmelCase    :					str							=   class_start_line_no + len(_lowerCamelCase						) - 1

  # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
  # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
  # block that appears in a `test_class`.
  _lowerCAmelCase    :					str							=   None
  # The indent level of the line at `class_lines[start_idx]` (if defined)
  _lowerCAmelCase    :					Tuple							=   0
  # To record if `pipeline_model_mapping` is found in `test_class`.
  _lowerCAmelCase    :					Tuple							=   None
  for idx, line in enumerate(_lowerCamelCase						):
    if line.strip().startswith("""all_model_classes = """						):
      _lowerCAmelCase    :					Any							=   len(_lowerCamelCase						) - len(line.lstrip()						)
      _lowerCAmelCase    :					str							=   idx
    elif line.strip().startswith("""all_generative_model_classes = """						):
      _lowerCAmelCase    :					Tuple							=   len(_lowerCamelCase						) - len(line.lstrip()						)
      _lowerCAmelCase    :					Union[str, Any]							=   idx
    elif line.strip().startswith("""pipeline_model_mapping = """						):
      _lowerCAmelCase    :					Dict							=   len(_lowerCamelCase						) - len(line.lstrip()						)
      _lowerCAmelCase    :					Optional[int]							=   idx
      _lowerCAmelCase    :					List[str]							=   line
      break

  if start_idx is None:
    return "", -1
  # Find the ending index (inclusive) of the above found block.
  _lowerCAmelCase    :					List[Any]							=   find_block_ending(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase						)

  # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
  # `is_timm_available()` instead of `is_torch_available()`.
  # Keep leading and trailing whitespaces
  _lowerCAmelCase    :					List[Any]							=   re.compile(R"""\s(is_\S+?_available\(\))\s"""						)
  for line in class_lines[start_idx : end_idx + 1]:
    _lowerCAmelCase    :					Tuple							=   r.search(_lowerCamelCase						)
    if backend_condition is not None:
      # replace the leading and trailing whitespaces to the space character " ".
      _lowerCAmelCase    :					Dict							=   """ """ + backend_condition[0][1:-1] + """ """
      _lowerCAmelCase    :					Dict							=   r.sub(_lowerCamelCase					, _lowerCamelCase						)
      break

  if def_line is None:
    # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
    # `all_model_classes` or `all_generative_model_classes`.
    _lowerCAmelCase    :					int							=   end_idx
  else:
    # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
    _lowerCAmelCase    :					str							=   start_idx - 1
    # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
    for idx in range(_lowerCamelCase					, end_idx + 1						):
      # These lines are going to be removed before writing to the test file.
      _lowerCAmelCase    :					Optional[Any]							=   None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
  _lowerCAmelCase    :					Any							=   [x.__name__ for x in test_class.__bases__]
  if "PipelineTesterMixin" not in parent_classes:
    # Put `PipelineTesterMixin` just before `unittest.TestCase`
    _lowerCAmelCase    :					Optional[Any]							=   [x for x in parent_classes if x != """TestCase"""] + ["""PipelineTesterMixin"""]
    if "TestCase" in parent_classes:
      # Here we **assume** the original string is always with `unittest.TestCase`.
      _parent_classes.append("""unittest.TestCase"""						)
    _lowerCAmelCase    :					str							=   """, """.join(_parent_classes						)
    for idx, line in enumerate(_lowerCamelCase						):
      # Find the ending of the declaration of `test_class`
      if line.strip().endswith("""):"""						):
        # mark the lines of the declaration of `test_class` to be removed
        for _idx in range(idx + 1						):
          _lowerCAmelCase    :					Optional[Any]							=   None  # noqa
        break
        # Add the new, one-line, class declaration for `test_class`
    _lowerCAmelCase    :					Optional[int]							=   f'class {test_class.__name__}({parent_classes}):\n'

  # Add indentation
  _lowerCAmelCase    :					Dict							=   """ """ * indent_level + line_to_add
  # Insert `pipeline_model_mapping` to `class_lines`.
  # (The line at `target_idx` should be kept by definition!)
  _lowerCAmelCase    :					str							=   class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
  # Remove the lines that are marked to be removed
  _lowerCAmelCase    :					Optional[Any]							=   [x for x in class_lines if x is not None]

  # Move from test class to module (in order to write to the test file)
  _lowerCAmelCase    :					Any							=   inspect.getsourcelines(inspect.getmodule(_lowerCamelCase						)						)[0]
  # Be careful with the 1-off between line numbers and array indices
  _lowerCAmelCase    :					int							=   module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
  _lowerCAmelCase    :					Any							=   """""".join(_lowerCamelCase						)

  _lowerCAmelCase    :					Optional[int]							=   inspect.getsourcefile(_lowerCamelCase						)
  with open(_lowerCamelCase					, """w"""					, encoding="""UTF-8"""					, newline="""\n"""						) as fp:
    fp.write(_lowerCamelCase						)

  return line_to_add


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    List[str]=False						)      ->  Optional[Any]:
  _lowerCAmelCase    :					Optional[int]							=   find_test_class(_lowerCamelCase						)
  if test_class:
    add_pipeline_model_mapping(_lowerCamelCase					, overwrite=_lowerCamelCase						)


if __name__ == "__main__":
     UpperCamelCase_            =    argparse.ArgumentParser()
     parser.add_argument(
         """--test_file""", type=str, help="""A path to the test file, starting with the repository's `tests` directory."""
     )
     parser.add_argument(
         """--all""",
         action="""store_true""",
         help="""If to check and modify all test files.""",
     )
     parser.add_argument(
         """--overwrite""",
         action="""store_true""",
         help="""If to overwrite a test class if it has already defined `pipeline_model_mapping`.""",
     )
     UpperCamelCase_            =    parser.parse_args()

     if not args.all and not args.test_file:
          raise ValueError("""Please specify either `test_file` or pass `--all` to check/modify all test files.""")
     elif args.all and args.test_file:
          raise ValueError("""Only one of `--test_file` and `--all` could be specified.""")

     UpperCamelCase_            =    []
     if args.test_file:
          UpperCamelCase_            =    [args.test_file]
     else:
          UpperCamelCase_            =    os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
          for test_file in glob.glob(pattern):
               # `Flax` is not concerned at this moment
               if not test_file.startswith("""test_modeling_flax_"""):
                    test_files.append(test_file)

     for test_file in test_files:
          if test_file in TEST_FILE_TO_IGNORE:
               print(F'[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.')
               continue
          add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)




'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ConvNextImageProcessor


class 					a_					(unittest.TestCase      ):




       def __init__(       self				,				snake_case_				,				snake_case_=7				,				snake_case_=3				,				snake_case_=1_8				,				snake_case_=3_0				,				snake_case_=4_0_0				,				snake_case_=True				,				snake_case_=None				,				snake_case_=0.875				,				snake_case_=True				,				snake_case_=[0.5, 0.5, 0.5]				,				snake_case_=[0.5, 0.5, 0.5]				,				):
         _lowerCAmelCase    :					List[str]							=   size if size is not None else {"""shortest_edge""": 2_0}
         _lowerCAmelCase    :					Tuple							=   parent
         _lowerCAmelCase    :					List[str]							=   batch_size
         _lowerCAmelCase    :					List[str]							=   num_channels
         _lowerCAmelCase    :					Tuple							=   image_size
         _lowerCAmelCase    :					Union[str, Any]							=   min_resolution
         _lowerCAmelCase    :					Tuple							=   max_resolution
         _lowerCAmelCase    :					List[Any]							=   do_resize
         _lowerCAmelCase    :					Dict							=   size
         _lowerCAmelCase    :					str							=   crop_pct
         _lowerCAmelCase    :					List[str]							=   do_normalize
         _lowerCAmelCase    :					Any							=   image_mean
         _lowerCAmelCase    :					Any							=   image_std







       def 							__UpperCamelCase    (       self					):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }



@require_torch
@require_vision
class 					a_					(_a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Dict      = ConvNextImageProcessor if is_vision_available() else None




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   ConvNextImageProcessingTester(self					)




       @property
       def 							__UpperCamelCase    (       self					):
         return self.image_processor_tester.prepare_image_processor_dict()




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class(**self.image_processor_dict					)
         self.assertTrue(hasattr(snake_case_				,				"""do_resize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""size"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""crop_pct"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""do_normalize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_mean"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_std"""					)					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   self.image_processing_class.from_dict(self.image_processor_dict					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 2_0}					)

         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class.from_dict(self.image_processor_dict				,				size=4_2					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 4_2}					)




       def 							__UpperCamelCase    (       self					):
         pass




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					str							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         _lowerCAmelCase    :					str							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				Image.Image					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[int]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					Optional[Any]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					int							=   self.image_processing_class(**self.image_processor_dict					)
         # create random numpy tensors
         _lowerCAmelCase    :					Any							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				numpify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				np.ndarray					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[Any]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					List[str]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)







       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					Tuple							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PyTorch tensors
         _lowerCAmelCase    :					List[str]							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				torchify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				torch.Tensor					)

         # Test not batched input
         _lowerCAmelCase    :					List[str]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					Optional[int]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)


