





'''simple docstring'''







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
snake_case_  :							str            =	"src/diffusers"
snake_case_  :							Any            =	"docs/source/en"
snake_case_  :							Optional[Any]            =	"."





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

						UpperCAmelCase_      :   Optional[int]      =	start_index
						while not lines[end_index].startswith(SCREAMING_SNAKE_CASE__ ):
												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 |
snake_case_  :							Optional[int]            =	"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
snake_case_  :							Optional[Any]            =	re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
snake_case_  :							str            =	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.
snake_case_  :							List[Any]            =	re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						Optional[Any]:
						UpperCAmelCase_      :   List[str]      =	re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''',      SCREAMING_SNAKE_CASE__ )
						return [m.group(0 ) for m in matches]





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      Dict )			->						Tuple:
						UpperCAmelCase_      :   Union[str, Any]      =	2 if text == '''✅''' or text == '''❌''' else len(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Optional[int]      =	(width - text_length) // 2
						UpperCAmelCase_      :   List[Any]      =	width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent





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

						# Let's lookup through all diffusers object (once).
						for attr_name in dir(SCREAMING_SNAKE_CASE__ ):
												UpperCAmelCase_      :   Union[str, Any]      =	None
												if attr_name.endswith('''Tokenizer''' ):
																		UpperCAmelCase_      :   Any      =	slow_tokenizers
																		UpperCAmelCase_      :   int      =	attr_name[:-9]
												elif attr_name.endswith('''TokenizerFast''' ):
																		UpperCAmelCase_      :   Any      =	fast_tokenizers
																		UpperCAmelCase_      :   Tuple      =	attr_name[:-13]
												elif _re_tf_models.match(SCREAMING_SNAKE_CASE__ ) is not None:
																		UpperCAmelCase_      :   int      =	tf_models
																		UpperCAmelCase_      :   Optional[Any]      =	_re_tf_models.match(SCREAMING_SNAKE_CASE__ ).groups()[0]
												elif _re_flax_models.match(SCREAMING_SNAKE_CASE__ ) is not None:
																		UpperCAmelCase_      :   Any      =	flax_models
																		UpperCAmelCase_      :   Union[str, Any]      =	_re_flax_models.match(SCREAMING_SNAKE_CASE__ ).groups()[0]
												elif _re_pt_models.match(SCREAMING_SNAKE_CASE__ ) is not None:
																		UpperCAmelCase_      :   str      =	pt_models
																		UpperCAmelCase_      :   Any      =	_re_pt_models.match(SCREAMING_SNAKE_CASE__ ).groups()[0]

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

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

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

						UpperCAmelCase_      :   List[str]      =	{True: '''✅''', False: '''❌'''}
						for name in model_names:
												UpperCAmelCase_      :   Tuple      =	model_name_to_prefix[name]
												UpperCAmelCase_      :   List[Any]      =	[
												    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(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ) for l, w in zip(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )] ) + "|\n"
						return table





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any]=False )			->						str:
						UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   Any      =	_find_text_in_file(
						    filename=os.path.join(SCREAMING_SNAKE_CASE__,      '''index.mdx''' ),      start_prompt='''<!--This table is updated automatically from the auto modules''',      end_prompt='''<!-- End table-->''',      )
						UpperCAmelCase_      :   Tuple      =	get_model_table_from_auto_modules()

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

					check_model_table(args.fix_and_overwrite)










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase:					int      =						logging.get_logger(__name__)

_lowercase:					Dict      =						{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A		      =				"mobilebert"




	def __init__(self						,			lowerCamelCase_=30522						,			lowerCamelCase_=512						,			lowerCamelCase_=24						,			lowerCamelCase_=4						,			lowerCamelCase_=512						,			lowerCamelCase_="relu"						,			lowerCamelCase_=0.0						,			lowerCamelCase_=0.1						,			lowerCamelCase_=512						,			lowerCamelCase_=2						,			lowerCamelCase_=0.02						,			lowerCamelCase_=1E-1_2						,			lowerCamelCase_=0						,			lowerCamelCase_=128						,			lowerCamelCase_=True						,			lowerCamelCase_=True						,			lowerCamelCase_=128						,			lowerCamelCase_=False						,			lowerCamelCase_=True						,			lowerCamelCase_=4						,			lowerCamelCase_="no_norm"						,			lowerCamelCase_=True						,			lowerCamelCase_=None						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						super().__init__(pad_token_id=lowerCamelCase_						,			**lowerCamelCase_						)

						a   	=  vocab_size
						a   	=  hidden_size
						a   	=  num_hidden_layers
						a   	=  num_attention_heads
						a   	=  hidden_act
						a   	=  intermediate_size
						a   	=  hidden_dropout_prob
						a   	=  attention_probs_dropout_prob
						a   	=  max_position_embeddings
						a   	=  type_vocab_size
						a   	=  initializer_range
						a   	=  layer_norm_eps
						a   	=  embedding_size
						a   	=  trigram_input
						a   	=  use_bottleneck
						a   	=  intra_bottleneck_size
						a   	=  use_bottleneck_attention
						a   	=  key_query_shared_bottleneck
						a   	=  num_feedforward_networks
						a   	=  normalization_type
						a   	=  classifier_activation

						if self.use_bottleneck:
											a   	=  intra_bottleneck_size
						else:
											a   	=  hidden_size

						a   	=  classifier_dropout




class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						if self.task == "multiple-choice":
											a   	=  {0: "batch", 1: "choice", 2: "sequence"}
						else:
											a   	=  {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						        ("token_type_ids", dynamic_axis),
						    ]						)
