
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     _SCREAMING_SNAKE_CASE     :
      def __init__(	self						,		lowerCamelCase						,		lowerCamelCase=13						,		lowerCamelCase=32						,		lowerCamelCase=2						,		lowerCamelCase=3						,		lowerCamelCase=16						,		lowerCamelCase=[1, 2, 1]						,		lowerCamelCase=[2, 2, 4]						,		lowerCamelCase=2						,		lowerCamelCase=2.0						,		lowerCamelCase=True						,		lowerCamelCase=0.0						,		lowerCamelCase=0.0						,		lowerCamelCase=0.1						,		lowerCamelCase="gelu"						,		lowerCamelCase=False						,		lowerCamelCase=True						,		lowerCamelCase=0.0_2						,		lowerCamelCase=1e-5						,		lowerCamelCase=True						,		lowerCamelCase=None						,		lowerCamelCase=True						,		lowerCamelCase=10						,		lowerCamelCase=8						,		lowerCamelCase=["stage1", "stage2"]						,		lowerCamelCase=[1, 2]						,		):
          snake_case__  = parent
          snake_case__  = batch_size
          snake_case__  = image_size
          snake_case__  = patch_size
          snake_case__  = num_channels
          snake_case__  = embed_dim
          snake_case__  = depths
          snake_case__  = num_heads
          snake_case__  = window_size
          snake_case__  = mlp_ratio
          snake_case__  = qkv_bias
          snake_case__  = hidden_dropout_prob
          snake_case__  = attention_probs_dropout_prob
          snake_case__  = drop_path_rate
          snake_case__  = hidden_act
          snake_case__  = use_absolute_embeddings
          snake_case__  = patch_norm
          snake_case__  = layer_norm_eps
          snake_case__  = initializer_range
          snake_case__  = is_training
          snake_case__  = scope
          snake_case__  = use_labels
          snake_case__  = type_sequence_label_size
          snake_case__  = encoder_stride
          snake_case__  = out_features
          snake_case__  = out_indices
      def A_	(	self       ):
          snake_case__  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

          snake_case__  = self.get_config()

          return config, pixel_values, labels
      def A_	(	self       ):
          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 A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = SwinModel(config=__SCREAMING_SNAKE_CASE       )
          model.to(__SCREAMING_SNAKE_CASE       )
          model.eval()
          snake_case__  = model(__SCREAMING_SNAKE_CASE       )

          snake_case__  = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
          snake_case__  = 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 A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = SwinBackbone(config=__SCREAMING_SNAKE_CASE       )
          model.to(__SCREAMING_SNAKE_CASE       )
          model.eval()
          snake_case__  = 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], 16, 16]       )

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

          # verify backbone works with out_features=None
          snake_case__  = None
          snake_case__  = SwinBackbone(config=__SCREAMING_SNAKE_CASE       )
          model.to(__SCREAMING_SNAKE_CASE       )
          model.eval()
          snake_case__  = 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 A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = SwinForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE       )
          model.to(__SCREAMING_SNAKE_CASE       )
          model.eval()
          snake_case__  = 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
          snake_case__  = 1
          snake_case__  = SwinForMaskedImageModeling(__SCREAMING_SNAKE_CASE       )
          model.to(__SCREAMING_SNAKE_CASE       )
          model.eval()

          snake_case__  = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
          snake_case__  = model(__SCREAMING_SNAKE_CASE       )
          self.parent.assertEqual(result.logits.shape						,		(self.batch_size, 1, self.image_size, self.image_size)       )
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = self.type_sequence_label_size
          snake_case__  = SwinForImageClassification(__SCREAMING_SNAKE_CASE       )
          model.to(__SCREAMING_SNAKE_CASE       )
          model.eval()
          snake_case__  = 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
          snake_case__  = 1
          snake_case__  = SwinForImageClassification(__SCREAMING_SNAKE_CASE       )
          model.to(__SCREAMING_SNAKE_CASE       )
          model.eval()

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



      def A_	(	self       ):
          snake_case__  = self.prepare_config_and_inputs()
          (
              (
              snake_case__    
          )  ,    (
              snake_case__    
          )  ,    (
              snake_case__    
          )  ,        
          )  = config_and_inputs
          snake_case__  = {"pixel_values": pixel_values}
          return config, inputs_dict





@require_torch
class     _SCREAMING_SNAKE_CASE     (					_A				,    _A				,    unittest.TestCase       ):
      _A  :						Optional[int]					=   (
          (
              SwinModel,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      _A  :						Optional[int]					=   (
          {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
          if is_torch_available()
          else {}
      )
      _A  :						List[Any]					=   True

      _A  :						List[str]					=   False
      _A  :						Union[str, Any]					=   False
      _A  :						Optional[int]					=   False
      def A_	(	self       ):
          snake_case__  = SwinModelTester(self       )
          snake_case__  = ConfigTester(self						,		config_class=__SCREAMING_SNAKE_CASE						,		embed_dim=37       )
      def A_	(	self       ):
          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 A_	(	self       ):
          return
      def A_	(	self       ):
          snake_case__  = 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 A_	(	self       ):
          pass
      def A_	(	self       ):
          super().test_training_gradient_checkpointing()
      def A_	(	self       ):
          snake_case__  = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE       )
      def A_	(	self       ):
          snake_case__  = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE       )
      def A_	(	self       ):
          snake_case__  = 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 A_	(	self       ):
          pass
      @unittest.skip(reason="Swin Transformer does not use feedforward chunking"       )
      def A_	(	self       ):
          pass
      def A_	(	self       ):
          snake_case__  ,    snake_case__  = self.model_tester.prepare_config_and_inputs_for_common()

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

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

              snake_case__  = ["pixel_values"]
              self.assertListEqual(arg_names[:1]						,		__SCREAMING_SNAKE_CASE       )
      def A_	(	self       ):
          snake_case__  ,    snake_case__  = self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__  = True

          for model_class in self.all_model_classes:
              snake_case__  = True
              snake_case__  = False
              snake_case__  = True
              snake_case__  = model_class(__SCREAMING_SNAKE_CASE       )
              model.to(__SCREAMING_SNAKE_CASE       )
              model.eval()
              with torch.no_grad():
                  snake_case__  = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       )       )
              snake_case__  = outputs.attentions
              snake_case__  = 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"]
              snake_case__  = True
              snake_case__  = config.window_size**2
              snake_case__  = model_class(__SCREAMING_SNAKE_CASE       )
              model.to(__SCREAMING_SNAKE_CASE       )
              model.eval()
              with torch.no_grad():
                  snake_case__  = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       )       )
              snake_case__  = 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]						,		)
              snake_case__  = len(__SCREAMING_SNAKE_CASE       )

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

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

              snake_case__  = 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 A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = model_class(__SCREAMING_SNAKE_CASE       )
          model.to(__SCREAMING_SNAKE_CASE       )
          model.eval()

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

          snake_case__  = outputs.hidden_states

          snake_case__  = 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
          snake_case__  = (
              config.patch_size
              if isinstance(config.patch_size						,		collections.abc.Iterable       )
              else (config.patch_size, config.patch_size)
          )

          snake_case__  = (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":
              snake_case__  = outputs.reshaped_hidden_states
              self.assertEqual(len(__SCREAMING_SNAKE_CASE       )						,		__SCREAMING_SNAKE_CASE       )

              snake_case__  ,    snake_case__  ,    snake_case__  ,    snake_case__  = reshaped_hidden_states[0].shape
              snake_case__  = (
                  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 A_	(	self       ):
          snake_case__  ,    snake_case__  = self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__  = (
              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:
              snake_case__  = 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"]
              snake_case__  = True

              self.check_hidden_states_output(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       )
      def A_	(	self       ):
          snake_case__  ,    snake_case__  = self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__  = 3

          snake_case__  = (
              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)
          )
          snake_case__  = (
              config.patch_size
              if isinstance(config.patch_size						,		collections.abc.Iterable       )
              else (config.patch_size, config.patch_size)
          )

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

          for model_class in self.all_model_classes:
              snake_case__  = 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"]
              snake_case__  = True
              self.check_hidden_states_output(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		(padded_height, padded_width)       )
      @slow
      def A_	(	self       ):
          for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__  = SwinModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
              self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )



      def A_	(	self       ):
          snake_case__  ,    snake_case__  = self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__  = _config_zero_init(__SCREAMING_SNAKE_CASE       )
          for model_class in self.all_model_classes:
              snake_case__  = 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     _SCREAMING_SNAKE_CASE     (					unittest.TestCase       ):
      @cached_property
      def A_	(	self       ):
          return (
              AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"       )
              if is_vision_available()
              else None
          )



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

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

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

          # verify the logits
          snake_case__  = torch.Size((1, 10_00)       )
          self.assertEqual(outputs.logits.shape						,		__SCREAMING_SNAKE_CASE       )
          snake_case__  = 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     _SCREAMING_SNAKE_CASE     (					unittest.TestCase				,    _A       ):
      _A  :						List[Any]					=   (SwinBackbone,) if is_torch_available() else ()
      _A  :						Dict					=   SwinConfig
      def A_	(	self       ):
          snake_case__  = 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)





