





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      UpperCamelCase_						(					_lowerCamelCase   ):
     lowerCAmelCase_							=		'''gpt_neo'''
     lowerCAmelCase_							=		['''past_key_values''']
     lowerCAmelCase_							=		{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




     def __init__(						self			,  lowerCAmelCase_=5_0257			,  lowerCAmelCase_=2048			,  lowerCAmelCase_=2048			,  lowerCAmelCase_=24			,  lowerCAmelCase_=[[["global", "local"], 12]]			,  lowerCAmelCase_=16			,  lowerCAmelCase_=None			,  lowerCAmelCase_=256			,  lowerCAmelCase_="gelu_new"			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.1			,  lowerCAmelCase_=1E-5			,  lowerCAmelCase_=0.02			,  lowerCAmelCase_=True			,  lowerCAmelCase_=5_0256			,  lowerCAmelCase_=5_0256			,  **lowerCAmelCase_			,  )						->							Tuple:
          _snake_case													=			vocab_size
          _snake_case													=			max_position_embeddings
          _snake_case													=			hidden_size
          _snake_case													=			num_layers
          _snake_case													=			num_heads
          _snake_case													=			intermediate_size
          _snake_case													=			window_size
          _snake_case													=			activation_function
          _snake_case													=			resid_dropout
          _snake_case													=			embed_dropout
          _snake_case													=			attention_dropout
          _snake_case													=			classifier_dropout
          _snake_case													=			layer_norm_epsilon
          _snake_case													=			initializer_range
          _snake_case													=			use_cache

          _snake_case													=			bos_token_id
          _snake_case													=			eos_token_id

          _snake_case													=			attention_types
          _snake_case													=			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 		lowerCAmelCase     (						lowerCAmelCase_					)						->							Any:
          _snake_case													=			[]
          for item in attention_types:
               for _ in range(item[1]					):
                    attentions.extend(item[0]					)
          return attentions





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]  )     ->	Any:



     '''simple docstring'''




     import torch

     _snake_case													=			input.size()
     _snake_case													=			len(UpperCamelCase__  )
     _snake_case													=			shape[dimension]

     _snake_case													=			torch.arange(0    ,      UpperCamelCase__    ,      UpperCamelCase__  )
     _snake_case													=			torch.div(sizedim - size    ,      UpperCamelCase__    ,      rounding_mode='floor'  ) + 1
     _snake_case													=			torch.arange(UpperCamelCase__  ) + low_indices[:min_length][:, None]

     _snake_case													=			[slice(UpperCamelCase__  )] * rank
     _snake_case													=			indices
     _snake_case													=			input[s]

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

     return sliced.permute(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Dict  )     ->	str:



     '''simple docstring'''




     import torch

     _snake_case													=			torch.arange(1    ,      UpperCamelCase__  )
     _snake_case													=			torch.remainder(UpperCamelCase__    ,      UpperCamelCase__  )
     _snake_case													=			remainders == 0
     _snake_case													=			candidates[divisor_indices]
     _snake_case													=			torch.max(UpperCamelCase__  )
     return largest_divisor, torch.div(UpperCamelCase__    ,      UpperCamelCase__    ,      rounding_mode='floor'  )




class      UpperCamelCase_						(					_lowerCamelCase   ):




     @property
     def 		lowerCAmelCase     (						self					)						->							Mapping[str, Mapping[int, str]]:
          _snake_case													=			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}					)
          if self.use_past:
               self.fill_with_past_key_values_(lowerCAmelCase_			,  direction='inputs'					)
               _snake_case													=			{0: 'batch', 1: 'past_sequence + sequence'}
          else:
               _snake_case													=			{0: 'batch', 1: 'sequence'}

          return common_inputs




     @property
     def 		lowerCAmelCase     (						self					)						->							int:
          return self._config.num_heads




     def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = -1			,  lowerCAmelCase_ = -1			,  lowerCAmelCase_ = False			,  lowerCAmelCase_ = None			,  )						->							Mapping[str, Any]:
          _snake_case													=			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()
          _snake_case													=			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

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

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

          return ordered_inputs






     @property
     def 		lowerCAmelCase     (						self					)						->							int:
          return 13










import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


UpperCAmelCase_ 							=       logging.get_logger(__name__)
class      UpperCamelCase_						(					_lowerCamelCase   ):




					def __init__(						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							None:
										warnings.warn(
										    'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
										    ' use FlavaImageProcessor instead.'			,  lowerCAmelCase_			,  )
										super().__init__(*lowerCAmelCase_			,  **lowerCAmelCase_					)



