


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
_UpperCAmelCase     :					List[Any]       ="""src/diffusers"""
_UpperCAmelCase     :					Dict       ="""docs/source/en"""
_UpperCAmelCase     :					List[str]       ="""."""



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

				lowerCAmelCase_	: List[Any]      			=			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 |
_UpperCAmelCase     :					int       ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase     :					Any       =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase     :					Optional[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.
_UpperCAmelCase     :					Union[str, Any]       =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
_UpperCAmelCase     :					Dict       =importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
_UpperCAmelCase     :					Tuple       =spec.loader.load_module()



def      lowerCAmelCase	(						lowerCAmelCase_	)->	Dict:
				lowerCAmelCase_	: Any      			=			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''	,				lowerCAmelCase_	)
				return [m.group(0	) for m in matches]



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	Any:
				lowerCAmelCase_	: Any      			=			2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_	)
				lowerCAmelCase_	: List[str]      			=			(width - text_length) // 2
				lowerCAmelCase_	: str      			=			width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def      lowerCAmelCase	(						)->	Dict:
				lowerCAmelCase_	: Optional[int]      			=			diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase_	: Tuple      			=			{
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				lowerCAmelCase_	: str      			=			{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.
				lowerCAmelCase_	: Union[str, Any]      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Dict      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Tuple      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Optional[int]      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Any      			=			collections.defaultdict(lowerCAmelCase_	)

				# Let's lookup through all diffusers object (once).
				for attr_name in dir(lowerCAmelCase_	):
								lowerCAmelCase_	: Tuple      			=			None
								if attr_name.endswith('''Tokenizer'''	):
												lowerCAmelCase_	: int      			=			slow_tokenizers
												lowerCAmelCase_	: Optional[Any]      			=			attr_name[:-9]
								elif attr_name.endswith('''TokenizerFast'''	):
												lowerCAmelCase_	: List[Any]      			=			fast_tokenizers
												lowerCAmelCase_	: Dict      			=			attr_name[:-13]
								elif _re_tf_models.match(lowerCAmelCase_	) is not None:
												lowerCAmelCase_	: Union[str, Any]      			=			tf_models
												lowerCAmelCase_	: Optional[int]      			=			_re_tf_models.match(lowerCAmelCase_	).groups()[0]
								elif _re_flax_models.match(lowerCAmelCase_	) is not None:
												lowerCAmelCase_	: Optional[Any]      			=			flax_models
												lowerCAmelCase_	: Optional[int]      			=			_re_flax_models.match(lowerCAmelCase_	).groups()[0]
								elif _re_pt_models.match(lowerCAmelCase_	) is not None:
												lowerCAmelCase_	: Tuple      			=			pt_models
												lowerCAmelCase_	: Tuple      			=			_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():
																				lowerCAmelCase_	: Dict      			=			True
																				break
																# Try again after removing the last word in the name
																lowerCAmelCase_	: Tuple      			=			''''''.join(camel_case_split(lowerCAmelCase_	)[:-1]	)

    # Let's build that table!
				lowerCAmelCase_	: Union[str, Any]      			=			list(model_name_to_config.keys()	)
				model_names.sort(key=str.lower	)
				lowerCAmelCase_	: Tuple      			=			['''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_	: Optional[Any]      			=			[len(lowerCAmelCase_	) + 2 for c in columns]
				lowerCAmelCase_	: List[Any]      			=			max([len(lowerCAmelCase_	) for name in model_names]	) + 2

				# Build the table per se
				lowerCAmelCase_	: List[str]      			=			'''|''' + '''|'''.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"

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



def      lowerCAmelCase	(						lowerCAmelCase_=False	)->	str:
				lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_	: Optional[int]      			=			_find_text_in_file(
				    filename=os.path.join(lowerCAmelCase_	,				'''index.mdx'''	)	,				start_prompt='''<!--This table is updated automatically from the auto modules'''	,				end_prompt='''<!-- End table-->'''	,				)
				lowerCAmelCase_	: int      			=			get_model_table_from_auto_modules()

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

						check_model_table(args.fix_and_overwrite)



import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
						_UpperCAmelCase     :					Optional[int]       =argparse.ArgumentParser()

						# Hyperparameters sent by the client are passed as command-line arguments to the script.
						parser.add_argument("""--epochs""", type=int, default=1)
						parser.add_argument("""--per_device_train_batch_size""", type=int, default=16)
						parser.add_argument("""--per_device_eval_batch_size""", type=int, default=8)
						parser.add_argument("""--model_name_or_path""", type=str)
						parser.add_argument("""--learning_rate""", type=str, default=5E-5)
						parser.add_argument("""--do_train""", type=bool, default=True)
						parser.add_argument("""--do_eval""", type=bool, default=True)
						parser.add_argument("""--output_dir""", type=str)

						_UpperCAmelCase       ,   _UpperCAmelCase     :					List[str]       =parser.parse_known_args()

						# overwrite batch size until we have tf_glue.py
						_UpperCAmelCase     :					Optional[Any]       =16
						_UpperCAmelCase     :					List[str]       =16

						# Set up logging
						_UpperCAmelCase     :					Optional[Any]       =logging.getLogger(__name__)

						logging.basicConfig(
						    level=logging.getLevelName("""INFO"""),
						    handlers=[logging.StreamHandler(sys.stdout)],
						    format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
						)

						# Load model and tokenizer
						_UpperCAmelCase     :					List[str]       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						_UpperCAmelCase     :					Any       =AutoTokenizer.from_pretrained(args.model_name_or_path)

						# Load dataset
						_UpperCAmelCase       ,   _UpperCAmelCase     :					Union[str, Any]       =load_dataset("""imdb""", split=["""train""", """test"""])
						_UpperCAmelCase     :					int       =train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
						_UpperCAmelCase     :					str       =test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

						# Preprocess train dataset
						_UpperCAmelCase     :					str       =train_dataset.map(
						    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
						)
						train_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

						_UpperCAmelCase     :					List[Any]       ={
						    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
						    for x in ["""input_ids""", """attention_mask"""]
						}
						_UpperCAmelCase     :					Optional[Any]       =tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])).batch(
						    args.per_device_train_batch_size
						)

						# Preprocess test dataset
						_UpperCAmelCase     :					List[Any]       =test_dataset.map(
						    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
						)
						test_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

						_UpperCAmelCase     :					Dict       ={
						    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
						    for x in ["""input_ids""", """attention_mask"""]
						}
						_UpperCAmelCase     :					Any       =tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])).batch(
						    args.per_device_eval_batch_size
						)

						# fine optimizer and loss
						_UpperCAmelCase     :					List[Any]       =tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
						_UpperCAmelCase     :					str       =tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
						_UpperCAmelCase     :					Optional[Any]       =[tf.keras.metrics.SparseCategoricalAccuracy()]
						model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

						_UpperCAmelCase     :					Tuple       =time.time()
						_UpperCAmelCase     :					List[str]       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						_UpperCAmelCase     :					Optional[int]       =time.time() - start_train_time

						logger.info("""*** Train ***""")
						logger.info(f"""train_runtime = {end_train_time}""")
						for key, value in train_results.history.items():
												logger.info(f"""  {key} = {value}""")