"""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
__lowerCamelCase									=	"src/diffusers"
__lowerCamelCase									=	"docs/source/en"
__lowerCamelCase									=	"."






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





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

							A__						  =				start_index
							while not lines[end_index].startswith(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 |
__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 diffusers module imported is the one in the repo.
__lowerCamelCase									=	importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(TRANSFORMERS_PATH, "__init__.py"),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__lowerCamelCase									=	spec.loader.load_module()






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





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






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				2 if text == "✅" or text == "❌" else len(snake_case_					)
							A__						  =				(width - text_length) // 2
							A__						  =				width - text_length - left_indent
							return " " * left_indent + text + " " * right_indent






def 	UpperCAmelCase       (						):






							"""simple docstring"""





							A__						  =				diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							A__						  =				{
							    name: config_mapping_names[code]
							    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
							    if code in config_mapping_names
							}
							A__						  =				{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.
							A__						  =				collections.defaultdict(snake_case_					)
							A__						  =				collections.defaultdict(snake_case_					)
							A__						  =				collections.defaultdict(snake_case_					)
							A__						  =				collections.defaultdict(snake_case_					)
							A__						  =				collections.defaultdict(snake_case_					)

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

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

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

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

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






def 	UpperCAmelCase       (						UpperCamelCase__=False					):






							"""simple docstring"""





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

							if current_table != new_table:
														if overwrite:
																					with open(os.path.join(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__":
				__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"""





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase					=     logging.get_logger(__name__)

UpperCamelCase					=     {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   UpperCamelCase__     	=							"""squeezebert"""





   def __init__(       self					, UpperCAmelCase_=30_522					, UpperCAmelCase_=768					, UpperCAmelCase_=12					, UpperCAmelCase_=12					, UpperCAmelCase_=3_072					, UpperCAmelCase_="gelu"					, UpperCAmelCase_=0.1					, UpperCAmelCase_=0.1					, UpperCAmelCase_=512					, UpperCAmelCase_=2					, UpperCAmelCase_=0.02					, UpperCAmelCase_=1E-1_2					, UpperCAmelCase_=0					, UpperCAmelCase_=768					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, UpperCAmelCase_=1					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, **UpperCAmelCase_					, )			->   int:




          '''simple docstring'''




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

          lowercase__:							Union[str, Any]         =     vocab_size
          lowercase__:							int         =     hidden_size
          lowercase__:							int         =     num_hidden_layers
          lowercase__:							List[str]         =     num_attention_heads
          lowercase__:							Any         =     hidden_act
          lowercase__:							List[str]         =     intermediate_size
          lowercase__:							Any         =     hidden_dropout_prob
          lowercase__:							List[Any]         =     attention_probs_dropout_prob
          lowercase__:							Optional[int]         =     max_position_embeddings
          lowercase__:							List[Any]         =     type_vocab_size
          lowercase__:							Union[str, Any]         =     initializer_range
          lowercase__:							str         =     layer_norm_eps
          lowercase__:							Optional[Any]         =     embedding_size
          lowercase__:							Optional[int]         =     q_groups
          lowercase__:							List[str]         =     k_groups
          lowercase__:							Union[str, Any]         =     v_groups
          lowercase__:							str         =     post_attention_groups
          lowercase__:							Optional[int]         =     intermediate_groups
          lowercase__:							List[Any]         =     output_groups







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   @property
   def __lowercase							(       self)			->   Mapping[str, Mapping[int, str]]:




          '''simple docstring'''




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






