




"""simple docstring"""







import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class       SCREAMING_SNAKE_CASE__					(					_a						,				unittest.TestCase					):
						_a    						=						AudioLDMPipeline
						_a    						=						TEXT_TO_AUDIO_PARAMS
						_a    						=						TEXT_TO_AUDIO_BATCH_PARAMS
						_a    						=						frozenset(
						    [
						        'num_inference_steps',
						        'num_waveforms_per_prompt',
						        'generator',
						        'latents',
						        'output_type',
						        'return_dict',
						        'callback',
						        'callback_steps',
						    ]					)


						def   __lowercase (							self				:			Optional[int]					):
										torch.manual_seed(0					)
										lowerCAmelCase  =				UNetaDConditionModel(
										    block_out_channels=(32, 64)						,						layers_per_block=2						,						sample_size=32						,						in_channels=4						,						out_channels=4						,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,						cross_attention_dim=(32, 64)						,						class_embed_type="""simple_projection"""						,						projection_class_embeddings_input_dim=32						,						class_embeddings_concat=lowerCAmelCase						,						)
										lowerCAmelCase  =				DDIMScheduler(
										    beta_start=0.0_0085						,						beta_end=0.012						,						beta_schedule="""scaled_linear"""						,						clip_sample=lowerCAmelCase						,						set_alpha_to_one=lowerCAmelCase						,						)
										torch.manual_seed(0					)
										lowerCAmelCase  =				AutoencoderKL(
										    block_out_channels=[32, 64]						,						in_channels=1						,						out_channels=1						,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,						latent_channels=4						,						)
										torch.manual_seed(0					)
										lowerCAmelCase  =				ClapTextConfig(
										    bos_token_id=0						,						eos_token_id=2						,						hidden_size=32						,						intermediate_size=37						,						layer_norm_eps=1e-05						,						num_attention_heads=4						,						num_hidden_layers=5						,						pad_token_id=1						,						vocab_size=1000						,						projection_dim=32						,						)
										lowerCAmelCase  =				ClapTextModelWithProjection(lowerCAmelCase					)
										lowerCAmelCase  =				RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta"""						,						model_max_length=77					)

										lowerCAmelCase  =				SpeechTaHifiGanConfig(
										    model_in_dim=8						,						sampling_rate=1_6000						,						upsample_initial_channel=16						,						upsample_rates=[2, 2]						,						upsample_kernel_sizes=[4, 4]						,						resblock_kernel_sizes=[3, 7]						,						resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]						,						normalize_before=lowerCAmelCase						,						)

										lowerCAmelCase  =				SpeechTaHifiGan(lowerCAmelCase					)

										lowerCAmelCase  =				{
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """vocoder""": vocoder,
										}
										return components


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Union[str, Any]=0					):
										if str(lowerCAmelCase					).startswith("""mps"""					):
														lowerCAmelCase  =				torch.manual_seed(lowerCAmelCase					)
										else:
														lowerCAmelCase  =				torch.Generator(device=lowerCAmelCase					).manual_seed(lowerCAmelCase					)
										lowerCAmelCase  =				{
										    """prompt""": """A hammer hitting a wooden surface""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 6.0,
										}
										return inputs


						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator

										lowerCAmelCase  =				self.get_dummy_components()
										lowerCAmelCase  =				AudioLDMPipeline(**lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe.to(lowerCAmelCase					)
										audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				self.get_dummy_inputs(lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe(**lowerCAmelCase					)
										lowerCAmelCase  =				output.audios[0]

										assert audio.ndim == 1
										assert len(lowerCAmelCase					) == 256

										lowerCAmelCase  =				audio[:10]
										lowerCAmelCase  =				np.array(
										    [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]					)

										assert np.abs(audio_slice - expected_slice					).max() < 1e-2


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				self.get_dummy_components()
										lowerCAmelCase  =				AudioLDMPipeline(**lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe.to(lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe.to(lowerCAmelCase					)
										audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				self.get_dummy_inputs(lowerCAmelCase					)
										lowerCAmelCase  =				3 * [inputs["""prompt"""]]

										# forward
										lowerCAmelCase  =				audioldm_pipe(**lowerCAmelCase					)
										lowerCAmelCase  =				output.audios[0]

										lowerCAmelCase  =				self.get_dummy_inputs(lowerCAmelCase					)
										lowerCAmelCase  =				3 * [inputs.pop("""prompt"""					)]

										lowerCAmelCase  =				audioldm_pipe.tokenizer(
										    lowerCAmelCase						,						padding="""max_length"""						,						max_length=audioldm_pipe.tokenizer.model_max_length						,						truncation=lowerCAmelCase						,						return_tensors="""pt"""						,						)
										lowerCAmelCase  =				text_inputs["""input_ids"""].to(lowerCAmelCase					)

										lowerCAmelCase  =				audioldm_pipe.text_encoder(
										    lowerCAmelCase						,						)
										lowerCAmelCase  =				prompt_embeds.text_embeds
										# additional L_2 normalization over each hidden-state
										lowerCAmelCase  =				F.normalize(lowerCAmelCase						,						dim=-1					)

										lowerCAmelCase  =				prompt_embeds

										# forward
										lowerCAmelCase  =				audioldm_pipe(**lowerCAmelCase					)
										lowerCAmelCase  =				output.audios[0]

										assert np.abs(audio_a - audio_a					).max() < 1e-2


						def   __lowercase (							self				:			List[Any]					):
										lowerCAmelCase  =				self.get_dummy_components()
										lowerCAmelCase  =				AudioLDMPipeline(**lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe.to(lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe.to(lowerCAmelCase					)
										audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				self.get_dummy_inputs(lowerCAmelCase					)
										lowerCAmelCase  =				3 * ["""this is a negative prompt"""]
										lowerCAmelCase  =				negative_prompt
										lowerCAmelCase  =				3 * [inputs["""prompt"""]]

										# forward
										lowerCAmelCase  =				audioldm_pipe(**lowerCAmelCase					)
										lowerCAmelCase  =				output.audios[0]

										lowerCAmelCase  =				self.get_dummy_inputs(lowerCAmelCase					)
										lowerCAmelCase  =				3 * [inputs.pop("""prompt"""					)]

										lowerCAmelCase  =				[]
										for p in [prompt, negative_prompt]:
														lowerCAmelCase  =				audioldm_pipe.tokenizer(
														    lowerCAmelCase						,						padding="""max_length"""						,						max_length=audioldm_pipe.tokenizer.model_max_length						,						truncation=lowerCAmelCase						,						return_tensors="""pt"""						,						)
														lowerCAmelCase  =				text_inputs["""input_ids"""].to(lowerCAmelCase					)

														lowerCAmelCase  =				audioldm_pipe.text_encoder(
														    lowerCAmelCase						,						)
														lowerCAmelCase  =				text_embeds.text_embeds
														# additional L_2 normalization over each hidden-state
														lowerCAmelCase  =				F.normalize(lowerCAmelCase						,						dim=-1					)

														embeds.append(lowerCAmelCase					)

										lowerCAmelCase ,						lowerCAmelCase  =				embeds

										# forward
										lowerCAmelCase  =				audioldm_pipe(**lowerCAmelCase					)
										lowerCAmelCase  =				output.audios[0]

										assert np.abs(audio_a - audio_a					).max() < 1e-2


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
										lowerCAmelCase  =				self.get_dummy_components()
										lowerCAmelCase  =				PNDMScheduler(skip_prk_steps=lowerCAmelCase					)
										lowerCAmelCase  =				AudioLDMPipeline(**lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe.to(lowerCAmelCase					)
										audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				self.get_dummy_inputs(lowerCAmelCase					)
										lowerCAmelCase  =				"""egg cracking"""
										lowerCAmelCase  =				audioldm_pipe(**lowerCAmelCase						,						negative_prompt=lowerCAmelCase					)
										lowerCAmelCase  =				output.audios[0]

										assert audio.ndim == 1
										assert len(lowerCAmelCase					) == 256

										lowerCAmelCase  =				audio[:10]
										lowerCAmelCase  =				np.array(
										    [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]					)

										assert np.abs(audio_slice - expected_slice					).max() < 1e-2


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
										lowerCAmelCase  =				self.get_dummy_components()
										lowerCAmelCase  =				PNDMScheduler(skip_prk_steps=lowerCAmelCase					)
										lowerCAmelCase  =				AudioLDMPipeline(**lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe.to(lowerCAmelCase					)
										audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				"""A hammer hitting a wooden surface"""

										# test num_waveforms_per_prompt=1 (default)
										lowerCAmelCase  =				audioldm_pipe(lowerCAmelCase						,						num_inference_steps=2					).audios

										assert audios.shape == (1, 256)

										# test num_waveforms_per_prompt=1 (default) for batch of prompts
										lowerCAmelCase  =				2
										lowerCAmelCase  =				audioldm_pipe([prompt] * batch_size						,						num_inference_steps=2					).audios

										assert audios.shape == (batch_size, 256)

										# test num_waveforms_per_prompt for single prompt
										lowerCAmelCase  =				2
										lowerCAmelCase  =				audioldm_pipe(lowerCAmelCase						,						num_inference_steps=2						,						num_waveforms_per_prompt=lowerCAmelCase					).audios

										assert audios.shape == (num_waveforms_per_prompt, 256)

										# test num_waveforms_per_prompt for batch of prompts
										lowerCAmelCase  =				2
										lowerCAmelCase  =				audioldm_pipe(
										    [prompt] * batch_size						,						num_inference_steps=2						,						num_waveforms_per_prompt=lowerCAmelCase					).audios

										assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)


						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
										lowerCAmelCase  =				self.get_dummy_components()
										lowerCAmelCase  =				AudioLDMPipeline(**lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe.to(lowerCAmelCase					)
										audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe.vocoder.config.sampling_rate

										lowerCAmelCase  =				self.get_dummy_inputs(lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe(audio_length_in_s=0.016						,						**lowerCAmelCase					)
										lowerCAmelCase  =				output.audios[0]

										assert audio.ndim == 1
										assert len(lowerCAmelCase					) / vocoder_sampling_rate == 0.016

										lowerCAmelCase  =				audioldm_pipe(audio_length_in_s=0.032						,						**lowerCAmelCase					)
										lowerCAmelCase  =				output.audios[0]

										assert audio.ndim == 1
										assert len(lowerCAmelCase					) / vocoder_sampling_rate == 0.032


						def   __lowercase (							self				:			Any					):
										lowerCAmelCase  =				self.get_dummy_components()
										lowerCAmelCase  =				AudioLDMPipeline(**lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe.to(lowerCAmelCase					)
										audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				["""hey"""]

										lowerCAmelCase  =				audioldm_pipe(lowerCAmelCase						,						num_inference_steps=1					)
										lowerCAmelCase  =				output.audios.shape
										assert audio_shape == (1, 256)

										lowerCAmelCase  =				audioldm_pipe.vocoder.config
										config.model_in_dim *= 2
										lowerCAmelCase  =				SpeechTaHifiGan(lowerCAmelCase					).to(lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe(lowerCAmelCase						,						num_inference_steps=1					)
										lowerCAmelCase  =				output.audios.shape
										# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
										assert audio_shape == (1, 256)


						def   __lowercase (							self				:			List[str]					):
										self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										self._test_inference_batch_single_identical(test_mean_pixel_difference=lowerCAmelCase					)




						@unittest.skipIf(
						    torch_device != """cuda""" or not is_xformers_available()						,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,						)
						def   __lowercase (							self				:			Any					):
										self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCAmelCase					)






@slow
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def   __lowercase (							self				:			List[Any]					):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Optional[Any]="cpu"						,						lowerCAmelCase				:			List[str]=torch.floataa						,						lowerCAmelCase				:			List[str]=0					):
										lowerCAmelCase  =				torch.Generator(device=lowerCAmelCase					).manual_seed(lowerCAmelCase					)
										lowerCAmelCase  =				np.random.RandomState(lowerCAmelCase					).standard_normal((1, 8, 128, 16)					)
										lowerCAmelCase  =				torch.from_numpy(lowerCAmelCase					).to(device=lowerCAmelCase						,						dtype=lowerCAmelCase					)
										lowerCAmelCase  =				{
										    """prompt""": """A hammer hitting a wooden surface""",
										    """latents""": latents,
										    """generator""": generator,
										    """num_inference_steps""": 3,
										    """guidance_scale""": 2.5,
										}
										return inputs


						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""					)
										lowerCAmelCase  =				audioldm_pipe.to(lowerCAmelCase					)
										audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				self.get_inputs(lowerCAmelCase					)
										lowerCAmelCase  =				25
										lowerCAmelCase  =				audioldm_pipe(**lowerCAmelCase					).audios[0]

										assert audio.ndim == 1
										assert len(lowerCAmelCase					) == 8_1920

										lowerCAmelCase  =				audio[7_7230:7_7240]
										lowerCAmelCase  =				np.array(
										    [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]					)
										lowerCAmelCase  =				np.abs(expected_slice - audio_slice					).max()
										assert max_diff < 1e-2




						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""					)
										lowerCAmelCase  =				LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config					)
										lowerCAmelCase  =				audioldm_pipe.to(lowerCAmelCase					)
										audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				self.get_inputs(lowerCAmelCase					)
										lowerCAmelCase  =				audioldm_pipe(**lowerCAmelCase					).audios[0]

										assert audio.ndim == 1
										assert len(lowerCAmelCase					) == 8_1920

										lowerCAmelCase  =				audio[2_7780:2_7790]
										lowerCAmelCase  =				np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]					)
										lowerCAmelCase  =				np.abs(expected_slice - audio_slice					).max()
										assert max_diff < 3e-2











"""simple docstring"""







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 							lowercase					(snake_case__   : int      ,       snake_case__   : Optional[int]      ,       snake_case__   : Tuple )     ->  Any:


				'''simple docstring'''






				with open(snake_case__      ,       """r"""      ,       encoding="""utf-8"""      ,       newline="""\n""" ) as f:
								lowerCAmelCase  =				f.readlines()
				# Find the start prompt.
				lowerCAmelCase  =				0
				while not lines[start_index].startswith(snake_case__ ):
								start_index += 1
				start_index += 1

				lowerCAmelCase  =				start_index
				while not lines[end_index].startswith(snake_case__ ):
								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 							lowercase					(snake_case__   : int )     ->  Any:


				'''simple docstring'''






				lowerCAmelCase  =				re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""      ,       snake_case__ )
				return [m.group(0 ) for m in matches]





def 							lowercase					(snake_case__   : str      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				2 if text == """✅""" or text == """❌""" else len(snake_case__ )
				lowerCAmelCase  =				(width - text_length) // 2
				lowerCAmelCase  =				width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent





def 							lowercase					()     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase  =				{
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				lowerCAmelCase  =				{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  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )

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

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

    # Let's build that table!
				lowerCAmelCase  =				list(model_name_to_config.keys() )
				model_names.sort(key=str.lower )
				lowerCAmelCase  =				["""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  =				[len(snake_case__ ) + 2 for c in columns]
				lowerCAmelCase  =				max([len(snake_case__ ) for name in model_names] ) + 2

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

				lowerCAmelCase  =				{True: """✅""", False: """❌"""}
				for name in model_names:
								lowerCAmelCase  =				model_name_to_prefix[name]
								lowerCAmelCase  =				[
								    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(snake_case__      ,       snake_case__ ) for l, w in zip(snake_case__      ,       snake_case__ )] ) + "|\n"
				return table





def 							lowercase					(snake_case__   : Dict=False )     ->  Any:


				'''simple docstring'''






				lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				_find_text_in_file(
				    filename=os.path.join(snake_case__      ,       """index.md""" )      ,       start_prompt="""<!--This table is updated automatically from the auto modules"""      ,       end_prompt="""<!-- End table-->"""      ,       )
				lowerCAmelCase  =				get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(snake_case__      ,       """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)





