

'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCamelCase :      List[str]					 =     logging.get_logger(__name__)

lowerCamelCase :      Any					 =     {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}







class 							A__    (       UpperCAmelCase__  ):
  A__		 =				'bart'
  A__		 =				['past_key_values']
  A__		 =				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





  def __init__(		self      :	int							,					_a      :	Any=5_0265							,					_a      :	List[str]=1024							,					_a      :	List[Any]=12							,					_a      :	str=4096							,					_a      :	int=16							,					_a      :	Optional[Any]=12							,					_a      :	List[str]=4096							,					_a      :	Optional[Any]=16							,					_a      :	Optional[int]=0.0							,					_a      :	Union[str, Any]=0.0							,					_a      :	Optional[Any]="gelu"							,					_a      :	int=1024							,					_a      :	Any=0.1							,					_a      :	Dict=0.0							,					_a      :	Any=0.0							,					_a      :	Union[str, Any]=0.02							,					_a      :	Tuple=0.0							,					_a      :	Tuple=False							,					_a      :	str=True							,					_a      :	Optional[int]=3							,					_a      :	Optional[int]=1							,					_a      :	Dict=0							,					_a      :	Tuple=2							,					_a      :	Union[str, Any]=True							,					_a      :	Dict=2							,					_a      :	Dict=2							,					**_a      :	int							,					)	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =vocab_size
       _SCREAMING_SNAKE_CASE							    =max_position_embeddings
       _SCREAMING_SNAKE_CASE							    =d_model
       _SCREAMING_SNAKE_CASE							    =encoder_ffn_dim
       _SCREAMING_SNAKE_CASE							    =encoder_layers
       _SCREAMING_SNAKE_CASE							    =encoder_attention_heads
       _SCREAMING_SNAKE_CASE							    =decoder_ffn_dim
       _SCREAMING_SNAKE_CASE							    =decoder_layers
       _SCREAMING_SNAKE_CASE							    =decoder_attention_heads
       _SCREAMING_SNAKE_CASE							    =dropout
       _SCREAMING_SNAKE_CASE							    =attention_dropout
       _SCREAMING_SNAKE_CASE							    =activation_dropout
       _SCREAMING_SNAKE_CASE							    =activation_function
       _SCREAMING_SNAKE_CASE							    =init_std
       _SCREAMING_SNAKE_CASE							    =encoder_layerdrop
       _SCREAMING_SNAKE_CASE							    =decoder_layerdrop
       _SCREAMING_SNAKE_CASE							    =classifier_dropout
       _SCREAMING_SNAKE_CASE							    =use_cache
       _SCREAMING_SNAKE_CASE							    =encoder_layers
       _SCREAMING_SNAKE_CASE							    =scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           num_labels=lowercase_							,					pad_token_id=lowercase_							,					bos_token_id=lowercase_							,					eos_token_id=lowercase_							,					is_encoder_decoder=lowercase_							,					decoder_start_token_id=lowercase_							,					forced_eos_token_id=lowercase_							,					**lowercase_							,					)

       # ensure backward compatibility for BART CNN models
       if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'							,					lowercase_ ):
            _SCREAMING_SNAKE_CASE							    =self.bos_token_id
            warnings.warn(
                f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                'The config can simply be saved and uploaded again to be fixed.' )







class 							A__    (       UpperCAmelCase__  ):





  @property
  def 						A  (		self      :	List[Any] )	->    List[str]:





       '''simple docstring'''






       if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE							    =OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ] )

            if self.use_past:
                 _SCREAMING_SNAKE_CASE							    ={0: '''batch'''}
                 _SCREAMING_SNAKE_CASE							    ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
            else:
                 _SCREAMING_SNAKE_CASE							    ={0: '''batch''', 1: '''decoder_sequence'''}
                 _SCREAMING_SNAKE_CASE							    ={0: '''batch''', 1: '''decoder_sequence'''}

            if self.use_past:
                 self.fill_with_past_key_values_(lowercase_							,					direction='inputs' )
       elif self.task == "causal-lm":
            # TODO: figure this case out.
            _SCREAMING_SNAKE_CASE							    =OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ] )
            if self.use_past:
                 _SCREAMING_SNAKE_CASE							    =self.num_layers
                 for i in range(lowercase_ ):
                      _SCREAMING_SNAKE_CASE							    ={0: '''batch''', 2: '''past_sequence + sequence'''}
                      _SCREAMING_SNAKE_CASE							    ={0: '''batch''', 2: '''past_sequence + sequence'''}
       else:
            _SCREAMING_SNAKE_CASE							    =OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                    ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                    ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                ] )

       return common_inputs





  @property
  def 						A  (		self      :	Dict )	->    List[Any]:





       '''simple docstring'''






       if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE							    =super().outputs
       else:
            _SCREAMING_SNAKE_CASE							    =super(lowercase_							,					self ).outputs
            if self.use_past:
                 _SCREAMING_SNAKE_CASE							    =self.num_layers
                 for i in range(lowercase_ ):
                      _SCREAMING_SNAKE_CASE							    ={0: '''batch''', 2: '''past_sequence + sequence'''}
                      _SCREAMING_SNAKE_CASE							    ={0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs





  def 						A  (		self      :	Any							,					_a      :	PreTrainedTokenizer							,					_a      :	int = -1							,					_a      :	int = -1							,					_a      :	bool = False							,					_a      :	Optional[TensorType] = None							,					)	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_							,					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_ )

       # Generate decoder inputs
       _SCREAMING_SNAKE_CASE							    =seq_length if not self.use_past else 1
       _SCREAMING_SNAKE_CASE							    =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_							,					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_ )
       _SCREAMING_SNAKE_CASE							    ={f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
       _SCREAMING_SNAKE_CASE							    =dict(**lowercase_							,					**lowercase_ )

       if self.use_past:
            if not is_torch_available():
                 raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
            else:
                 import torch
            _SCREAMING_SNAKE_CASE							    =common_inputs['''input_ids'''].shape
            _SCREAMING_SNAKE_CASE							    =common_inputs['''decoder_input_ids'''].shape[1]
            _SCREAMING_SNAKE_CASE							    =self.num_attention_heads
            _SCREAMING_SNAKE_CASE							    =(
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            _SCREAMING_SNAKE_CASE							    =decoder_seq_length + 3
            _SCREAMING_SNAKE_CASE							    =(
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            _SCREAMING_SNAKE_CASE							    =torch.cat(
                [common_inputs['decoder_attention_mask'], torch.ones(lowercase_							,					lowercase_ )]							,					dim=1 )

            _SCREAMING_SNAKE_CASE							    =[]
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            _SCREAMING_SNAKE_CASE							    =self.num_layers
            _SCREAMING_SNAKE_CASE							    =min(lowercase_							,					lowercase_ )
            _SCREAMING_SNAKE_CASE							    =max(lowercase_							,					lowercase_ ) - min_num_layers
            _SCREAMING_SNAKE_CASE							    ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

            for _ in range(lowercase_ ):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(lowercase_ ),
                         torch.zeros(lowercase_ ),
                         torch.zeros(lowercase_ ),
                         torch.zeros(lowercase_ ),
                     ) )
            # TODO: test this.
            _SCREAMING_SNAKE_CASE							    =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
            for _ in range(lowercase_							,					lowercase_ ):
                 common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
       return common_inputs





  def 						A  (		self      :	Optional[Any]							,					_a      :	PreTrainedTokenizer							,					_a      :	int = -1							,					_a      :	int = -1							,					_a      :	bool = False							,					_a      :	Optional[TensorType] = None							,					)	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_							,					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_ )

       if self.use_past:
            if not is_torch_available():
                 raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
            else:
                 import torch
            _SCREAMING_SNAKE_CASE							    =common_inputs['''input_ids'''].shape
            # Not using the same length for past_key_values
            _SCREAMING_SNAKE_CASE							    =seqlen + 2
            _SCREAMING_SNAKE_CASE							    =self.num_layers
            _SCREAMING_SNAKE_CASE							    =self.num_attention_heads
            _SCREAMING_SNAKE_CASE							    =(
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            _SCREAMING_SNAKE_CASE							    =common_inputs['''attention_mask'''].dtype
            _SCREAMING_SNAKE_CASE							    =torch.cat(
                [common_inputs['attention_mask'], torch.ones(lowercase_							,					lowercase_							,					dtype=lowercase_ )]							,					dim=1 )
            _SCREAMING_SNAKE_CASE							    =[
                (torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
            ]
       return common_inputs





  def 						A  (		self      :	int							,					_a      :	PreTrainedTokenizer							,					_a      :	int = -1							,					_a      :	int = -1							,					_a      :	bool = False							,					_a      :	Optional[TensorType] = None							,					)	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =compute_effective_axis_dimension(
           lowercase_							,					fixed_dimension=OnnxConfig.default_fixed_batch							,					num_token_to_add=0 )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       _SCREAMING_SNAKE_CASE							    =tokenizer.num_special_tokens_to_add(lowercase_ )
       _SCREAMING_SNAKE_CASE							    =compute_effective_axis_dimension(
           lowercase_							,					fixed_dimension=OnnxConfig.default_fixed_sequence							,					num_token_to_add=lowercase_ )

       # Generate dummy inputs according to compute batch and sequence
       _SCREAMING_SNAKE_CASE							    =[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
       _SCREAMING_SNAKE_CASE							    =dict(tokenizer(lowercase_							,					return_tensors=lowercase_ ) )
       return common_inputs





  def 						A  (		self      :	List[str]							,					_a      :	PreTrainedTokenizer							,					_a      :	int = -1							,					_a      :	int = -1							,					_a      :	bool = False							,					_a      :	Optional[TensorType] = None							,					)	->    List[Any]:





       '''simple docstring'''






       if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE							    =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                lowercase_							,					batch_size=lowercase_							,					seq_length=lowercase_							,					is_pair=lowercase_							,					framework=lowercase_ )

       elif self.task == "causal-lm":
            _SCREAMING_SNAKE_CASE							    =self._generate_dummy_inputs_for_causal_lm(
                lowercase_							,					batch_size=lowercase_							,					seq_length=lowercase_							,					is_pair=lowercase_							,					framework=lowercase_ )
       else:
            _SCREAMING_SNAKE_CASE							    =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                lowercase_							,					batch_size=lowercase_							,					seq_length=lowercase_							,					is_pair=lowercase_							,					framework=lowercase_ )

       return common_inputs





  def 						A  (		self      :	Dict							,					_a      :	str							,					_a      :	int							,					_a      :	Tuple							,					_a      :	str )	->    Optional[int]:





       '''simple docstring'''






       if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE							    =super()._flatten_past_key_values_(lowercase_							,					lowercase_							,					lowercase_							,					lowercase_ )
       else:
            _SCREAMING_SNAKE_CASE							    =super(lowercase_							,					self )._flatten_past_key_values_(
                lowercase_							,					lowercase_							,					lowercase_							,					lowercase_ )








"""simple docstring"""






import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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_   : Optional[int]   			= """src/transformers"""
UpperCAmelCase_   : Tuple   			= """docs/source/en"""
UpperCAmelCase_   : Optional[Any]   			= """."""



def 					_A			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






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

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


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase_   : Optional[int]   			= direct_transformers_import(TRANSFORMERS_PATH)



def 					_A			(__a							) ->       int:
    """simple docstring"""






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



def 					_A			(__a    ,				__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		2 if text == '''✅''' or text == '''❌''' else len(__a							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		(width - text_length) // 2
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent



def 					_A			() ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		{
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{name: config.replace('''Config'''    ,				''''''							) 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.
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		collections.defaultdict(__a							)

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

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

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

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

    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{True: '''✅''', False: '''❌'''}
    for name in model_names:
        SCREAMING_SNAKE_CASE_      :  str    						=		model_name_to_prefix[name]
        SCREAMING_SNAKE_CASE_      :  int    						=		[
            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(__a    ,				__a							) for l, w in zip(__a    ,				__a							)]							) + "|\n"
    return table



def 					_A			(__a=False							) ->       List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		_find_text_in_file(
        filename=os.path.join(__a    ,				'''index.md'''							)    ,				start_prompt='''<!--This table is updated automatically from the auto modules'''    ,				end_prompt='''<!-- End table-->'''    ,				)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(__a    ,				'''index.md'''							)    ,				'''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.md` has not been updated. Run `make fix-copies` to fix this.'''							)


if __name__ == "__main__":
  UpperCAmelCase_   : Union[str, Any]   			= argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  UpperCAmelCase_   : Any   			= parser.parse_args()

  check_model_table(args.fix_and_overwrite)


