




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


__lowerCAmelCase		:    Optional[int]	      =		{}
for task, _ in pipeline_test_mapping.items():
		__lowerCAmelCase		:    Union[str, Any]	      =		{'pt': None, 'tf': None}


# DO **NOT** add item to this set (unless the reason is approved)
__lowerCAmelCase		:    Optional[int]	      =		{
    'tests/models/esm/test_modeling_esmfold.py',  # The pipeline test mapping is added to `test_modeling_esm.py`
}




def       __magic_name__    (			A		: List[str]       ):






			'''simple docstring'''







			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       __magic_name__    (			A		: Union[str, Any],  A		: int       ):






			'''simple docstring'''







			if PIPELINE_TEST_MAPPING[task].get(A,  A       ) is not None:
						return PIPELINE_TEST_MAPPING[task][framework]

			a   =      pipeline_test_mapping[task]["test"]
			a   =      None

			if framework == "pt":
						a   =      getattr(A,  "model_mapping",  A       )
			elif framework == "tf":
						a   =      getattr(A,  "tf_model_mapping",  A       )

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

			# cache the results
			a   =      mapping
			return mapping




def       __magic_name__    (			A		: Optional[int],  A		: Union[str, Any]       ):






			'''simple docstring'''







			a   =      get_framework(A       )
			if framework is None:
						return None
			a   =      get_mapping_for_task(A,  A       )
			if mapping is None:
						return None

			a   =      list({model_class.config_class for model_class in test_class.all_model_classes}       )
			if len(A       ) != 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.
			a   =      mapping.get(config_classes[0],  A       )
			if isinstance(A,  (tuple, list)       ):
						a   =      sorted(A,  key=lambda A       : x.__name__       )

			return model_class




def       __magic_name__    (			A		: List[Any]       ):






			'''simple docstring'''







			a   =      [(task, get_model_for_pipeline_test(A,  A       )) for task in pipeline_test_mapping]
			a   =      sorted([(task, model) for task, model in mapping if model is not None],  key=lambda A       : x[0]       )

			return dict(A       )




def       __magic_name__    (			A		: List[str]       ):






			'''simple docstring'''







			a   =      get_framework(A       )
			if framework == "pt":
						a   =      "torch"
			a   =      "{}"

			a   =      get_pipeline_model_mapping(A       )
			if len(A       ) == 0:
						return ""

			a   =      []
			for task, model_classes in mapping.items():
						if isinstance(A,  (tuple, list)       ):
									# A list/tuple of model classes
									a   =      "(" + ", ".join([x.__name__ for x in model_classes]       ) + ")"
						else:
									# A single model class
									a   =      model_classes.__name__
						texts.append(F"""\"{task}\": {value}"""       )
			a   =      "{" + ", ".join(A       ) + "}"
			a   =      F"""pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"""

			return text




def       __magic_name__    (			A		: Any       ):






			'''simple docstring'''







			a   =      {"ModelTesterMixin", "TFModelTesterMixin", "FlaxModelTesterMixin"}
			if not issubclass(A,  unittest.TestCase       ):
						return False
			return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]       )       ) > 0




def       __magic_name__    (			A		: str       ):






			'''simple docstring'''







			a   =      [x for x in get_test_classes(A       ) if is_valid_test_class(A       )]

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

			return target_test_class




def       __magic_name__    (			A		: Optional[int],  A		: List[str],  A		: Optional[int]       ):






			'''simple docstring'''







			a   =      start_idx
			for idx, line in enumerate(lines[start_idx:]       ):
						a   =      len(A       ) - len(line.lstrip()       )
						if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
									a   =      start_idx + idx
						elif idx > 0 and indent <= indent_level:
									# Outside the definition block of `pipeline_model_mapping`
									break

			return end_idx




def       __magic_name__    (			A		: List[Any],  A		: int=False       ):






			'''simple docstring'''







			if getattr(A,  "pipeline_model_mapping",  A       ) is not None:
						if not overwrite:
									return "", -1

			a   =      get_pipeline_model_mapping_string(A       )
			if len(A       ) == 0:
						return "", -1
			a   =      line_to_add + "\n"

			# The code defined the class `test_class`
			a   ,		a   =      inspect.getsourcelines(A       )
			# `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(A       ):
						if line.lstrip().startswith("class "       ):
									a   =      class_lines[idx:]
									class_start_line_no += idx
									break
			a   =      class_start_line_no + len(A       ) - 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`.
			a   =      None
			# The indent level of the line at `class_lines[start_idx]` (if defined)
			a   =      0
			# To record if `pipeline_model_mapping` is found in `test_class`.
			a   =      None
			for idx, line in enumerate(A       ):
						if line.strip().startswith("all_model_classes = "       ):
									a   =      len(A       ) - len(line.lstrip()       )
									a   =      idx
						elif line.strip().startswith("all_generative_model_classes = "       ):
									a   =      len(A       ) - len(line.lstrip()       )
									a   =      idx
						elif line.strip().startswith("pipeline_model_mapping = "       ):
									a   =      len(A       ) - len(line.lstrip()       )
									a   =      idx
									a   =      line
									break

			if start_idx is None:
						return "", -1
			# Find the ending index (inclusive) of the above found block.
			a   =      find_block_ending(A,  A,  A       )

			# 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
			a   =      re.compile(R"\s(is_\S+?_available\(\))\s"       )
			for line in class_lines[start_idx : end_idx + 1]:
						a   =      r.search(A       )
						if backend_condition is not None:
									# replace the leading and trailing whitespaces to the space character " ".
									a   =      " " + backend_condition[0][1:-1] + " "
									a   =      r.sub(A,  A       )
									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`.
						a   =      end_idx
			else:
						# `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
						a   =      start_idx - 1
						# mark the lines of the currently existing `pipeline_model_mapping` to be removed.
						for idx in range(A,  end_idx + 1       ):
									# These lines are going to be removed before writing to the test file.
									a   =      None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
			a   =      [x.__name__ for x in test_class.__bases__]
			if "PipelineTesterMixin" not in parent_classes:
						# Put `PipelineTesterMixin` just before `unittest.TestCase`
						a   =      [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"       )
						a   =      ", ".join(_parent_classes       )
						for idx, line in enumerate(A       ):
									# 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       ):
															a   =      None  # noqa
												break
        # Add the new, one-line, class declaration for `test_class`
						a   =      F"""class {test_class.__name__}({parent_classes}):\n"""

			# Add indentation
			a   =      " " * indent_level + line_to_add
			# Insert `pipeline_model_mapping` to `class_lines`.
			# (The line at `target_idx` should be kept by definition!)
			a   =      class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
			# Remove the lines that are marked to be removed
			a   =      [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)
			a   =      inspect.getsourcelines(inspect.getmodule(A       )       )[0]
			# Be careful with the 1-off between line numbers and array indices
			a   =      module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
			a   =      "".join(A       )

			a   =      inspect.getsourcefile(A       )
			with open(A,  "w",  encoding="UTF-8",  newline="\n"       ) as fp:
						fp.write(A       )

			return line_to_add




def       __magic_name__    (			A		: str,  A		: Union[str, Any]=False       ):






			'''simple docstring'''







			a   =      find_test_class(A       )
			if test_class:
						add_pipeline_model_mapping(A,  overwrite=A       )


if __name__ == "__main__":
		__lowerCAmelCase		:    Optional[Any]	      =		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`.',
		)
		__lowerCAmelCase		:    int	      =		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.')

		__lowerCAmelCase		:    Optional[Any]	      =		[]
		if args.test_file:
				__lowerCAmelCase		:    Dict	      =		[args.test_file]
		else:
				__lowerCAmelCase		:    str	      =		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)












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_	:							List[Any]										= """AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_	:							Dict										= """AutoTokenizer"""





				def __init__(				self    :					int ,				__lowerCamelCase    :					Dict=None ,				__lowerCamelCase    :					int=None ,				**__lowerCamelCase    :					Optional[Any]							) ->					str:
							a   =      None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead." ,				__lowerCamelCase ,				)
										a   =      kwargs.pop("feature_extractor"							)

							a   =      image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError("You need to specify an `image_processor`."							)
							if tokenizer is None:
										raise ValueError("You need to specify a `tokenizer`."							)

							super().__init__(__lowerCamelCase ,				__lowerCamelCase							)
							a   =      self.image_processor
							a   =      False





				def __call__(				self    :					Any ,				*__lowerCamelCase    :					int ,				**__lowerCamelCase    :					Optional[int]							) ->					int:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase							)

							a   =      kwargs.pop("images" ,				__lowerCamelCase							)
							a   =      kwargs.pop("text" ,				__lowerCamelCase							)
							if len(__lowerCamelCase							) > 0:
										a   =      args[0]
										a   =      args[1:]

							if images is None and text is None:
										raise ValueError("You need to specify either an `images` or `text` input to process."							)

							if images is not None:
										a   =      self.image_processor(__lowerCamelCase ,				*__lowerCamelCase ,				**__lowerCamelCase							)
							if text is not None:
										a   =      self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase							)

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										a   =      encodings["input_ids"]
										return inputs





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				*__lowerCamelCase    :					str ,				**__lowerCamelCase    :					str							) ->					Optional[Any]:
							return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Any							) ->					str:
							return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				@contextmanager
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your images inputs, or in a separate call."							)
							a   =      True
							a   =      self.tokenizer
							yield
							a   =      self.image_processor
							a   =      False





				@property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,				__lowerCamelCase ,				)
							return self.image_processor_class





				@property
				def 							__UpperCAmelCase							(				self    :					int							) ->					Union[str, Any]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,				__lowerCamelCase ,				)
							return self.image_processor






