





"""simple docstring"""

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

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


__UpperCAmelCase        =logging.get_logger(__name__)

__UpperCAmelCase        ={
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}


class       lowerCAmelCase__				(   __A  ):
 lowercase__     :  Dict				=   """gpt_neo"""
 lowercase__     :  List[Any]				=   ["""past_key_values"""]
 lowercase__     :  Dict				=   {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}







 def __init__(   self		,	UpperCamelCase__=5_02_57		,	UpperCamelCase__=20_48		,	UpperCamelCase__=20_48		,	UpperCamelCase__=24		,	UpperCamelCase__=[[["global", "local"], 12]]		,	UpperCamelCase__=16		,	UpperCamelCase__=None		,	UpperCamelCase__=2_56		,	UpperCamelCase__="gelu_new"		,	UpperCamelCase__=0.0		,	UpperCamelCase__=0.0		,	UpperCamelCase__=0.0		,	UpperCamelCase__=0.1		,	UpperCamelCase__=1e-5		,	UpperCamelCase__=0.02		,	UpperCamelCase__=True		,	UpperCamelCase__=5_02_56		,	UpperCamelCase__=5_02_56		,	**UpperCamelCase__		,	):

    '''simple docstring'''


    A__											=					vocab_size
    A__											=					max_position_embeddings
    A__											=					hidden_size
    A__											=					num_layers
    A__											=					num_heads
    A__											=					intermediate_size
    A__											=					window_size
    A__											=					activation_function
    A__											=					resid_dropout
    A__											=					embed_dropout
    A__											=					attention_dropout
    A__											=					classifier_dropout
    A__											=					layer_norm_epsilon
    A__											=					initializer_range
    A__											=					use_cache

    A__											=					bos_token_id
    A__											=					eos_token_id

    A__											=					attention_types
    A__											=					self.expand_attention_types_params(UpperCamelCase__					)

    if len(self.attention_layers					) != self.num_layers:
       raise ValueError(
           "Configuration for convolutional module is incorrect. "
           "It is required that `len(config.attention_layers)` == `config.num_layers` "
           f"""but is `len(config.attention_layers) = {len(self.attention_layers					)}`, """
           f"""`config.num_layers = {self.num_layers}`. """
           "`config.attention_layers` is prepared using `config.attention_types`. "
           "Please verify the value of `config.attention_types` argument."					)

    super().__init__(bos_token_id=UpperCamelCase__		,	eos_token_id=UpperCamelCase__		,	**UpperCamelCase__					)







 @staticmethod
 def  lowercase_       (   UpperCamelCase__					):

    '''simple docstring'''


    A__											=					[]
    for item in attention_types:
       for _ in range(item[1]					):
          attentions.extend(item[0]					)
    return attentions





def        __a							(			A ,	A ,	A ,	A )       ->						List[str]:



   '''simple docstring'''



   import torch

   A__											=					input.size()
   A__											=					len(__A )
   A__											=					shape[dimension]

   A__											=					torch.arange(0 ,	__A ,	__A )
   A__											=					torch.div(sizedim - size ,	__A ,	rounding_mode="floor" ) + 1
   A__											=					torch.arange(__A ) + low_indices[:min_length][:, None]

   A__											=					[slice(__A )] * rank
   A__											=					indices
   A__											=					input[s]

   A__											=					list(range(0 ,	rank + 1 ) )
   perm.append(perm.pop(dimension + 1 ) )

   return sliced.permute(__A )



def        __a							(			A ,	A )       ->						List[str]:



   '''simple docstring'''



   import torch

   A__											=					torch.arange(1 ,	__A )
   A__											=					torch.remainder(__A ,	__A )
   A__											=					remainders == 0
   A__											=					candidates[divisor_indices]
   A__											=					torch.max(__A )
   return largest_divisor, torch.div(__A ,	__A ,	rounding_mode="floor" )


class       lowerCAmelCase__				(   __A  ):







 @property
 def  lowercase_       (   self					):

    '''simple docstring'''


    A__											=					OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
    if self.use_past:
       self.fill_with_past_key_values_(UpperCamelCase__		,	direction="inputs"					)
       A__											=					{0: '''batch''', 1: '''past_sequence + sequence'''}
    else:
       A__											=					{0: '''batch''', 1: '''sequence'''}

    return common_inputs







 @property
 def  lowercase_       (   self					):

    '''simple docstring'''


    return self._config.num_heads







 def  lowercase_       (   self		,	UpperCamelCase__		,	UpperCamelCase__ = -1		,	UpperCamelCase__ = -1		,	UpperCamelCase__ = False		,	UpperCamelCase__ = None		,	):

    '''simple docstring'''


    A__											=					super(UpperCamelCase__		,	self					).generate_dummy_inputs(
        UpperCamelCase__		,	batch_size=UpperCamelCase__		,	seq_length=UpperCamelCase__		,	is_pair=UpperCamelCase__		,	framework=UpperCamelCase__					)

    # We need to order the input in the way they appears in the forward()
    A__											=					OrderedDict({"input_ids": common_inputs["input_ids"]}					)

    # Need to add the past_keys
    if self.use_past:
       if not is_torch_available():
          raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
       else:
          import torch

          A__											=					common_inputs['''input_ids'''].shape
          # Not using the same length for past_key_values
          A__											=					seqlen + 2
          A__											=					(
              batch,
              self.num_attention_heads,
              past_key_values_length,
              self._config.hidden_size // self.num_attention_heads,
          )
          A__											=					[
              (torch.zeros(UpperCamelCase__					), torch.zeros(UpperCamelCase__					)) for _ in range(self.num_layers					)
          ]

    A__											=					common_inputs['''attention_mask''']
    if self.use_past:
       A__											=					ordered_inputs['''attention_mask'''].dtype
       A__											=					torch.cat(
           [ordered_inputs["attention_mask"], torch.ones(UpperCamelCase__		,	UpperCamelCase__		,	dtype=UpperCamelCase__					)]		,	dim=1					)

    return ordered_inputs







 @property
 def  lowercase_       (   self					):

    '''simple docstring'''


    return 13







'''simple docstring'''



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
      import torch
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      '''char'''
      UpperCamelCase_             =      '''bpe'''
      UpperCamelCase_             =      '''wp'''





SCREAMING_SNAKE_CASE							=						(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      ['''image_processor''', '''char_tokenizer''']
      UpperCamelCase_             =      '''ViTImageProcessor'''
      UpperCamelCase_             =      '''MgpstrTokenizer'''






      def __init__(			self      :		int  ,   UpperCAmelCase      :		Tuple=None  ,   UpperCAmelCase      :		Dict=None  ,   **UpperCAmelCase      :		Any		)	->	List[str]:


       '''simple docstring'''

       lowercase       : str						=None
       if "feature_extractor" in kwargs:
        warnings.warn(
            '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
            ''' instead.'''  ,   UpperCAmelCase  ,   )
        lowercase       : int						=kwargs.pop('''feature_extractor'''		)

       lowercase       : Optional[Any]						=image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
        raise ValueError('''You need to specify an `image_processor`.'''		)
       if tokenizer is None:
        raise ValueError('''You need to specify a `tokenizer`.'''		)

       lowercase       : Any						=tokenizer
       lowercase       : Dict						=AutoTokenizer.from_pretrained('''gpt2'''		)
       lowercase       : Optional[int]						=AutoTokenizer.from_pretrained('''bert-base-uncased'''		)

       super().__init__(UpperCAmelCase  ,   UpperCAmelCase		)






      def __call__(			self      :		List[Any]  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   UpperCAmelCase      :		List[str]=None  ,   UpperCAmelCase      :		Dict=None  ,   **UpperCAmelCase      :		Union[str, Any]		)	->	int:


       '''simple docstring'''

       if images is None and text is None:
        raise ValueError('''You need to specify either an `images` or `text` input to process.'''		)

       if images is not None:
        lowercase       : str						=self.image_processor(UpperCAmelCase  ,   return_tensors=UpperCAmelCase  ,   **UpperCAmelCase		)
       if text is not None:
        lowercase       : Any						=self.char_tokenizer(UpperCAmelCase  ,   return_tensors=UpperCAmelCase  ,   **UpperCAmelCase		)

       if text is None:
        return inputs
       elif images is None:
        return encodings
       else:
        lowercase       : List[str]						=encodings['''input_ids''']
        return inputs






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Optional[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase	,		lowercase	,		lowercase       : Union[str, Any]						=sequences
       lowercase       : Dict						=char_preds.size(0		)

       lowercase	,		lowercase       : Any						=self._decode_helper(UpperCAmelCase  ,   '''char'''		)
       lowercase	,		lowercase       : int						=self._decode_helper(UpperCAmelCase  ,   '''bpe'''		)
       lowercase	,		lowercase       : Optional[Any]						=self._decode_helper(UpperCAmelCase  ,   '''wp'''		)

       lowercase       : Dict						=[]
       lowercase       : List[Any]						=[]
       for i in range(UpperCAmelCase		):
        lowercase       : List[str]						=[char_scores[i], bpe_scores[i], wp_scores[i]]
        lowercase       : Union[str, Any]						=[char_strs[i], bpe_strs[i], wp_strs[i]]
        lowercase       : List[str]						=scores.index(max(UpperCAmelCase		)		)
        final_strs.append(strs[max_score_index]		)
        final_scores.append(scores[max_score_index]		)

       lowercase       : List[Any]						={}
       lowercase       : Optional[Any]						=final_strs
       lowercase       : Optional[Any]						=final_scores
       lowercase       : str						=char_strs
       lowercase       : Tuple						=bpe_strs
       lowercase       : List[str]						=wp_strs
       return out






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		Union[str, Any]		)	->	Optional[int]:


       '''simple docstring'''

       if format == DecodeType.CHARACTER:
        lowercase       : Union[str, Any]						=self.char_decode
        lowercase       : List[str]						=1
        lowercase       : Tuple						='''[s]'''
       elif format == DecodeType.BPE:
        lowercase       : Union[str, Any]						=self.bpe_decode
        lowercase       : Optional[int]						=2
        lowercase       : Optional[Any]						='''#'''
       elif format == DecodeType.WORDPIECE:
        lowercase       : List[str]						=self.wp_decode
        lowercase       : Union[str, Any]						=102
        lowercase       : int						='''[SEP]'''
       else:
        raise ValueError(f'Format {format} is not supported.'		)

       lowercase	,		lowercase       : Union[str, Any]						=[], []
       lowercase       : Optional[int]						=pred_logits.size(0		)
       lowercase       : List[str]						=pred_logits.size(1		)
       lowercase	,		lowercase       : Optional[int]						=pred_logits.topk(1  ,   dim=-1  ,   largest=UpperCAmelCase  ,   sorted=UpperCAmelCase		)
       lowercase       : Tuple						=preds_index.view(-1  ,   UpperCAmelCase		)[:, 1:]
       lowercase       : Any						=decoder(UpperCAmelCase		)
       lowercase	,		lowercase       : List[str]						=torch.nn.functional.softmax(UpperCAmelCase  ,   dim=2		).max(dim=2		)
       lowercase       : List[Any]						=preds_max_prob[:, 1:]

       for index in range(UpperCAmelCase		):
        lowercase       : List[Any]						=preds_str[index].find(UpperCAmelCase		)
        lowercase       : Any						=preds_str[index][:pred_eos]
        lowercase       : Any						=preds_index[index].cpu().tolist()
        lowercase       : Optional[int]						=pred_index.index(UpperCAmelCase		) if eos_token in pred_index else -1
        lowercase       : int						=preds_max_prob[index][: pred_eos_index + 1]
        lowercase       : Tuple						=pred_max_prob.cumprod(dim=0		)[-1] if pred_max_prob.nelement() != 0 else 0.0
        dec_strs.append(UpperCAmelCase		)
        conf_scores.append(UpperCAmelCase		)

       return dec_strs, conf_scores






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		Optional[int]		)	->	Dict:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=[seq.replace(''' '''  ,   ''''''		) for seq in self.char_tokenizer.batch_decode(UpperCAmelCase		)]
       return decode_strs






      def 					A__   (			self      :		Optional[int]  ,   UpperCAmelCase      :		Optional[Any]		)	->	str:


       '''simple docstring'''

       return self.bpe_tokenizer.batch_decode(UpperCAmelCase		)






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		Dict		)	->	List[str]:


       '''simple docstring'''

       lowercase       : str						=[seq.replace(''' '''  ,   ''''''		) for seq in self.wp_tokenizer.batch_decode(UpperCAmelCase		)]
       return decode_strs



