






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






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





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

       lowercase__:							Union[str, Any]         =     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 |
UpperCamelCase					=     """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
UpperCamelCase					=     re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
UpperCamelCase					=     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					=     re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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






def 							A(    snake_case_						):
       """simple docstring"""





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






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							str         =     2 if text == "✅" or text == "❌" else len(snake_case_						)
       lowercase__:							Union[str, Any]         =     (width - text_length) // 2
       lowercase__:							int         =     width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent






def 							A(    ):
       """simple docstring"""





       lowercase__:							List[str]         =     diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       lowercase__:							Optional[Any]         =     {
           name: config_mapping_names[code]
           for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
           if code in config_mapping_names
       }
       lowercase__:							Optional[Any]         =     {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.
       lowercase__:							List[str]         =     collections.defaultdict(snake_case_						)
       lowercase__:							Union[str, Any]         =     collections.defaultdict(snake_case_						)
       lowercase__:							Optional[int]         =     collections.defaultdict(snake_case_						)
       lowercase__:							int         =     collections.defaultdict(snake_case_						)
       lowercase__:							int         =     collections.defaultdict(snake_case_						)

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

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

       # Build the table per se
       lowercase__:							List[Any]         =     "|" + "|".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"

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






def 							A(    snake_case_=False						):
       """simple docstring"""





       lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__:							Any         =     _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-->"		,  )
       lowercase__:							Any         =     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__":
  UpperCamelCase					=     argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  UpperCamelCase					=     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),
              ])






