
"""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__				=					'src/transformers'
lowerCAmelCase__				=					'docs/source/en'
lowerCAmelCase__				=					'.'



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

		SCREAMING_SNAKE_CASE_    			=			start_index
		while not lines[end_index].startswith(__a     ):
				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__				=					'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase__				=					re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase__				=					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__				=					re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''',				__a     )
		return [m.group(0     ) for m in matches]



def 							_lowerCamelCase			(			__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			2 if text == '''✅''' or text == '''❌''' else len(__a     )
		SCREAMING_SNAKE_CASE_    			=			(width - text_length) // 2
		SCREAMING_SNAKE_CASE_    			=			width - text_length - left_indent
		return " " * left_indent + text + " " * right_indent



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

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

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

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

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

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



def 							_lowerCamelCase			(			__a=False     ):
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			_find_text_in_file(
		    filename=os.path.join(__a,				'''index.md'''     ),				start_prompt='''<!--This table is updated automatically from the auto modules''',				end_prompt='''<!-- End table-->''',				)
		SCREAMING_SNAKE_CASE_    			=			get_model_table_from_auto_modules()

		if current_table != new_table:
				if overwrite:
						with open(os.path.join(__a,				'''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__				=					argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						lowerCAmelCase__				=					parser.parse_args()

						check_model_table(args.fix_and_overwrite)

"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''num_train_timesteps''': 10_00,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







									for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for time_step in [0, 5_00, 9_99]:
											for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

													self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''learned_range'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											if i + 1 == timesteps.shape[0]:
													SCREAMING_SNAKE_CASE_    			=			None
											else:
													SCREAMING_SNAKE_CASE_    			=			timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(
											    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass