

'''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


SCREAMING_SNAKE_CASE_     = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     = {
    """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       SCREAMING_SNAKE_CASE       (				lowercase_       ):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					Union[str, Any]     =					'gpt_neo'
      SCREAMING_SNAKE_CASE__			:					str     =					['past_key_values']
      SCREAMING_SNAKE_CASE__			:					str     =					{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}




      def __init__(       self			:		Optional[int]  ,       snake_case			:		List[Any]=50257  ,       snake_case			:		Union[str, Any]=2048  ,       snake_case			:		int=2048  ,       snake_case			:		Optional[Any]=24  ,       snake_case			:		Dict=[[["global", "local"], 12]]  ,       snake_case			:		List[Any]=16  ,       snake_case			:		Any=None  ,       snake_case			:		List[Any]=256  ,       snake_case			:		Any="gelu_new"  ,       snake_case			:		List[Any]=0.0  ,       snake_case			:		Optional[int]=0.0  ,       snake_case			:		Union[str, Any]=0.0  ,       snake_case			:		Union[str, Any]=0.1  ,       snake_case			:		int=1e-5  ,       snake_case			:		Optional[int]=0.02  ,       snake_case			:		Optional[Any]=True  ,       snake_case			:		Tuple=50256  ,       snake_case			:		Any=50256  ,       **snake_case			:		Union[str, Any]  ,       ):



          """simple docstring"""





          _snake_case			:			Any				       =		vocab_size
          _snake_case			:			Dict				       =		max_position_embeddings
          _snake_case			:			List[str]				       =		hidden_size
          _snake_case			:			Tuple				       =		num_layers
          _snake_case			:			Any				       =		num_heads
          _snake_case			:			int				       =		intermediate_size
          _snake_case			:			List[Any]				       =		window_size
          _snake_case			:			List[str]				       =		activation_function
          _snake_case			:			Optional[Any]				       =		resid_dropout
          _snake_case			:			Union[str, Any]				       =		embed_dropout
          _snake_case			:			Union[str, Any]				       =		attention_dropout
          _snake_case			:			Tuple				       =		classifier_dropout
          _snake_case			:			Dict				       =		layer_norm_epsilon
          _snake_case			:			Tuple				       =		initializer_range
          _snake_case			:			List[str]				       =		use_cache

          _snake_case			:			List[Any]				       =		bos_token_id
          _snake_case			:			str				       =		eos_token_id

          _snake_case			:			Any				       =		attention_types
          _snake_case			:			Optional[int]				       =		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 						__UpperCAmelCase      (       snake_case			:		Union[str, Any] ):



          """simple docstring"""





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



def 	lowerCamelCase__							(   a__      ,   a__      ,   a__      ,   a__)		->     Dict:



    """simple docstring"""







    import torch

    _snake_case			:			List[Any]				       =		input.size()
    _snake_case			:			str				       =		len(lowerCAmelCase__)
    _snake_case			:			Tuple				       =		shape[dimension]

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

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

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

    return sliced.permute(lowerCAmelCase__)

def 	lowerCamelCase__							(   a__      ,   a__)		->     Optional[int]:



    """simple docstring"""







    import torch

    _snake_case			:			List[str]				       =		torch.arange(1      ,   lowerCAmelCase__)
    _snake_case			:			Tuple				       =		torch.remainder(lowerCAmelCase__      ,   lowerCAmelCase__)
    _snake_case			:			int				       =		remainders == 0
    _snake_case			:			Union[str, Any]				       =		candidates[divisor_indices]
    _snake_case			:			Optional[Any]				       =		torch.max(lowerCAmelCase__)
    return largest_divisor, torch.div(lowerCAmelCase__      ,   lowerCAmelCase__      ,   rounding_mode='floor')




class       SCREAMING_SNAKE_CASE       (				lowercase_       ):






      '''simple docstring'''






      @property
      def 						__UpperCAmelCase      (       self			:		List[Any] ):



          """simple docstring"""





          _snake_case			:			List[str]				       =		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
          if self.use_past:
              self.fill_with_past_key_values_(__lowerCAmelCase  ,       direction='inputs' )
              _snake_case			:			Optional[Any]				       =		{0: 'batch', 1: 'past_sequence + sequence'}
          else:
              _snake_case			:			Union[str, Any]				       =		{0: 'batch', 1: 'sequence'}

          return common_inputs




      @property
      def 						__UpperCAmelCase      (       self			:		List[Any] ):



          """simple docstring"""





          return self._config.num_heads




      def 						__UpperCAmelCase      (       self			:		Dict  ,       snake_case			:		List[str]  ,       snake_case			:		List[str] = -1  ,       snake_case			:		Optional[int] = -1  ,       snake_case			:		Union[str, Any] = False  ,       snake_case			:		str = None  ,       ):



          """simple docstring"""





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

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

          return ordered_inputs




      @property
      def 						__UpperCAmelCase      (       self			:		Tuple ):



          """simple docstring"""





          return 13




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase       :			Optional[int]   	={
    """configuration_chinese_clip""": [
        """CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ChineseCLIPConfig""",
        """ChineseCLIPOnnxConfig""",
        """ChineseCLIPTextConfig""",
        """ChineseCLIPVisionConfig""",
    ],
    """processing_chinese_clip""": ["""ChineseCLIPProcessor"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase       :			List[Any]   	=["""ChineseCLIPFeatureExtractor"""]
       __lowerCAmelCase       :			List[Any]   	=["""ChineseCLIPImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase       :			Tuple   	=[
           """CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """ChineseCLIPModel""",
           """ChineseCLIPPreTrainedModel""",
           """ChineseCLIPTextModel""",
           """ChineseCLIPVisionModel""",
       ]

if TYPE_CHECKING:
       from .configuration_chinese_clip import (
           CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           ChineseCLIPConfig,
           ChineseCLIPOnnxConfig,
           ChineseCLIPTextConfig,
           ChineseCLIPVisionConfig,
       )
       from .processing_chinese_clip import ChineseCLIPProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_chinese_clip import (
                  CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ChineseCLIPModel,
                  ChineseCLIPPreTrainedModel,
                  ChineseCLIPTextModel,
                  ChineseCLIPVisionModel,
              )

else:
       import sys

       __lowerCAmelCase       :			Optional[Any]   	=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



