


import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
					import torch
					from torch import nn

					from transformers import MaskFormerSwinBackbone
					from transformers.models.maskformer import MaskFormerSwinModel






class 	A_		:
							def __init__(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_3       ,SCREAMING_SNAKE_CASE__		:      str=3_2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=3       ,SCREAMING_SNAKE_CASE__		:      Any=1_6       ,SCREAMING_SNAKE_CASE__		:      Dict=[1, 2, 1]       ,SCREAMING_SNAKE_CASE__		:      List[str]=[2, 2, 4]       ,SCREAMING_SNAKE_CASE__		:      Any=2       ,SCREAMING_SNAKE_CASE__		:      List[Any]=2.0       ,SCREAMING_SNAKE_CASE__		:      List[str]=True       ,SCREAMING_SNAKE_CASE__		:      Any=0.0       ,SCREAMING_SNAKE_CASE__		:      Tuple=0.0       ,SCREAMING_SNAKE_CASE__		:      List[str]=0.1       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]="gelu"       ,SCREAMING_SNAKE_CASE__		:      str=False       ,SCREAMING_SNAKE_CASE__		:      str=True       ,SCREAMING_SNAKE_CASE__		:      List[Any]=0.02       ,SCREAMING_SNAKE_CASE__		:      str=1E-5       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=True       ,SCREAMING_SNAKE_CASE__		:      List[str]=None       ,SCREAMING_SNAKE_CASE__		:      List[Any]=True       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=1_0       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=8       ,SCREAMING_SNAKE_CASE__		:      Any=["stage1", "stage2", "stage3"]       ,SCREAMING_SNAKE_CASE__		:      List[Any]=[1, 2, 3]       ,):
								__lowerCamelCase    :    Tuple 	=			parent
								__lowerCamelCase    :    Optional[Any] 	=			batch_size
								__lowerCamelCase    :    int 	=			image_size
								__lowerCamelCase    :    List[Any] 	=			patch_size
								__lowerCamelCase    :    Optional[Any] 	=			num_channels
								__lowerCamelCase    :    List[Any] 	=			embed_dim
								__lowerCamelCase    :    List[Any] 	=			depths
								__lowerCamelCase    :    Dict 	=			num_heads
								__lowerCamelCase    :    List[str] 	=			window_size
								__lowerCamelCase    :    Optional[Any] 	=			mlp_ratio
								__lowerCamelCase    :    Union[str, Any] 	=			qkv_bias
								__lowerCamelCase    :    Union[str, Any] 	=			hidden_dropout_prob
								__lowerCamelCase    :    List[str] 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    str 	=			drop_path_rate
								__lowerCamelCase    :    Any 	=			hidden_act
								__lowerCamelCase    :    List[Any] 	=			use_absolute_embeddings
								__lowerCamelCase    :    List[str] 	=			patch_norm
								__lowerCamelCase    :    int 	=			layer_norm_eps
								__lowerCamelCase    :    List[str] 	=			initializer_range
								__lowerCamelCase    :    Tuple 	=			is_training
								__lowerCamelCase    :    List[str] 	=			scope
								__lowerCamelCase    :    Any 	=			use_labels
								__lowerCamelCase    :    str 	=			type_sequence_label_size
								__lowerCamelCase    :    Tuple 	=			encoder_stride
								__lowerCamelCase    :    List[str] 	=			out_features
								__lowerCamelCase    :    int 	=			out_indices
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    int 	=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								__lowerCamelCase    :    Optional[int] 	=			None
								if self.use_labels:
									__lowerCamelCase    :    Optional[int] 	=			ids_tensor([self.batch_size]       ,self.type_sequence_label_size)

								__lowerCamelCase    :    Tuple 	=			self.get_config()

								return config, pixel_values, labels
							def    lowerCAmelCase					(		self		:      Any):
								return MaskFormerSwinConfig(
								    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    lowerCAmelCase					(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    int 	=			MaskFormerSwinModel(config=_a)
								model.to(_a)
								model.eval()
								__lowerCamelCase    :    Tuple 	=			model(_a)

								__lowerCamelCase    :    Dict 	=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
								__lowerCamelCase    :    int 	=			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    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase    :    Any 	=			MaskFormerSwinBackbone(config=_a)
								model.to(_a)
								model.eval()
								__lowerCamelCase    :    List[Any] 	=			model(_a)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps)       ,len(config.out_features))
								self.parent.assertListEqual(list(result.feature_maps[0].shape)       ,[1_3, 1_6, 1_6, 1_6])

								# verify channels
								self.parent.assertEqual(len(model.channels)       ,len(config.out_features))
								self.parent.assertListEqual(model.channels       ,[1_6, 3_2, 6_4])

								# verify ValueError
								with self.parent.assertRaises(_a):
									__lowerCamelCase    :    Optional[int] 	=			['stem']
									__lowerCamelCase    :    str 	=			MaskFormerSwinBackbone(config=_a)


							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Optional[int] 	=			self.prepare_config_and_inputs()
								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			config_and_inputs
								__lowerCamelCase    :    Optional[int] 	=			{'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class 	A_		(	__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  Any										=				(
							    (
							        MaskFormerSwinModel,
							        MaskFormerSwinBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							_UpperCAmelCase   :  Optional[Any]										=				{'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
							_UpperCAmelCase   :  Dict										=				False
							_UpperCAmelCase   :  Union[str, Any]										=				False
							_UpperCAmelCase   :  Dict										=				False
							_UpperCAmelCase   :  List[Any]										=				False
							_UpperCAmelCase   :  List[str]										=				False
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Optional[Any] 	=			MaskFormerSwinModelTester(self)
								__lowerCamelCase    :    Optional[int] 	=			ConfigTester(self       ,config_class=_a       ,embed_dim=3_7)
							@require_torch_multi_gpu
							@unittest.skip(
							    reason=(
							        '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
							        ' `nn.DataParallel`'
							    ))
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								pass
							def    lowerCAmelCase					(		self		:      List[Any]):
								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		:      Optional[int]):
								return
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    int 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_a)
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Optional[Any] 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*_a)
							@unittest.skip('Swin does not use inputs_embeds')
							def    lowerCAmelCase					(		self		:      Optional[int]):
								pass
							@unittest.skip('Swin does not support feedforward chunking')
							def    lowerCAmelCase					(		self		:      Any):
								pass
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase	,			__lowerCamelCase    :    int 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    List[Any] 	=			model_class(_a)
									self.assertIsInstance(model.get_input_embeddings()       ,(nn.Module))
									__lowerCamelCase    :    Dict 	=			model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(_a       ,nn.Linear))
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    Union[str, Any] 	=			model_class(_a)
									__lowerCamelCase    :    int 	=			inspect.signature(model.forward)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__lowerCamelCase    :    List[str] 	=			[*signature.parameters.keys()]

									__lowerCamelCase    :    Tuple 	=			['pixel_values']
									self.assertListEqual(arg_names[:1]       ,_a)
							@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions')
							def    lowerCAmelCase					(		self		:      List[str]):
								pass
							@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone')
							def    lowerCAmelCase					(		self		:      Dict):
								pass
							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    Any 	=			model_class(_a)
								model.to(_a)
								model.eval()

								with torch.no_grad():
									__lowerCamelCase    :    Any 	=			model(**self._prepare_for_class(_a       ,_a))

								__lowerCamelCase    :    Tuple 	=			outputs.hidden_states

								__lowerCamelCase    :    List[Any] 	=			getattr(
								    self.model_tester       ,'expected_num_hidden_layers'       ,len(self.model_tester.depths) + 1)
								self.assertEqual(len(_a)       ,_a)

								# Swin has a different seq_length
								__lowerCamelCase    :    Union[str, Any] 	=			(
								    config.patch_size
								    if isinstance(config.patch_size       ,collections.abc.Iterable)
								    else (config.patch_size, config.patch_size)
								)

								__lowerCamelCase    :    Tuple 	=			(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]       ,)
							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			self.model_tester.prepare_config_and_inputs_for_common()

								__lowerCamelCase    :    Dict 	=			(
								    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:
									__lowerCamelCase    :    Optional[Any] 	=			True
									self.check_hidden_states_output(_a       ,_a       ,_a       ,_a)

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

									self.check_hidden_states_output(_a       ,_a       ,_a       ,_a)
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			self.model_tester.prepare_config_and_inputs_for_common()
								__lowerCamelCase    :    Any 	=			3

								__lowerCamelCase    :    int 	=			(
								    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)
								)
								__lowerCamelCase    :    Optional[int] 	=			(
								    config.patch_size
								    if isinstance(config.patch_size       ,collections.abc.Iterable)
								    else (config.patch_size, config.patch_size)
								)

								__lowerCamelCase    :    Tuple 	=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								__lowerCamelCase    :    Union[str, Any] 	=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    List[str] 	=			True
									self.check_hidden_states_output(_a       ,_a       ,_a       ,(padded_height, padded_width))

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									__lowerCamelCase    :    int 	=			True
									self.check_hidden_states_output(_a       ,_a       ,_a       ,(padded_height, padded_width))
							@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints')
							def    lowerCAmelCase					(		self		:      List[Any]):
								pass
							@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin')
							def    lowerCAmelCase					(		self		:      Dict):
								pass
							@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin')
							def    lowerCAmelCase					(		self		:      Tuple):
								pass


							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase	,			__lowerCamelCase    :    str 	=			self.model_tester.prepare_config_and_inputs_for_common()

								def set_nan_tensor_to_zero(SCREAMING_SNAKE_CASE__		:      Optional[Any]):
									__lowerCamelCase    :    List[str] 	=			0
									return t

								def check_equivalence(SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Any={}):
									with torch.no_grad():
										__lowerCamelCase    :    int 	=			model(**_a       ,return_dict=_a       ,**_a)
										__lowerCamelCase    :    int 	=			model(**_a       ,return_dict=_a       ,**_a).to_tuple()

										def recursive_check(SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      List[Any]):
											if isinstance(_a       ,(List, Tuple)):
												for tuple_iterable_value, dict_iterable_value in zip(_a       ,_a):
													recursive_check(_a       ,_a)
											elif isinstance(_a       ,_a):
												for tuple_iterable_value, dict_iterable_value in zip(
												    tuple_object.values()       ,dict_object.values()):
													recursive_check(_a       ,_a)
											elif tuple_object is None:
												return
											else:
												self.assertTrue(
												    torch.allclose(
												        set_nan_tensor_to_zero(_a)       ,set_nan_tensor_to_zero(_a)       ,atol=1E-5)       ,msg=(
												        'Tuple and dict output are not equal. Difference:'
												        F" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
												        F" {torch.isnan(_a).any()} and `inf`: {torch.isinf(_a)}. Dict has"
												        F" `nan`: {torch.isnan(_a).any()} and `inf`: {torch.isinf(_a)}."
												    )       ,)

										recursive_check(_a       ,_a)

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    Union[str, Any] 	=			model_class(_a)
									model.to(_a)
									model.eval()

									__lowerCamelCase    :    Tuple 	=			self._prepare_for_class(_a       ,_a)
									__lowerCamelCase    :    List[Any] 	=			self._prepare_for_class(_a       ,_a)
									check_equivalence(_a       ,_a       ,_a)

									__lowerCamelCase    :    Tuple 	=			self._prepare_for_class(_a       ,_a       ,return_labels=_a)
									__lowerCamelCase    :    List[str] 	=			self._prepare_for_class(_a       ,_a       ,return_labels=_a)
									check_equivalence(_a       ,_a       ,_a)

									__lowerCamelCase    :    Optional[int] 	=			self._prepare_for_class(_a       ,_a)
									__lowerCamelCase    :    List[str] 	=			self._prepare_for_class(_a       ,_a)
									check_equivalence(_a       ,_a       ,_a       ,{'output_hidden_states': True})

									__lowerCamelCase    :    str 	=			self._prepare_for_class(_a       ,_a       ,return_labels=_a)
									__lowerCamelCase    :    int 	=			self._prepare_for_class(_a       ,_a       ,return_labels=_a)
									check_equivalence(_a       ,_a       ,_a       ,{'output_hidden_states': True})






@require_torch
class 	A_		(	unittest.TestCase      ,							__SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Dict										=				(MaskFormerSwinBackbone,) if is_torch_available() else ()
							_UpperCAmelCase   :  Union[str, Any]										=				MaskFormerSwinConfig
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    Tuple 	=			MaskFormerSwinModelTester(self)


							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			self.model_tester.prepare_config_and_inputs_for_common()
								__lowerCamelCase    :    Union[str, Any] 	=			inputs_dict['pixel_values'].shape[0]

								for backbone_class in self.all_model_classes:
									__lowerCamelCase    :    Tuple 	=			backbone_class(_a)
									backbone.to(_a)
									backbone.eval()

									__lowerCamelCase    :    Union[str, Any] 	=			backbone(**_a)

									# Test default outputs and verify feature maps
									self.assertIsInstance(outputs.feature_maps       ,_a)
									self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
									for feature_map, n_channels in zip(outputs.feature_maps       ,backbone.channels):
										self.assertTrue(feature_map.shape[:2]       ,(batch_size, n_channels))
									self.assertIsNone(outputs.hidden_states)
									self.assertIsNone(outputs.attentions)

									# Test output_hidden_states=True
									__lowerCamelCase    :    Tuple 	=			backbone(**_a       ,output_hidden_states=_a)
									self.assertIsNotNone(outputs.hidden_states)
									self.assertTrue(len(outputs.hidden_states)       ,len(backbone.stage_names))
									# We skip the stem layer
									for hidden_states, n_channels in zip(outputs.hidden_states[1:]       ,backbone.channels):
										for hidden_state in hidden_states:
											# Hidden states are in the format (batch_size, (height * width), n_channels)
											__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			hidden_state.shape
											self.assertTrue((h_batch_size, h_n_channels)       ,(batch_size, n_channels))

            # Test output_attentions=True
									if self.has_attentions:
										__lowerCamelCase    :    str 	=			backbone(**_a       ,output_attentions=_a)
										self.assertIsNotNone(outputs.attentions)







import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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		="""src/transformers"""
a		="""docs/source/en"""
a		="""."""






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	with open(lowerCamelCase__     ,			'r'     ,			encoding='utf-8'     ,			newline='\n'      ) as f:
		__lowerCamelCase    :    Any 	=			f.readlines()
	# Find the start prompt.
	__lowerCamelCase    :    List[str] 	=			0
	while not lines[start_index].startswith(lowerCamelCase__      ):
		start_index += 1
	start_index += 1

	__lowerCamelCase    :    int 	=			start_index
	while not lines[end_index].startswith(lowerCamelCase__      ):
		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		="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
a		=re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
a		=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		=re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
a		=direct_transformers_import(TRANSFORMERS_PATH)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[str]:
	__lowerCamelCase    :    int 	=			re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'     ,			lowerCamelCase__      )
	return [m.group(0      ) for m in matches]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Tuple:
	__lowerCamelCase    :    int 	=			2 if text == '✅' or text == '❌' else len(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			(width - text_length) // 2
	__lowerCamelCase    :    List[Any] 	=			width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent






def        SCREAMING_SNAKE_CASE__	(  )  ->					str:
	__lowerCamelCase    :    Union[str, Any] 	=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	__lowerCamelCase    :    List[str] 	=			{
	    name: config_maping_names[code]
	    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_maping_names
	}
	__lowerCamelCase    :    Dict 	=			{name: config.replace('Config'     ,			''      ) 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.
	__lowerCamelCase    :    Union[str, Any] 	=			collections.defaultdict(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			collections.defaultdict(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			collections.defaultdict(lowerCamelCase__      )
	__lowerCamelCase    :    List[str] 	=			collections.defaultdict(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			collections.defaultdict(lowerCamelCase__      )

	# Let's lookup through all transformers object (once).
	for attr_name in dir(lowerCamelCase__      ):
		__lowerCamelCase    :    List[Any] 	=			None
		if attr_name.endswith('Tokenizer'      ):
			__lowerCamelCase    :    Dict 	=			slow_tokenizers
			__lowerCamelCase    :    List[Any] 	=			attr_name[:-9]
		elif attr_name.endswith('TokenizerFast'      ):
			__lowerCamelCase    :    Union[str, Any] 	=			fast_tokenizers
			__lowerCamelCase    :    str 	=			attr_name[:-1_3]
		elif _re_tf_models.match(lowerCamelCase__      ) is not None:
			__lowerCamelCase    :    List[str] 	=			tf_models
			__lowerCamelCase    :    Optional[int] 	=			_re_tf_models.match(lowerCamelCase__      ).groups()[0]
		elif _re_flax_models.match(lowerCamelCase__      ) is not None:
			__lowerCamelCase    :    List[Any] 	=			flax_models
			__lowerCamelCase    :    Optional[Any] 	=			_re_flax_models.match(lowerCamelCase__      ).groups()[0]
		elif _re_pt_models.match(lowerCamelCase__      ) is not None:
			__lowerCamelCase    :    Optional[int] 	=			pt_models
			__lowerCamelCase    :    Any 	=			_re_pt_models.match(lowerCamelCase__      ).groups()[0]

		if lookup_dict is not None:
			while len(lowerCamelCase__      ) > 0:
				if attr_name in model_name_to_prefix.values():
					__lowerCamelCase    :    List[Any] 	=			True
					break
				# Try again after removing the last word in the name
				__lowerCamelCase    :    str 	=			''.join(camel_case_split(lowerCamelCase__      )[:-1]      )

    # Let's build that table!
	__lowerCamelCase    :    str 	=			list(model_name_to_config.keys()      )
	model_names.sort(key=str.lower      )
	__lowerCamelCase    :    Union[str, Any] 	=			['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).
	__lowerCamelCase    :    List[Any] 	=			[len(lowerCamelCase__      ) + 2 for c in columns]
	__lowerCamelCase    :    int 	=			max([len(lowerCamelCase__      ) for name in model_names]      ) + 2

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

	__lowerCamelCase    :    List[str] 	=			{True: '✅', False: '❌'}
	for name in model_names:
		__lowerCamelCase    :    Optional[int] 	=			model_name_to_prefix[name]
		__lowerCamelCase    :    Any 	=			[
		    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(lowerCamelCase__     ,			lowerCamelCase__      ) for l, w in zip(lowerCamelCase__     ,			lowerCamelCase__      )]      ) + "|\n"
	return table






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__=False      )  ->					Any:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			_find_text_in_file(
	    filename=os.path.join(lowerCamelCase__     ,			'index.md'      )     ,			start_prompt='<!--This table is updated automatically from the auto modules'     ,			end_prompt='<!-- End table-->'     ,			)
	__lowerCamelCase    :    List[Any] 	=			get_model_table_from_auto_modules()

	if current_table != new_table:
		if overwrite:
			with open(os.path.join(lowerCamelCase__     ,			'index.md'      )     ,			'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.md` has not been updated. Run `make fix-copies` to fix this.'      )


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

					check_model_table(args.fix_and_overwrite)




