






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   :						int							=  """src/diffusers"""
__UpperCamelCase   :						List[Any]							=  """docs/source/en"""
__UpperCamelCase   :						Any							=  """."""
def        a_	( _A ,				_A ,				_A					)     ->     Optional[int]:







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

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


# This is to make sure the diffusers module imported is the one in the repo.
__UpperCamelCase   :						List[Any]							=  importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__UpperCamelCase   :						Any							=  spec.loader.load_module()
def        a_	( _A					)     ->     Optional[Any]:







				"""simple docstring"""
				snake_case__													=       re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)' ,				_A					)
				return [m.group(0					) for m in matches]
def        a_	( _A ,				_A					)     ->     str:







				"""simple docstring"""
				snake_case__													=       2 if text == '✅' or text == '❌' else len(_A					)
				snake_case__													=       (width - text_length) // 2
				snake_case__													=       width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent
def        a_	( )     ->     Any:







				"""simple docstring"""
				snake_case__													=       diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				snake_case__													=       {
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				snake_case__													=       {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.
				snake_case__													=       collections.defaultdict(_A					)
				snake_case__													=       collections.defaultdict(_A					)
				snake_case__													=       collections.defaultdict(_A					)
				snake_case__													=       collections.defaultdict(_A					)
				snake_case__													=       collections.defaultdict(_A					)

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

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

				# Build the table per se
				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"

				snake_case__													=       {True: '✅', False: '❌'}
				for name in model_names:
								snake_case__													=       model_name_to_prefix[name]
								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        a_	( _A=False					)     ->     Optional[Any]:







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

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(_A ,				'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   :						Tuple							=  argparse.ArgumentParser()
							parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
							__UpperCamelCase   :						str							=  parser.parse_args()

							check_model_table(args.fix_and_overwrite)












import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__UpperCamelCase   :						List[Any]							=  logging.getLogger(__name__)
def        a_	( )     ->     int:







				"""simple docstring"""
				snake_case__													=       argparse.ArgumentParser(description='PyTorch Transformer Language Model'					)
				parser.add_argument('--model_name' ,				type=_A ,				default='transfo-xl-wt103' ,				help='pretrained model name'					)
				parser.add_argument(
				    '--split' ,				type=_A ,				default='test' ,				choices=['all', 'valid', 'test'] ,				help='which split to evaluate'					)
				parser.add_argument('--batch_size' ,				type=_A ,				default=10 ,				help='batch size'					)
				parser.add_argument('--tgt_len' ,				type=_A ,				default=128 ,				help='number of tokens to predict'					)
				parser.add_argument('--ext_len' ,				type=_A ,				default=0 ,				help='length of the extended context'					)
				parser.add_argument('--mem_len' ,				type=_A ,				default=1600 ,				help='length of the retained previous heads'					)
				parser.add_argument('--clamp_len' ,				type=_A ,				default=1000 ,				help='max positional embedding index'					)
				parser.add_argument('--no_cuda' ,				action='store_true' ,				help='Do not use CUDA even though CUA is available'					)
				parser.add_argument('--work_dir' ,				type=_A ,				required=_A ,				help='path to the work_dir'					)
				parser.add_argument('--no_log' ,				action='store_true' ,				help='do not log the eval result'					)
				parser.add_argument('--same_length' ,				action='store_true' ,				help='set same length attention with masking'					)
				parser.add_argument('--server_ip' ,				type=_A ,				default='' ,				help='Can be used for distant debugging.'					)
				parser.add_argument('--server_port' ,				type=_A ,				default='' ,				help='Can be used for distant debugging.'					)
				snake_case__													=       parser.parse_args()
				assert args.ext_len >= 0, "extended context length must be non-negative"

				if args.server_ip and args.server_port:
								# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
								import ptvsd

								print('Waiting for debugger attach'					)
								ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,				redirect_output=_A					)
								ptvsd.wait_for_attach()

				snake_case__													=       torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'					)
				logger.info('device: {}'.format(_A					)					)

				# Load a pre-processed dataset
				# You can also build the corpus yourself using TransfoXLCorpus methods
				# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
				# and tokenizing the dataset
				# The pre-processed corpus is a convertion (using the conversion script )
				snake_case__													=       TransfoXLCorpus.from_pretrained(args.model_name					)

				snake_case__													=       corpus.get_iterator('valid' ,				args.batch_size ,				args.tgt_len ,				device=_A ,				ext_len=args.ext_len					)
				snake_case__													=       corpus.get_iterator('test' ,				args.batch_size ,				args.tgt_len ,				device=_A ,				ext_len=args.ext_len					)

				# Load a pre-trained model
				snake_case__													=       TransfoXLLMHeadModel.from_pretrained(args.model_name					)
				model.to(_A					)

				logger.info(
				    'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
				        args.batch_size ,				args.tgt_len ,				args.ext_len ,				args.mem_len ,				args.clamp_len					)					)

				model.reset_memory_length(args.mem_len					)
				if args.clamp_len > 0:
								snake_case__													=       args.clamp_len
				if args.same_length:
								snake_case__													=       True

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(_A					):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								snake_case__							,  snake_case__													=       0, 0.0
								snake_case__													=       time.time()
								with torch.no_grad():
												snake_case__													=       None
												for idx, (data, target, seq_len) in enumerate(_A					):
																snake_case__													=       model(_A ,				lm_labels=_A ,				mems=_A					)
																snake_case__							,  snake_case__							,  snake_case__													=       ret
																snake_case__													=       loss.mean()
																total_loss += seq_len * loss.item()
																total_len += seq_len
												snake_case__													=       time.time() - start_time
								logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(_A ,				1000 * total_time / (idx + 1)					)					)
								return total_loss / total_len

				# Run on test data.
				if args.split == "all":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       evaluate(_A					)
				elif args.split == "valid":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       None
				elif args.split == "test":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       None

				def format_log(_A ,				_A					):
								snake_case__													=       '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(_A ,				_A ,				math.exp(_A					)					)
								return log_str

				snake_case__													=       ''
				if valid_loss is not None:
								log_str += format_log(_A ,				'valid'					)
				if test_loss is not None:
								log_str += format_log(_A ,				'test'					)

				logger.info('=' * 100					)
				logger.info(_A					)
				logger.info('=' * 100					)


if __name__ == "__main__":
							main()




