

"""simple docstring"""




import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
	from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor





class 			__UpperCamelCase :


		def __init__(self :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		List[str]=1_3     ,     __SCREAMING_SNAKE_CASE :		List[str]=3_2     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=2     ,     __SCREAMING_SNAKE_CASE :		str=3     ,     __SCREAMING_SNAKE_CASE :		Dict=1_6     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=[1, 2, 1]     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=[2, 2, 4]     ,     __SCREAMING_SNAKE_CASE :		Tuple=2     ,     __SCREAMING_SNAKE_CASE :		List[Any]=2.0     ,     __SCREAMING_SNAKE_CASE :		Tuple=True     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=0.0     ,     __SCREAMING_SNAKE_CASE :		int=0.0     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=0.1     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]="gelu"     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=False     ,     __SCREAMING_SNAKE_CASE :		Dict=True     ,     __SCREAMING_SNAKE_CASE :		int=0.0_2     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=1E-5     ,     __SCREAMING_SNAKE_CASE :		List[str]=True     ,     __SCREAMING_SNAKE_CASE :		str=None     ,     __SCREAMING_SNAKE_CASE :		Dict=True     ,     __SCREAMING_SNAKE_CASE :		List[Any]=1_0     ,     __SCREAMING_SNAKE_CASE :		int=8     ,     __SCREAMING_SNAKE_CASE :		str=["stage1", "stage2"]     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=[1, 2]     ,     ):
							A			=					parent
							A			=					batch_size
							A			=					image_size
							A			=					patch_size
							A			=					num_channels
							A			=					embed_dim
							A			=					depths
							A			=					num_heads
							A			=					window_size
							A			=					mlp_ratio
							A			=					qkv_bias
							A			=					hidden_dropout_prob
							A			=					attention_probs_dropout_prob
							A			=					drop_path_rate
							A			=					hidden_act
							A			=					use_absolute_embeddings
							A			=					patch_norm
							A			=					layer_norm_eps
							A			=					initializer_range
							A			=					is_training
							A			=					scope
							A			=					use_labels
							A			=					type_sequence_label_size
							A			=					encoder_stride
							A			=					out_features
							A			=					out_indices


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							A			=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							A			=					None
							if self.use_labels:
												A			=					ids_tensor([self.batch_size]     ,     self.type_sequence_label_size)

							A			=					self.get_config()

							return config, pixel_values, labels


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							return SwinConfig(
							    image_size=self.image_size     ,     patch_size=self.patch_size     ,     num_channels=self.num_channels     ,     embed_dim=self.embed_dim     ,     depths=self.depths     ,     num_heads=self.num_heads     ,     window_size=self.window_size     ,     mlp_ratio=self.mlp_ratio     ,     qkv_bias=self.qkv_bias     ,     hidden_dropout_prob=self.hidden_dropout_prob     ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,     drop_path_rate=self.drop_path_rate     ,     hidden_act=self.hidden_act     ,     use_absolute_embeddings=self.use_absolute_embeddings     ,     path_norm=self.patch_norm     ,     layer_norm_eps=self.layer_norm_eps     ,     initializer_range=self.initializer_range     ,     encoder_stride=self.encoder_stride     ,     out_features=self.out_features     ,     out_indices=self.out_indices     ,     )


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		List[Any]):
							A			=					SwinModel(config=__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A			=					model(__SCREAMING_SNAKE_CASE)

							A			=					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
							A			=					int(config.embed_dim * 2 ** (len(config.depths) - 1))

							self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, expected_seq_len, expected_dim))


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		str):
							A			=					SwinBackbone(config=__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A			=					model(__SCREAMING_SNAKE_CASE)

							# verify hidden states
							self.parent.assertEqual(len(result.feature_maps)     ,     len(config.out_features))
							self.parent.assertListEqual(list(result.feature_maps[0].shape)     ,     [self.batch_size, model.channels[0], 1_6, 1_6])

							# verify channels
							self.parent.assertEqual(len(model.channels)     ,     len(config.out_features))

							# verify backbone works with out_features=None
							A			=					None
							A			=					SwinBackbone(config=__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A			=					model(__SCREAMING_SNAKE_CASE)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps)     ,     1)
							self.parent.assertListEqual(list(result.feature_maps[0].shape)     ,     [self.batch_size, model.channels[-1], 4, 4])

							# verify channels
							self.parent.assertEqual(len(model.channels)     ,     1)


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Dict     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]):
							A			=					SwinForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A			=					model(__SCREAMING_SNAKE_CASE)
							self.parent.assertEqual(
							    result.logits.shape     ,     (self.batch_size, self.num_channels, self.image_size, self.image_size))

							# test greyscale images
							A			=					1
							A			=					SwinForMaskedImageModeling(__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()

							A			=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
							A			=					model(__SCREAMING_SNAKE_CASE)
							self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, 1, self.image_size, self.image_size))


		def 					SCREAMING_SNAKE_CASE__   (self :		Any     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		int):
							A			=					self.type_sequence_label_size
							A			=					SwinForImageClassification(__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A			=					model(__SCREAMING_SNAKE_CASE     ,     labels=__SCREAMING_SNAKE_CASE)
							self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.type_sequence_label_size))

							# test greyscale images
							A			=					1
							A			=					SwinForImageClassification(__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()

							A			=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
							A			=					model(__SCREAMING_SNAKE_CASE)
							self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.type_sequence_label_size))


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			=					self.prepare_config_and_inputs()
							(
							    (
							    A
							)			,    (
							    A
							)			,    (
							    A
							)			,    
							)			=					config_and_inputs
							A			=					{"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class 			__UpperCamelCase (   _A     ,		_A     ,		unittest.TestCase						):
		SCREAMING_SNAKE_CASE								=    (
		    (
		        SwinModel,
		        SwinBackbone,
		        SwinForImageClassification,
		        SwinForMaskedImageModeling,
		    )
		    if is_torch_available()
		    else ()
		)
		SCREAMING_SNAKE_CASE								=    (
		    {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
		    if is_torch_available()
		    else {}
		)
		SCREAMING_SNAKE_CASE								=    True

		SCREAMING_SNAKE_CASE								=    False
		SCREAMING_SNAKE_CASE								=    False
		SCREAMING_SNAKE_CASE								=    False


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							A			=					SwinModelTester(self)
							A			=					ConfigTester(self     ,     config_class=__SCREAMING_SNAKE_CASE     ,     embed_dim=3_7)


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							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 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							return


		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							A			=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)


		@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							pass


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							super().test_training_gradient_checkpointing()


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							A			=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							A			=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE)


		@unittest.skip(reason="Swin does not use inputs_embeds")
		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							pass


		@unittest.skip(reason="Swin Transformer does not use feedforward chunking")
		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							pass


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												A			=					model_class(__SCREAMING_SNAKE_CASE)
												self.assertIsInstance(model.get_input_embeddings()     ,     (nn.Module))
												A			=					model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE     ,     nn.Linear))


		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												A			=					model_class(__SCREAMING_SNAKE_CASE)
												A			=					inspect.signature(model.forward)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												A			=					[*signature.parameters.keys()]

												A			=					["pixel_values"]
												self.assertListEqual(arg_names[:1]     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()
							A			=					True

							for model_class in self.all_model_classes:
												A			=					True
												A			=					False
												A			=					True
												A			=					model_class(__SCREAMING_SNAKE_CASE)
												model.to(__SCREAMING_SNAKE_CASE)
												model.eval()
												with torch.no_grad():
																	A			=					model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE))
												A			=					outputs.attentions
												A			=					len(self.model_tester.depths)
												self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,     __SCREAMING_SNAKE_CASE)

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												A			=					True
												A			=					config.window_size**2
												A			=					model_class(__SCREAMING_SNAKE_CASE)
												model.to(__SCREAMING_SNAKE_CASE)
												model.eval()
												with torch.no_grad():
																	A			=					model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE))
												A			=					outputs.attentions
												self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,     __SCREAMING_SNAKE_CASE)

												self.assertListEqual(
												    list(attentions[0].shape[-3:])     ,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,     )
												A			=					len(__SCREAMING_SNAKE_CASE)

												# Check attention is always last and order is fine
												A			=					True
												A			=					True
												A			=					model_class(__SCREAMING_SNAKE_CASE)
												model.to(__SCREAMING_SNAKE_CASE)
												model.eval()
												with torch.no_grad():
																	A			=					model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE))

												# also another +1 for reshaped_hidden_states
												A			=					1 if model_class.__name__ == "SwinBackbone" else 2
												self.assertEqual(out_len + added_hidden_states     ,     len(__SCREAMING_SNAKE_CASE))

												A			=					outputs.attentions

												self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,     __SCREAMING_SNAKE_CASE)

												self.assertListEqual(
												    list(self_attentions[0].shape[-3:])     ,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,     )


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		Dict     ,     __SCREAMING_SNAKE_CASE :		List[Any]):
							A			=					model_class(__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()

							with torch.no_grad():
												A			=					model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE))

							A			=					outputs.hidden_states

							A			=					getattr(
							    self.model_tester     ,     "expected_num_hidden_layers"     ,     len(self.model_tester.depths) + 1)
							self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,     __SCREAMING_SNAKE_CASE)

							# Swin has a different seq_length
							A			=					(
							    config.patch_size
							    if isinstance(config.patch_size     ,     collections.abc.Iterable)
							    else (config.patch_size, config.patch_size)
							)

							A			=					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:])     ,     [num_patches, self.model_tester.embed_dim]     ,     )

							if not model_class.__name__ == "SwinBackbone":
												A			=					outputs.reshaped_hidden_states
												self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,     __SCREAMING_SNAKE_CASE)

												A			,    A			,    A			,    A			=					reshaped_hidden_states[0].shape
												A			=					(
												    reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     height * width).permute(0     ,     2     ,     1)
												)
												self.assertListEqual(
												    list(reshaped_hidden_states.shape[-2:])     ,     [num_patches, self.model_tester.embed_dim]     ,     )


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()

							A			=					(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size     ,     collections.abc.Iterable)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes:
												A			=					True
												self.check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												A			=					True

												self.check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()
							A			=					3

							A			=					(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size     ,     collections.abc.Iterable)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							A			=					(
							    config.patch_size
							    if isinstance(config.patch_size     ,     collections.abc.Iterable)
							    else (config.patch_size, config.patch_size)
							)

							A			=					image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							A			=					image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
												A			=					True
												self.check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     (padded_height, padded_width))

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												A			=					True
												self.check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     (padded_height, padded_width))


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A			=					SwinModel.from_pretrained(__SCREAMING_SNAKE_CASE)
												self.assertIsNotNone(__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()

							A			=					_config_zero_init(__SCREAMING_SNAKE_CASE)
							for model_class in self.all_model_classes:
												A			=					model_class(config=__SCREAMING_SNAKE_CASE)
												for name, param in model.named_parameters():
																	if "embeddings" not in name and param.requires_grad:
																						self.assertIn(
																						    ((param.data.mean() * 1E9).round() / 1E9).item()     ,     [0.0, 1.0]     ,     msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""     ,     )





@require_vision
@require_torch
class 			__UpperCamelCase (   unittest.TestCase						):


		@cached_property
		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							return (
							    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
							    if is_vision_available()
							    else None
							)


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							A			=					SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224").to(__SCREAMING_SNAKE_CASE)
							A			=					self.default_image_processor

							A			=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
							A			=					image_processor(images=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt").to(__SCREAMING_SNAKE_CASE)

							# forward pass
							with torch.no_grad():
												A			=					model(**__SCREAMING_SNAKE_CASE)

							# verify the logits
							A			=					torch.Size((1, 1_0_0_0))
							self.assertEqual(outputs.logits.shape     ,     __SCREAMING_SNAKE_CASE)
							A			=					torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]).to(__SCREAMING_SNAKE_CASE)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-4))





@require_torch
class 			__UpperCamelCase (   unittest.TestCase     ,		_A						):
		SCREAMING_SNAKE_CASE								=    (SwinBackbone,) if is_torch_available() else ()
		SCREAMING_SNAKE_CASE								=    SwinConfig


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			=					SwinModelTester(self)








"""simple docstring"""




import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__A     :    List[str]    =		'src/diffusers'
__A     :    Optional[int]    =		'docs/source/en'
__A     :    Union[str, Any]    =		'.'
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							"r"			,							encoding="utf-8"			,							newline="\n"      ) as f:
										A			=					f.readlines()
					# Find the start prompt.
					A			=					0
					while not lines[start_index].startswith(lowercase__      ):
										start_index += 1
					start_index += 1

					A			=					start_index
					while not lines[end_index].startswith(lowercase__      ):
										end_index += 1
					end_index -= 1

					while len(lines[start_index]      ) <= 1:
										start_index += 1
					while len(lines[end_index]      ) <= 1:
										end_index -= 1
					end_index += 1
					return "".join(lines[start_index:end_index]      ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__A     :    Any    =		'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__A     :    Union[str, Any]    =		re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__A     :    int    =		re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__A     :    Any    =		re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
__A     :    Optional[int]    =		importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__A     :    Tuple    =		spec.loader.load_module()
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"			,							lowercase__      )
					return [m.group(0      ) for m in matches]
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					2 if text == "✅" or text == "❌" else len(lowercase__      )
					A			=					(width - text_length) // 2
					A			=					width - text_length - left_indent
					return " " * left_indent + text + " " * right_indent
def    __SCREAMING_SNAKE_CASE						(						):
					"""simple docstring"""
					A			=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					A			=					{
					    name: config_mapping_names[code]
					    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
					    if code in config_mapping_names
					}
					A			=					{name: config.replace("ConfigMixin"			,							""      ) for name, config in model_name_to_config.items()}

					# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )

					# Let's lookup through all diffusers object (once).
					for attr_name in dir(lowercase__      ):
										A			=					None
										if attr_name.endswith("Tokenizer"      ):
															A			=					slow_tokenizers
															A			=					attr_name[:-9]
										elif attr_name.endswith("TokenizerFast"      ):
															A			=					fast_tokenizers
															A			=					attr_name[:-13]
										elif _re_tf_models.match(lowercase__      ) is not None:
															A			=					tf_models
															A			=					_re_tf_models.match(lowercase__      ).groups()[0]
										elif _re_flax_models.match(lowercase__      ) is not None:
															A			=					flax_models
															A			=					_re_flax_models.match(lowercase__      ).groups()[0]
										elif _re_pt_models.match(lowercase__      ) is not None:
															A			=					pt_models
															A			=					_re_pt_models.match(lowercase__      ).groups()[0]

										if lookup_dict is not None:
															while len(lowercase__      ) > 0:
																				if attr_name in model_name_to_prefix.values():
																									A			=					True
																									break
																				# Try again after removing the last word in the name
																				A			=					"".join(camel_case_split(lowercase__      )[:-1]      )

    # Let's build that table!
					A			=					list(model_name_to_config.keys()      )
					model_names.sort(key=str.lower      )
					A			=					["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
					# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
					A			=					[len(lowercase__      ) + 2 for c in columns]
					A			=					max([len(lowercase__      ) for name in model_names]      ) + 2

					# Build the table per se
					A			=					"|" + "|".join([_center_text(lowercase__			,							lowercase__      ) for c, w in zip(lowercase__			,							lowercase__      )]      ) + "|\n"
					# Use ":-----:" format to center-aligned table cell texts
					table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]      ) + "|\n"

					A			=					{True: "✅", False: "❌"}
					for name in model_names:
										A			=					model_name_to_prefix[name]
										A			=					[
										    name,
										    check[slow_tokenizers[prefix]],
										    check[fast_tokenizers[prefix]],
										    check[pt_models[prefix]],
										    check[tf_models[prefix]],
										    check[flax_models[prefix]],
										]
										table += "|" + "|".join([_center_text(lowercase__			,							lowercase__      ) for l, w in zip(lowercase__			,							lowercase__      )]      ) + "|\n"
					return table
def    __SCREAMING_SNAKE_CASE						(						lowercase__=False      ):
					"""simple docstring"""
					A			,    A			,    A			,    A			=					_find_text_in_file(
					    filename=os.path.join(lowercase__			,							"index.mdx"      )			,							start_prompt="<!--This table is updated automatically from the auto modules"			,							end_prompt="<!-- End table-->"			,							)
					A			=					get_model_table_from_auto_modules()

					if current_table != new_table:
										if overwrite:
															with open(os.path.join(lowercase__			,							"index.mdx"      )			,							"w"			,							encoding="utf-8"			,							newline="\n"      ) as f:
																				f.writelines(lines[:start_index] + [new_table] + lines[end_index:]      )
										else:
															raise ValueError(
															    "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."      )


if __name__ == "__main__":
	__A     :    Any    =		argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	__A     :    str    =		parser.parse_args()

	check_model_table(args.fix_and_overwrite)





