
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


__SCREAMING_SNAKE_CASE      : Dict       =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      : Optional[int]       =  {
    """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 		__magic_name__ (    _lowerCAmelCase     ):
		_lowerCAmelCase		=						"gpt_neo"
		_lowerCAmelCase		=						["past_key_values"]
		_lowerCAmelCase		=						{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





		def __init__(				self     :						Union[str, Any]    ,	lowerCamelCase__     :						Optional[int]=5_0_2_5_7    ,	lowerCamelCase__     :						str=2_0_4_8    ,	lowerCamelCase__     :						Any=2_0_4_8    ,	lowerCamelCase__     :						List[str]=2_4    ,	lowerCamelCase__     :						Dict=[[["global", "local"], 1_2]]    ,	lowerCamelCase__     :						List[Any]=1_6    ,	lowerCamelCase__     :						Union[str, Any]=None    ,	lowerCamelCase__     :						Dict=2_5_6    ,	lowerCamelCase__     :						List[str]="gelu_new"    ,	lowerCamelCase__     :						List[Any]=0.0    ,	lowerCamelCase__     :						List[Any]=0.0    ,	lowerCamelCase__     :						Any=0.0    ,	lowerCamelCase__     :						Any=0.1    ,	lowerCamelCase__     :						List[Any]=1E-5    ,	lowerCamelCase__     :						Tuple=0.0_2    ,	lowerCamelCase__     :						Any=True    ,	lowerCamelCase__     :						List[Any]=5_0_2_5_6    ,	lowerCamelCase__     :						Optional[int]=5_0_2_5_6    ,	**lowerCamelCase__     :						Optional[Any]    ,	):
							lowerCAmelCase		:       Union[str, Any]							=  vocab_size
							lowerCAmelCase		:       Optional[int]							=  max_position_embeddings
							lowerCAmelCase		:       Optional[Any]							=  hidden_size
							lowerCAmelCase		:       Union[str, Any]							=  num_layers
							lowerCAmelCase		:       Any							=  num_heads
							lowerCAmelCase		:       int							=  intermediate_size
							lowerCAmelCase		:       str							=  window_size
							lowerCAmelCase		:       Any							=  activation_function
							lowerCAmelCase		:       Tuple							=  resid_dropout
							lowerCAmelCase		:       List[Any]							=  embed_dropout
							lowerCAmelCase		:       Union[str, Any]							=  attention_dropout
							lowerCAmelCase		:       List[Any]							=  classifier_dropout
							lowerCAmelCase		:       Tuple							=  layer_norm_epsilon
							lowerCAmelCase		:       Union[str, Any]							=  initializer_range
							lowerCAmelCase		:       str							=  use_cache

							lowerCAmelCase		:       Union[str, Any]							=  bos_token_id
							lowerCAmelCase		:       int							=  eos_token_id

							lowerCAmelCase		:       Any							=  attention_types
							lowerCAmelCase		:       Tuple							=  self.expand_attention_types_params(lowerCamelCase__					)

							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=lowerCamelCase__    ,	eos_token_id=lowerCamelCase__    ,	**lowerCamelCase__					)





		@staticmethod
		def    _A				(				lowerCamelCase__     :						Optional[Any]					):
							lowerCAmelCase		:       Union[str, Any]							=  []
							for item in attention_types:
												for _ in range(item[1]					):
																	attentions.extend(item[0]					)
							return attentions



def      UpperCAmelCase__				(							__magic_name__    :  Tuple				,	__magic_name__    :  Dict				,	__magic_name__    :  Union[str, Any]				,	__magic_name__    :  int	):


					'''simple docstring'''

					import torch

					lowerCAmelCase		:       Union[str, Any]							=  input.size()
					lowerCAmelCase		:       Tuple							=  len(SCREAMING_SNAKE_CASE__	)
					lowerCAmelCase		:       Any							=  shape[dimension]

					lowerCAmelCase		:       Dict							=  torch.arange(0				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__	)
					lowerCAmelCase		:       Dict							=  torch.div(sizedim - size				,	SCREAMING_SNAKE_CASE__				,	rounding_mode='''floor'''	) + 1
					lowerCAmelCase		:       List[str]							=  torch.arange(SCREAMING_SNAKE_CASE__	) + low_indices[:min_length][:, None]

					lowerCAmelCase		:       List[str]							=  [slice(SCREAMING_SNAKE_CASE__	)] * rank
					lowerCAmelCase		:       Tuple							=  indices
					lowerCAmelCase		:       Union[str, Any]							=  input[s]

					lowerCAmelCase		:       List[Any]							=  list(range(0				,	rank + 1	)	)
					perm.append(perm.pop(dimension + 1	)	)

					return sliced.permute(SCREAMING_SNAKE_CASE__	)
def      UpperCAmelCase__				(							__magic_name__    :  int				,	__magic_name__    :  List[Any]	):


					'''simple docstring'''

					import torch

					lowerCAmelCase		:       Optional[int]							=  torch.arange(1				,	SCREAMING_SNAKE_CASE__	)
					lowerCAmelCase		:       Optional[int]							=  torch.remainder(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__	)
					lowerCAmelCase		:       str							=  remainders == 0
					lowerCAmelCase		:       Dict							=  candidates[divisor_indices]
					lowerCAmelCase		:       Any							=  torch.max(SCREAMING_SNAKE_CASE__	)
					return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	rounding_mode='''floor'''	)


class 		__magic_name__ (    _lowerCAmelCase     ):





		@property
		def    _A				(				self     :						Tuple					):
							lowerCAmelCase		:       Optional[int]							=  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}					)
							if self.use_past:
												self.fill_with_past_key_values_(lowerCamelCase__    ,	direction='''inputs'''					)
												lowerCAmelCase		:       List[Any]							=  {0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
												lowerCAmelCase		:       str							=  {0: '''batch''', 1: '''sequence'''}

							return common_inputs





		@property
		def    _A				(				self     :						Tuple					):
							return self._config.num_heads





		def    _A				(				self     :						Optional[int]    ,	lowerCamelCase__     :						List[str]    ,	lowerCamelCase__     :						List[Any] = -1    ,	lowerCamelCase__     :						List[str] = -1    ,	lowerCamelCase__     :						Dict = False    ,	lowerCamelCase__     :						Optional[int] = None    ,	):
							lowerCAmelCase		:       Dict							=  super(lowerCamelCase__    ,	self					).generate_dummy_inputs(
							    lowerCamelCase__    ,	batch_size=lowerCamelCase__    ,	seq_length=lowerCamelCase__    ,	is_pair=lowerCamelCase__    ,	framework=lowerCamelCase__					)

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase		:       Union[str, Any]							=  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

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

							lowerCAmelCase		:       Dict							=  common_inputs['''attention_mask''']
							if self.use_past:
												lowerCAmelCase		:       str							=  ordered_inputs['''attention_mask'''].dtype
												lowerCAmelCase		:       Dict							=  torch.cat(
												    [ordered_inputs['''attention_mask'''], torch.ones(lowerCamelCase__    ,	lowerCamelCase__    ,	dtype=lowerCamelCase__					)]    ,	dim=1					)

							return ordered_inputs





		@property
		def    _A				(				self     :						Any					):
							return 1_3








'''simple docstring'''



import copy
import random

from transformers import CLIPTokenizer
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      def __init__(	self			, *lowerCamelCase			, **lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         super().__init__(*lowerCamelCase			, **lowerCamelCase   )
         __a					  =     {}




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )				->Dict:



         '''simple docstring'''
         __a					  =     super().add_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
         if num_added_tokens == 0:
            raise ValueError(
                F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                ' `placeholder_token` that is not already in the tokenizer.'   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=1			, **lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         __a					  =     []
         if num_vec_per_token == 1:
            self.try_adding_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
            output.append(lowerCamelCase   )
         else:
            __a					  =     []
            for i in range(lowerCamelCase   ):
               __a					  =     placeholder_token + F"""_{i}"""
               self.try_adding_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
               output.append(lowerCamelCase   )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
         for token in self.token_map:
            if token in placeholder_token:
               raise ValueError(
                   F"""The tokenizer already has placeholder token {token} that can get confused with"""
                   F""" {placeholder_token}keep placeholder tokens independent"""   )
         __a					  =     output




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0   )				->int:



         '''simple docstring'''
         if isinstance(lowerCamelCase			, lowerCamelCase   ):
            __a					  =     []
            for i in range(len(lowerCamelCase   )   ):
               output.append(self.replace_placeholder_tokens_in_text(text[i]			, vector_shuffle=lowerCamelCase   )   )
            return output
         for placeholder_token in self.token_map:
            if placeholder_token in text:
               __a					  =     self.token_map[placeholder_token]
               __a					  =     tokens[: 1 + int(len(lowerCamelCase   ) * prop_tokens_to_load   )]
               if vector_shuffle:
                  __a					  =     copy.copy(lowerCamelCase   )
                  random.shuffle(lowerCamelCase   )
               __a					  =     text.replace(lowerCamelCase			, ' '.join(lowerCamelCase   )   )
         return text




      def __call__(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0			, **lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         return super().__call__(
             self.replace_placeholder_tokens_in_text(
                 lowerCamelCase			, vector_shuffle=lowerCamelCase			, prop_tokens_to_load=lowerCamelCase   )			, *lowerCamelCase			, **lowerCamelCase			, )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0			, **lowerCamelCase   )				->List[Any]:



         '''simple docstring'''
         return super().encode(
             self.replace_placeholder_tokens_in_text(
                 lowerCamelCase			, vector_shuffle=lowerCamelCase			, prop_tokens_to_load=lowerCamelCase   )			, *lowerCamelCase			, **lowerCamelCase			, )