


from typing import Mapping

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


UpperCamelCase					:       Tuple			    =     logging.get_logger(__name__)

UpperCamelCase					:       List[Any]			    =     {
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		'umt5'
  _lowercase									=		['past_key_values']

  def __init__( self			:  Union[str, Any]				,				lowerCamelCase__			:  List[Any]=250_112				,				lowerCamelCase__			:  str=512				,				lowerCamelCase__			:  Optional[Any]=64				,				lowerCamelCase__			:  List[Any]=1_024				,				lowerCamelCase__			:  int=8				,				lowerCamelCase__			:  List[str]=None				,				lowerCamelCase__			:  Optional[Any]=6				,				lowerCamelCase__			:  int=32				,				lowerCamelCase__			:  Union[str, Any]=128				,				lowerCamelCase__			:  Tuple=0.1				,				lowerCamelCase__			:  Optional[Any]=1E-6				,				lowerCamelCase__			:  Union[str, Any]=1.0				,				lowerCamelCase__			:  Union[str, Any]="gated-gelu"				,				lowerCamelCase__			:  Any=True				,				lowerCamelCase__			:  List[str]=True				,				lowerCamelCase__			:  Any="T5Tokenizer"				,				lowerCamelCase__			:  Union[str, Any]=True				,				lowerCamelCase__			:  Optional[Any]=0				,				lowerCamelCase__			:  List[Any]=1				,				lowerCamelCase__			:  List[str]=0				,				**lowerCamelCase__			:  Any				,				):
        super().__init__(
            is_encoder_decoder=lowerCamelCase__				,				tokenizer_class=lowerCamelCase__				,				tie_word_embeddings=lowerCamelCase__				,				pad_token_id=lowerCamelCase__				,				eos_token_id=lowerCamelCase__				,				decoder_start_token_id=lowerCamelCase__				,				**lowerCamelCase__				,				)
        a__	:  Dict     =   vocab_size
        a__	:  List[Any]     =   d_model
        a__	:  Optional[Any]     =   d_kv
        a__	:  Optional[int]     =   d_ff
        a__	:  List[Any]     =   num_layers
        a__	:  int     =   (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        a__	:  str     =   num_heads
        a__	:  Optional[int]     =   relative_attention_num_buckets
        a__	:  Any     =   relative_attention_max_distance
        a__	:  int     =   dropout_rate
        a__	:  Union[str, Any]     =   layer_norm_epsilon
        a__	:  Optional[int]     =   initializer_factor
        a__	:  Optional[int]     =   feed_forward_proj
        a__	:  Tuple     =   use_cache

        a__	:  List[Any]     =   self.feed_forward_proj.split("-"       )
        a__	:  Optional[int]     =   act_info[-1]
        a__	:  Union[str, Any]     =   act_info[0] == "gated"

        if len(lowerCamelCase__       ) > 1 and act_info[0] != "gated" or len(lowerCamelCase__       ) > 2:
              raise ValueError(
                  f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                  "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                  "'gated-gelu' or 'relu'"       )

        if feed_forward_proj == "gated-gelu":
              a__	:  Optional[int]     =   "gelu_new"

  @property
  def    _UpperCamelCase( self			:  int       ):
        return self.d_model

  @property
  def    _UpperCamelCase( self			:  int       ):
        return self.num_heads

  @property
  def    _UpperCamelCase( self			:  Dict       ):
        return self.num_layers




class A__     ( A__  ):



  """simple docstring"""



  @property
  # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  Dict     =   {
            "input_ids": {0: "batch", 1: "encoder_sequence"},
            "attention_mask": {0: "batch", 1: "encoder_sequence"},
        }
        if self.use_past:
              a__	:  Tuple     =   "past_encoder_sequence + sequence"
              a__	:  Dict     =   {0: "batch"}
              a__	:  Union[str, Any]     =   {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
              a__	:  Tuple     =   {0: "batch", 1: "decoder_sequence"}
              a__	:  List[str]     =   {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
              self.fill_with_past_key_values_(lowerCamelCase__				,				direction="inputs"       )

        return common_inputs

  @property
  # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
  def    _UpperCamelCase( self			:  int       ):
        return 13

  @property
  def    _UpperCamelCase( self			:  List[str]       ):
        return 5E-4






import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE__   			=   '''docs/source/en/_toctree.yml'''


def A    (							__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     defaultdict(__UpperCamelCase       )
			for doc in model_doc:
						counts[doc["local"]] += 1
			A__	  =     [key for key, value in counts.items() if value > 1]

			A__	  =     []
			for duplicate_key in duplicates:
						A__	  =     list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}       )
						if len(__UpperCamelCase       ) > 1:
									raise ValueError(
									    f'''{duplicate_key} is present several times in the documentation table of content at '''
									    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
									    'others.'       )
						# Only add this once
						new_doc.append({'local': duplicate_key, 'title': titles[0]}       )

			# Add none duplicate-keys
			new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]       )

			# Sort
			return sorted(__UpperCamelCase			,						key=lambda __UpperCamelCase       : s["title"].lower()       )


def A    (							__UpperCamelCase=False       )    ->   str:
			with open(__UpperCamelCase			,						encoding='utf-8'       ) as f:
						A__	  =     yaml.safe_load(f.read()       )

			# Get to the API doc
			A__	  =     0
			while content[api_idx]["title"] != "API":
						api_idx += 1
			A__	  =     content[api_idx]['sections']

			# Then to the model doc
			A__	  =     0
			while api_doc[model_idx]["title"] != "Models":
						model_idx += 1

			A__	  =     api_doc[model_idx]['sections']

			A__	  =     [(idx, section) for idx, section in enumerate(__UpperCamelCase       ) if 'sections' in section]
			A__	  =     False
			for idx, modality_doc in modalities_docs:
						A__	  =     modality_doc['sections']
						A__	  =     clean_model_doc_toc(__UpperCamelCase       )

						if old_modality_doc != new_modality_doc:
									A__	  =     True
									if overwrite:
												A__	  =     new_modality_doc

			if diff:
						if overwrite:
									A__	  =     model_doc
									A__	  =     api_doc
									with open(__UpperCamelCase			,						'w'			,						encoding='utf-8'       ) as f:
												f.write(yaml.dump(__UpperCamelCase			,						allow_unicode=__UpperCamelCase       )       )
						else:
									raise ValueError(
									    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							check_model_doc(args.fix_and_overwrite)




