


"""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												=							'''src/diffusers'''
__snake_case												=							'''docs/source/en'''
__snake_case												=							'''.'''
def    A_ ( _lowerCAmelCase  :       Any,  _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Dict							):






     """simple docstring"""

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

     _a     						=					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 |
__snake_case												=							'''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
__snake_case												=							re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__snake_case												=							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												=							re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the diffusers module imported is the one in the repo.
__snake_case												=							importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(TRANSFORMERS_PATH, '''__init__.py'''),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__snake_case												=							spec.loader.load_module()
def    A_ ( _lowerCAmelCase  :       List[str]							):






     """simple docstring"""

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






     """simple docstring"""

     _a     						=					2 if text == '''✅''' or text == '''❌''' else len(_lowerCAmelCase							)
     _a     						=					(width - text_length) // 2
     _a     						=					width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent
def    A_ ( ):






     """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(_lowerCAmelCase							)
     _a     						=					collections.defaultdict(_lowerCAmelCase							)
     _a     						=					collections.defaultdict(_lowerCAmelCase							)
     _a     						=					collections.defaultdict(_lowerCAmelCase							)
     _a     						=					collections.defaultdict(_lowerCAmelCase							)

     # Let's lookup through all diffusers object (once).
     for attr_name in dir(_lowerCAmelCase							):
          _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(_lowerCAmelCase							) is not None:
               _a     						=					tf_models
               _a     						=					_re_tf_models.match(_lowerCAmelCase							).groups()[0]
          elif _re_flax_models.match(_lowerCAmelCase							) is not None:
               _a     						=					flax_models
               _a     						=					_re_flax_models.match(_lowerCAmelCase							).groups()[0]
          elif _re_pt_models.match(_lowerCAmelCase							) is not None:
               _a     						=					pt_models
               _a     						=					_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():
                         _a     						=					True
                         break
                    # Try again after removing the last word in the name
                    _a     						=					''''''.join(camel_case_split(_lowerCAmelCase							)[:-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(_lowerCAmelCase							) + 2 for c in columns]
     _a     						=					max([len(_lowerCAmelCase							) for name in model_names]							) + 2

     # Build the table per se
     _a     						=					'''|''' + '''|'''.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"

     _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(_lowerCAmelCase,  _lowerCAmelCase							) for l, w in zip(_lowerCAmelCase,  _lowerCAmelCase							)]							) + "|\n"
     return table
def    A_ ( _lowerCAmelCase  :       int=False							):






     """simple docstring"""

     _a						,   _a						,   _a						,   _a     						=					_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-->''',  )
     _a     						=					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__":
   __snake_case												=							argparse.ArgumentParser()
   parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
   __snake_case												=							parser.parse_args()

   check_model_table(args.fix_and_overwrite)



"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__snake_case												=							logging.get_logger(__name__)

__snake_case												=							{
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						List[Any]     =		'deberta'


     def __init__(   self     ,					__UpperCAmelCase=50265     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=0.02     ,					__UpperCAmelCase=1e-7     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=-1     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase="gelu"     ,					**__UpperCAmelCase     ,					)							->				Any:
          super().__init__(**__UpperCAmelCase				)

          _a     						=					hidden_size
          _a     						=					num_hidden_layers
          _a     						=					num_attention_heads
          _a     						=					intermediate_size
          _a     						=					hidden_act
          _a     						=					hidden_dropout_prob
          _a     						=					attention_probs_dropout_prob
          _a     						=					max_position_embeddings
          _a     						=					type_vocab_size
          _a     						=					initializer_range
          _a     						=					relative_attention
          _a     						=					max_relative_positions
          _a     						=					pad_token_id
          _a     						=					position_biased_input

          # Backwards compatibility
          if type(__UpperCAmelCase				) == str:
               _a     						=					[x.strip() for x in pos_att_type.lower().split('''|'''				)]

          _a     						=					pos_att_type
          _a     						=					vocab_size
          _a     						=					layer_norm_eps

          _a     						=					kwargs.get('''pooler_hidden_size'''     ,					__UpperCAmelCase				)
          _a     						=					pooler_dropout
          _a     						=					pooler_hidden_act







class      __lowerCamelCase	(       a__					):

     '''simple docstring'''


     @property
     def 				_UpperCAmelCase   (   self				)							->				Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a     						=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
               _a     						=					{0: '''batch''', 1: '''sequence'''}
          if self._config.type_vocab_size > 0:
               return OrderedDict(
                   [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]				)
          else:
               return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]				)


     @property
     def 				_UpperCAmelCase   (   self				)							->				int:
          return 12





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = 3     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = None     ,					)							->				Mapping[str, Any]:
          _a     						=					super().generate_dummy_inputs(preprocessor=__UpperCAmelCase     ,					framework=__UpperCAmelCase				)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
               del dummy_inputs["token_type_ids"]
          return dummy_inputs