



import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__snake_case						:Optional[int]      		=     '''src/diffusers'''
__snake_case						:Any      		=     '''.'''


# This is to make sure the diffusers module imported is the one in the repo.
__snake_case						:List[Any]      		=     importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
    submodule_search_locations=[DIFFUSERS_PATH],
)
__snake_case						:int      		=     spec.loader.load_module()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    return line.startswith(_UpperCAmelCase				) or len(_UpperCAmelCase				) <= 1 or re.search(R'''^\s*\)(\s*->.*:|:)\s*$'''   , _UpperCAmelCase				) is not None





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				object_name.split('''.'''				)
    __a						       =				0

    # First let's find the module where our object lives.
    __a						       =				parts[i]
    while i < len(_UpperCAmelCase				) and not os.path.isfile(os.path.join(_UpperCAmelCase   , f'{module}.py'				)				):
        i += 1
        if i < len(_UpperCAmelCase				):
            __a						       =				os.path.join(_UpperCAmelCase   , parts[i]				)
    if i >= len(_UpperCAmelCase				):
        raise ValueError(f'`object_name` should begin with the name of a module of diffusers but got {object_name}.'				)

    with open(os.path.join(_UpperCAmelCase   , f'{module}.py'				)   , '''r'''   , encoding='''utf-8'''   , newline='''\n'''				) as f:
        __a						       =				f.readlines()

    # Now let's find the class / func in the code!
    __a						       =				''''''
    __a						       =				0
    for name in parts[i + 1 :]:
        while (
            line_index < len(_UpperCAmelCase				) and re.search(Rf'^{indent}(class|def)\s+{name}(\(|\:)'   , lines[line_index]				) is None
        ):
            line_index += 1
        indent += "    "
        line_index += 1

    if line_index >= len(_UpperCAmelCase				):
        raise ValueError(f' {object_name} does not match any function or class in {module}.'				)

    # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
    __a						       =				line_index
    while line_index < len(_UpperCAmelCase				) and _should_continue(lines[line_index]   , _UpperCAmelCase				):
        line_index += 1
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]				) <= 1:
        line_index -= 1

    __a						       =				lines[start_index:line_index]
    return "".join(_UpperCAmelCase				)


__snake_case						:List[Any]      		=     re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
__snake_case						:Tuple      		=     re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
__snake_case						:Union[str, Any]      		=     re.compile(r'''<FILL\s+[^>]*>''')





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				code.split('''\n'''				)
    __a						       =				0
    while idx < len(_UpperCAmelCase				) and len(lines[idx]				) == 0:
        idx += 1
    if idx < len(_UpperCAmelCase				):
        return re.search(R'''^(\s*)\S'''   , lines[idx]				).groups()[0]
    return ""





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				len(get_indent(_UpperCAmelCase				)				) > 0
    if has_indent:
        __a						       =				f'class Bla:\n{code}'
    __a						       =				black.Mode(target_versions={black.TargetVersion.PYaa}   , line_length=119   , preview=_UpperCAmelCase				)
    __a						       =				black.format_str(_UpperCAmelCase   , mode=_UpperCAmelCase				)
    __a       ,   __a						       =				style_docstrings_in_code(_UpperCAmelCase				)
    return result[len('''class Bla:\n'''				) :] if has_indent else result





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase=False				):
    with open(_UpperCAmelCase   , '''r'''   , encoding='''utf-8'''   , newline='''\n'''				) as f:
        __a						       =				f.readlines()
    __a						       =				[]
    __a						       =				0
    # Not a for loop cause `lines` is going to change (if `overwrite=True`).
    while line_index < len(_UpperCAmelCase				):
        __a						       =				_re_copy_warning.search(lines[line_index]				)
        if search is None:
            line_index += 1
            continue

        # There is some copied code here, let's retrieve the original.
        __a       ,   __a       ,   __a						       =				search.groups()
        __a						       =				find_code_in_diffusers(_UpperCAmelCase				)
        __a						       =				get_indent(_UpperCAmelCase				)

        __a						       =				line_index + 1 if indent == theoretical_indent else line_index + 2
        __a						       =				theoretical_indent
        __a						       =				start_index

        # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
        __a						       =				True
        while line_index < len(_UpperCAmelCase				) and should_continue:
            line_index += 1
            if line_index >= len(_UpperCAmelCase				):
                break
            __a						       =				lines[line_index]
            __a						       =				_should_continue(_UpperCAmelCase   , _UpperCAmelCase				) and re.search(f'^{indent}# End copy'   , _UpperCAmelCase				) is None
        # Clean up empty lines at the end (if any).
        while len(lines[line_index - 1]				) <= 1:
            line_index -= 1

        __a						       =				lines[start_index:line_index]
        __a						       =				''''''.join(_UpperCAmelCase				)

        # Remove any nested `Copied from` comments to avoid circular copies
        __a						       =				[line for line in theoretical_code.split('''\n'''				) if _re_copy_warning.search(_UpperCAmelCase				) is None]
        __a						       =				'''\n'''.join(_UpperCAmelCase				)

        # Before comparing, use the `replace_pattern` on the original code.
        if len(_UpperCAmelCase				) > 0:
            __a						       =				replace_pattern.replace('''with'''   , ''''''				).split(''','''				)
            __a						       =				[_re_replace_pattern.search(_UpperCAmelCase				) for p in patterns]
            for pattern in patterns:
                if pattern is None:
                    continue
                __a       ,   __a       ,   __a						       =				pattern.groups()
                __a						       =				re.sub(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)
                if option.strip() == "all-casing":
                    __a						       =				re.sub(obja.lower()   , obja.lower()   , _UpperCAmelCase				)
                    __a						       =				re.sub(obja.upper()   , obja.upper()   , _UpperCAmelCase				)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
            __a						       =				blackify(lines[start_index - 1] + theoretical_code				)
            __a						       =				theoretical_code[len(lines[start_index - 1]				) :]

        # Test for a diff and act accordingly.
        if observed_code != theoretical_code:
            diffs.append([object_name, start_index]				)
            if overwrite:
                __a						       =				lines[:start_index] + [theoretical_code] + lines[line_index:]
                __a						       =				start_index + 1

    if overwrite and len(_UpperCAmelCase				) > 0:
        # Warn the user a file has been modified.
        print(f'Detected changes, rewriting {filename}.'				)
        with open(_UpperCAmelCase   , '''w'''   , encoding='''utf-8'''   , newline='''\n'''				) as f:
            f.writelines(_UpperCAmelCase				)
    return diffs





def      __snake_case	(			_UpperCAmelCase = False				):
    __a						       =				glob.glob(os.path.join(_UpperCAmelCase   , '''**/*.py'''				)   , recursive=_UpperCAmelCase				)
    __a						       =				[]
    for filename in all_files:
        __a						       =				is_copy_consistent(_UpperCAmelCase   , _UpperCAmelCase				)
        diffs += [f'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
    if not overwrite and len(_UpperCAmelCase				) > 0:
        __a						       =				'''\n'''.join(_UpperCAmelCase				)
        raise Exception(
            '''Found the following copy inconsistencies:\n'''
            + diff
            + '''\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'''				)


if __name__ == "__main__":
 __snake_case						:List[str]      		=     argparse.ArgumentParser()
 parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
 __snake_case						:int      		=     parser.parse_args()

 check_copies(args.fix_and_overwrite)







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFResNetForImageClassification, TFResNetModel
		from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor




class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:List[Any]     ,   a		:Dict     ,   a		:Any=3     ,   a		:Any=3_2     ,   a		:Optional[Any]=3     ,   a		:str=1_0     ,   a		:Union[str, Any]=[1_0, 2_0, 3_0, 4_0]     ,   a		:Optional[Any]=[1, 1, 2, 1]     ,   a		:Optional[Any]=True     ,   a		:Dict=True     ,   a		:Tuple="relu"     ,   a		:List[str]=3     ,   a		:Tuple=None     ,   )		-> Tuple:
								__UpperCamelCase	:   Optional[Any]			   =					parent
								__UpperCamelCase	:   Dict			   =					batch_size
								__UpperCamelCase	:   int			   =					image_size
								__UpperCamelCase	:   Dict			   =					num_channels
								__UpperCamelCase	:   Optional[int]			   =					embeddings_size
								__UpperCamelCase	:   List[Any]			   =					hidden_sizes
								__UpperCamelCase	:   Optional[Any]			   =					depths
								__UpperCamelCase	:   Optional[int]			   =					is_training
								__UpperCamelCase	:   Union[str, Any]			   =					use_labels
								__UpperCamelCase	:   Optional[int]			   =					hidden_act
								__UpperCamelCase	:   Tuple			   =					num_labels
								__UpperCamelCase	:   Tuple			   =					scope
								__UpperCamelCase	:   Dict			   =					len(a						)







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Any:
								__UpperCamelCase	:   List[str]			   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								__UpperCamelCase	:   List[str]			   =					None
								if self.use_labels:
															__UpperCamelCase	:   Tuple			   =					ids_tensor([self.batch_size]     ,   self.num_labels						)

								__UpperCamelCase	:   List[Any]			   =					self.get_config()

								return config, pixel_values, labels







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> int:
								return ResNetConfig(
								    num_channels=self.num_channels     ,   embeddings_size=self.embeddings_size     ,   hidden_sizes=self.hidden_sizes     ,   depths=self.depths     ,   hidden_act=self.hidden_act     ,   num_labels=self.num_labels     ,   image_size=self.image_size     ,   )







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:Dict     ,   a		:int     ,   a		:Optional[Any]						)		-> Tuple:
								__UpperCamelCase	:   str			   =					TFResNetModel(config=a						)
								__UpperCamelCase	:   Union[str, Any]			   =					model(a						)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape     ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)     ,   )







	def    _lowerCamelCase				(					self		:Union[str, Any]     ,   a		:Optional[int]     ,   a		:List[str]     ,   a		:Optional[Any]						)		-> Any:
								__UpperCamelCase	:   str			   =					self.num_labels
								__UpperCamelCase	:   Optional[int]			   =					TFResNetForImageClassification(a						)
								__UpperCamelCase	:   List[str]			   =					model(a     ,   labels=a						)
								self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)						)







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> List[str]:
								__UpperCamelCase	:   Optional[int]			   =					self.prepare_config_and_inputs()
								__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   Union[str, Any]			   =					config_and_inputs
								__UpperCamelCase	:   List[str]			   =					{"pixel_values": pixel_values}
								return config, inputs_dict




@require_tf
class  lowerCamelCase__							(				__lowercase     ,						__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
	_A								=     (
	    {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	_A								=     False
	_A								=     False
	_A								=     False
	_A								=     False
	_A								=     False







	def    _lowerCamelCase				(					self		:int						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					TFResNetModelTester(self						)
								__UpperCamelCase	:   List[Any]			   =					ConfigTester(self     ,   config_class=a     ,   has_text_modality=a						)







	def    _lowerCamelCase				(					self		:int						)		-> Dict:
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()







	def    _lowerCamelCase				(					self		:str						)		-> Optional[Any]:
								return







	@unittest.skip(reason="ResNet does not use inputs_embeds"						)
	def    _lowerCamelCase				(					self		:Tuple						)		-> Tuple:
								pass







	@unittest.skip(reason="ResNet does not support input and output embeddings"						)
	def    _lowerCamelCase				(					self		:List[Any]						)		-> List[str]:
								pass







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Tuple:
								__UpperCamelCase   ,						__UpperCamelCase	:   Union[str, Any]			   =					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															__UpperCamelCase	:   Dict			   =					model_class(a						)
															__UpperCamelCase	:   Union[str, Any]			   =					inspect.signature(model.call						)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															__UpperCamelCase	:   Dict			   =					[*signature.parameters.keys()]

															__UpperCamelCase	:   Union[str, Any]			   =					["pixel_values"]
															self.assertListEqual(arg_names[:1]     ,   a						)







	def    _lowerCamelCase				(					self		:List[str]						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Tuple:
								def check_hidden_states_output(a		:Optional[Any]     ,   a		:Optional[int]     ,   a		:List[str]						):
															__UpperCamelCase	:   int			   =					model_class(a						)
															__UpperCamelCase	:   int			   =					model(**self._prepare_for_class(a     ,   a						)						)

															__UpperCamelCase	:   Optional[Any]			   =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															__UpperCamelCase	:   Optional[int]			   =					self.model_tester.num_stages
															self.assertEqual(len(a						)     ,   expected_num_stages + 1						)

															# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]						)     ,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,   )

								__UpperCamelCase   ,						__UpperCamelCase	:   Optional[Any]			   =					self.model_tester.prepare_config_and_inputs_for_common()
								__UpperCamelCase	:   str			   =					["basic", "bottleneck"]
								for model_class in self.all_model_classes:
															for layer_type in layers_type:
																						__UpperCamelCase	:   int			   =					layer_type
																						__UpperCamelCase	:   int			   =					True
																						check_hidden_states_output(a     ,   a     ,   a						)

																						# check that output_hidden_states also work using config
																						del inputs_dict["output_hidden_states"]
																						__UpperCamelCase	:   int			   =					True

																						check_hidden_states_output(a     ,   a     ,   a						)







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Dict:
								__UpperCamelCase	:   int			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a						)







	@slow
	def    _lowerCamelCase				(					self		:Dict						)		-> Dict:
								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__UpperCamelCase	:   Optional[Any]			   =					TFResNetModel.from_pretrained(a						)
															self.assertIsNotNone(a						)




def 	_SCREAMING_SNAKE_CASE					(		)					->    int:






							'''simple docstring'''





							__UpperCamelCase	:   Tuple			   =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
							return image




@require_tf
@require_vision
class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	@cached_property
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Tuple:
								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
								    if is_vision_available()
								    else None
								)







	@slow
	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Optional[int]:
								__UpperCamelCase	:   int			   =					TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

								__UpperCamelCase	:   List[Any]			   =					self.default_image_processor
								__UpperCamelCase	:   List[str]			   =					prepare_img()
								__UpperCamelCase	:   List[str]			   =					image_processor(images=a     ,   return_tensors="tf"						)

								# forward pass
								__UpperCamelCase	:   Dict			   =					model(**a						)

								# verify the logits
								__UpperCamelCase	:   Dict			   =					tf.TensorShape((1, 1_0_0_0)						)
								self.assertEqual(outputs.logits.shape     ,   a						)

								__UpperCamelCase	:   Union[str, Any]			   =					tf.constant([-11.1069, -9.7877, -8.3777]						)

								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()     ,   a     ,   atol=1E-4						)						)