



"""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
__lowerCAmelCase       :					List[Any]      					=					'''src/transformers'''
__lowerCAmelCase       :					Dict      					=					'''docs/source/en'''
__lowerCAmelCase       :					Optional[Any]      					=					'''.'''
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any					, __UpperCamelCase      :			List[str]					, __UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






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

						snake_case_   : Union[str, Any]						=			start_index
						while not lines[end_index].startswith(__UpperCamelCase    ):
												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 |
__lowerCAmelCase       :					int      					=					'''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase       :					int      					=					re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__lowerCAmelCase       :					Any      					=					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.
__lowerCAmelCase       :					List[str]      					=					re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase       :					int      					=					direct_transformers_import(TRANSFORMERS_PATH)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






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


						'''simple docstring'''






						snake_case_   : Dict						=			2 if text == """✅""" or text == """❌""" else len(__UpperCamelCase    )
						snake_case_   : Tuple						=			(width - text_length) // 2
						snake_case_   : Any						=			width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : List[Any]						=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						snake_case_   : Optional[Any]						=			{
						    name: config_maping_names[code]
						    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_maping_names
						}
						snake_case_   : List[str]						=			{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.
						snake_case_   : Dict						=			collections.defaultdict(__UpperCamelCase    )
						snake_case_   : List[Any]						=			collections.defaultdict(__UpperCamelCase    )
						snake_case_   : str						=			collections.defaultdict(__UpperCamelCase    )
						snake_case_   : str						=			collections.defaultdict(__UpperCamelCase    )
						snake_case_   : Dict						=			collections.defaultdict(__UpperCamelCase    )

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

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

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

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

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


						'''simple docstring'''






						snake_case_   : Optional[int]						=			_find_text_in_file(
						    filename=os.path.join(__UpperCamelCase					, """index.md"""    )					, start_prompt="""<!--This table is updated automatically from the auto modules"""					, end_prompt="""<!-- End table-->"""					, )
						snake_case_   : int						=			get_model_table_from_auto_modules()

						if current_table != new_table:
												if overwrite:
																		with open(os.path.join(__UpperCamelCase					, """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__":
		__lowerCAmelCase       :					List[Any]      					=					argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		__lowerCAmelCase       :					Any      					=					parser.parse_args()

		check_model_table(args.fix_and_overwrite)






"""simple docstring"""





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


__lowerCAmelCase       :					Optional[Any]      					=					False



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





		@property
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								return 1_2





		@property
		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								return 1_2





		@property
		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								return 3_2





		@property
		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								torch.manual_seed(0   )
								snake_case_   : List[Any]						=			VQModel(
								    block_out_channels=[3_2, 6_4]							,    in_channels=3							,    out_channels=3							,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]							,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]							,    latent_channels=3							,    num_vq_embeddings=self.num_embed							,    vq_embed_dim=3							,    )
								return model





		@property
		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : int						=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )
								return tokenizer





		@property
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								torch.manual_seed(0   )
								snake_case_   : Dict						=			CLIPTextConfig(
								    bos_token_id=0							,    eos_token_id=2							,    hidden_size=self.text_embedder_hidden_size							,    intermediate_size=3_7							,    layer_norm_eps=1E-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=1_0_0_0							,    )
								return CLIPTextModel(_lowercase   )





		@property
		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								torch.manual_seed(0   )

								snake_case_   : Union[str, Any]						=			1_2
								snake_case_   : Tuple						=			1_2

								snake_case_   : Tuple						=			{
								    """attention_bias""": True,
								    """cross_attention_dim""": 3_2,
								    """attention_head_dim""": height * width,
								    """num_attention_heads""": 1,
								    """num_vector_embeds""": self.num_embed,
								    """num_embeds_ada_norm""": self.num_embeds_ada_norm,
								    """norm_num_groups""": 3_2,
								    """sample_size""": width,
								    """activation_fn""": """geglu-approximate""",
								}

								snake_case_   : Optional[Any]						=			TransformeraDModel(**_lowercase   )
								return model





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : str						=			"""cpu"""

								snake_case_   : List[str]						=			self.dummy_vqvae
								snake_case_   : Any						=			self.dummy_text_encoder
								snake_case_   : Tuple						=			self.dummy_tokenizer
								snake_case_   : int						=			self.dummy_transformer
								snake_case_   : int						=			VQDiffusionScheduler(self.num_embed   )
								snake_case_   : Dict						=			LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase   )

								snake_case_   : Optional[Any]						=			VQDiffusionPipeline(
								    vqvae=_lowercase							,    text_encoder=_lowercase							,    tokenizer=_lowercase							,    transformer=_lowercase							,    scheduler=_lowercase							,    learned_classifier_free_sampling_embeddings=_lowercase							,    )
								snake_case_   : int						=			pipe.to(_lowercase   )
								pipe.set_progress_bar_config(disable=_lowercase   )

								snake_case_   : List[Any]						=			"""teddy bear playing in the pool"""

								snake_case_   : Dict						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : List[Any]						=			pipe([prompt]							,    generator=_lowercase							,    num_inference_steps=2							,    output_type="""np"""   )
								snake_case_   : Optional[int]						=			output.images

								snake_case_   : List[Any]						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Dict						=			pipe(
								    [prompt]							,    generator=_lowercase							,    output_type="""np"""							,    return_dict=_lowercase							,    num_inference_steps=2   )[0]

								snake_case_   : List[Any]						=			image[0, -3:, -3:, -1]
								snake_case_   : Any						=			image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 2_4, 2_4, 3)

								snake_case_   : Dict						=			np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : int						=			"""cpu"""

								snake_case_   : List[Any]						=			self.dummy_vqvae
								snake_case_   : Optional[int]						=			self.dummy_text_encoder
								snake_case_   : List[Any]						=			self.dummy_tokenizer
								snake_case_   : Union[str, Any]						=			self.dummy_transformer
								snake_case_   : str						=			VQDiffusionScheduler(self.num_embed   )
								snake_case_   : List[Any]						=			LearnedClassifierFreeSamplingEmbeddings(
								    learnable=_lowercase							,    hidden_size=self.text_embedder_hidden_size							,    length=tokenizer.model_max_length   )

								snake_case_   : Union[str, Any]						=			VQDiffusionPipeline(
								    vqvae=_lowercase							,    text_encoder=_lowercase							,    tokenizer=_lowercase							,    transformer=_lowercase							,    scheduler=_lowercase							,    learned_classifier_free_sampling_embeddings=_lowercase							,    )
								snake_case_   : Any						=			pipe.to(_lowercase   )
								pipe.set_progress_bar_config(disable=_lowercase   )

								snake_case_   : Tuple						=			"""teddy bear playing in the pool"""

								snake_case_   : str						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Tuple						=			pipe([prompt]							,    generator=_lowercase							,    num_inference_steps=2							,    output_type="""np"""   )
								snake_case_   : Dict						=			output.images

								snake_case_   : Union[str, Any]						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Any						=			pipe(
								    [prompt]							,    generator=_lowercase							,    output_type="""np"""							,    return_dict=_lowercase							,    num_inference_steps=2   )[0]

								snake_case_   : Optional[Any]						=			image[0, -3:, -3:, -1]
								snake_case_   : int						=			image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 2_4, 2_4, 3)

								snake_case_   : int						=			np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2.0
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2




@slow
@require_torch_gpu
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : List[Any]						=			load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""   )

								snake_case_   : str						=			VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""   )
								snake_case_   : Optional[Any]						=			pipeline.to(_lowercase   )
								pipeline.set_progress_bar_config(disable=_lowercase   )

								# requires GPU generator for gumbel softmax
								# don't use GPU generator in tests though
								snake_case_   : Any						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Optional[int]						=			pipeline(
								    """teddy bear playing in the pool"""							,    num_images_per_prompt=1							,    generator=_lowercase							,    output_type="""np"""							,    )

								snake_case_   : Union[str, Any]						=			output.images[0]

								assert image.shape == (2_5_6, 2_5_6, 3)
								assert np.abs(expected_image - image   ).max() < 2.0

