





'''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
_SCREAMING_SNAKE_CASE					=    '''src/transformers'''
_SCREAMING_SNAKE_CASE					=    '''docs/source/en'''
_SCREAMING_SNAKE_CASE					=    '''.'''




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

			__lowercase										=							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 |
_SCREAMING_SNAKE_CASE					=    '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
_SCREAMING_SNAKE_CASE					=    re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
_SCREAMING_SNAKE_CASE					=    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.
_SCREAMING_SNAKE_CASE					=    re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''		,       lowerCamelCase_				)
			return [m.group(0				) for m in matches]




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  int				):
			__lowercase										=							2 if text == '''✅''' or text == '''❌''' else len(lowerCamelCase_				)
			__lowercase										=							(width - text_length) // 2
			__lowercase										=							width - text_length - left_indent
			return " " * left_indent + text + " " * right_indent




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

			# Let's lookup through all transformers object (once).
			for attr_name in dir(lowerCamelCase_				):
						__lowercase										=							None
						if attr_name.endswith('''Tokenizer'''				):
									__lowercase										=							slow_tokenizers
									__lowercase										=							attr_name[:-9]
						elif attr_name.endswith('''TokenizerFast'''				):
									__lowercase										=							fast_tokenizers
									__lowercase										=							attr_name[:-1_3]
						elif _re_tf_models.match(lowerCamelCase_				) is not None:
									__lowercase										=							tf_models
									__lowercase										=							_re_tf_models.match(lowerCamelCase_				).groups()[0]
						elif _re_flax_models.match(lowerCamelCase_				) is not None:
									__lowercase										=							flax_models
									__lowercase										=							_re_flax_models.match(lowerCamelCase_				).groups()[0]
						elif _re_pt_models.match(lowerCamelCase_				) is not None:
									__lowercase										=							pt_models
									__lowercase										=							_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():
															__lowercase										=							True
															break
												# Try again after removing the last word in the name
												__lowercase										=							''''''.join(camel_case_split(lowerCamelCase_				)[:-1]				)

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

			# Build the table per se
			__lowercase										=							'''|''' + '''|'''.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"

			__lowercase										=							{True: '''✅''', False: '''❌'''}
			for name in model_names:
						__lowercase										=							model_name_to_prefix[name]
						__lowercase										=							[
						    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    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]=False				):
			__lowercase						,		__lowercase						,		__lowercase						,		__lowercase										=							_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-->'''		,       )
			__lowercase										=							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__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()
	parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
	_SCREAMING_SNAKE_CASE					=    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


_SCREAMING_SNAKE_CASE					=    False






class 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

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







				'''simple docstring'''





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


	@property
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				return 12


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







				'''simple docstring'''





				return 12


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







				'''simple docstring'''





				return 32


	@property
	def 						_UpperCAmelCase     (self )							->							List[str]:







				'''simple docstring'''





				torch.manual_seed(0 )
				__lowercase										=							VQModel(
				    block_out_channels=[32, 64]     ,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 )							->							int:







				'''simple docstring'''





				__lowercase										=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
				return tokenizer


	@property
	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				torch.manual_seed(0 )
				__lowercase										=							CLIPTextConfig(
				    bos_token_id=0     ,eos_token_id=2     ,hidden_size=self.text_embedder_hidden_size     ,intermediate_size=37     ,layer_norm_eps=1E-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=1000     ,)
				return CLIPTextModel(_lowerCamelCase )


	@property
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				torch.manual_seed(0 )

				__lowercase										=							12
				__lowercase										=							12

				__lowercase										=							{
				    '''attention_bias''': True,
				    '''cross_attention_dim''': 32,
				    '''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''': 32,
				    '''sample_size''': width,
				    '''activation_fn''': '''geglu-approximate''',
				}

				__lowercase										=							TransformeraDModel(**_lowerCamelCase )
				return model


	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							'''cpu'''

				__lowercase										=							self.dummy_vqvae
				__lowercase										=							self.dummy_text_encoder
				__lowercase										=							self.dummy_tokenizer
				__lowercase										=							self.dummy_transformer
				__lowercase										=							VQDiffusionScheduler(self.num_embed )
				__lowercase										=							LearnedClassifierFreeSamplingEmbeddings(learnable=_lowerCamelCase )

				__lowercase										=							VQDiffusionPipeline(
				    vqvae=_lowerCamelCase     ,text_encoder=_lowerCamelCase     ,tokenizer=_lowerCamelCase     ,transformer=_lowerCamelCase     ,scheduler=_lowerCamelCase     ,learned_classifier_free_sampling_embeddings=_lowerCamelCase     ,)
				__lowercase										=							pipe.to(_lowerCamelCase )
				pipe.set_progress_bar_config(disable=_lowerCamelCase )

				__lowercase										=							'''teddy bear playing in the pool'''

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe([prompt]     ,generator=_lowerCamelCase     ,num_inference_steps=2     ,output_type='''np''' )
				__lowercase										=							output.images

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe(
				    [prompt]     ,generator=_lowerCamelCase     ,output_type='''np'''     ,return_dict=_lowerCamelCase     ,num_inference_steps=2 )[0]

				__lowercase										=							image[0, -3:, -3:, -1]
				__lowercase										=							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 24, 24, 3)

				__lowercase										=							np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2] )

				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 )							->							Any:







				'''simple docstring'''





				__lowercase										=							'''cpu'''

				__lowercase										=							self.dummy_vqvae
				__lowercase										=							self.dummy_text_encoder
				__lowercase										=							self.dummy_tokenizer
				__lowercase										=							self.dummy_transformer
				__lowercase										=							VQDiffusionScheduler(self.num_embed )
				__lowercase										=							LearnedClassifierFreeSamplingEmbeddings(
				    learnable=_lowerCamelCase     ,hidden_size=self.text_embedder_hidden_size     ,length=tokenizer.model_max_length )

				__lowercase										=							VQDiffusionPipeline(
				    vqvae=_lowerCamelCase     ,text_encoder=_lowerCamelCase     ,tokenizer=_lowerCamelCase     ,transformer=_lowerCamelCase     ,scheduler=_lowerCamelCase     ,learned_classifier_free_sampling_embeddings=_lowerCamelCase     ,)
				__lowercase										=							pipe.to(_lowerCamelCase )
				pipe.set_progress_bar_config(disable=_lowerCamelCase )

				__lowercase										=							'''teddy bear playing in the pool'''

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe([prompt]     ,generator=_lowerCamelCase     ,num_inference_steps=2     ,output_type='''np''' )
				__lowercase										=							output.images

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe(
				    [prompt]     ,generator=_lowerCamelCase     ,output_type='''np'''     ,return_dict=_lowerCamelCase     ,num_inference_steps=2 )[0]

				__lowercase										=							image[0, -3:, -3:, -1]
				__lowercase										=							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 24, 24, 3)

				__lowercase										=							np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8] )

				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 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy''' )

				__lowercase										=							VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq''' )
				__lowercase										=							pipeline.to(_lowerCamelCase )
				pipeline.set_progress_bar_config(disable=_lowerCamelCase )

				# requires GPU generator for gumbel softmax
				# don't use GPU generator in tests though
				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipeline(
				    '''teddy bear playing in the pool'''     ,num_images_per_prompt=1     ,generator=_lowerCamelCase     ,output_type='''np'''     ,)

				__lowercase										=							output.images[0]

				assert image.shape == (256, 256, 3)
				assert np.abs(expected_image - image ).max() < 2.0





