"""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


__lowerCAmelCase       :			Tuple   	=logging.get_logger(__name__)

__lowerCAmelCase       :			Union[str, Any]   	={
    """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 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Union[str, Any]				    =      'gpt_neo'
      snake_case__     :  str				    =      ['past_key_values']
      snake_case__     :  str				    =      {'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=0.0_2						,			__lowerCAmelCase=True						,			__lowerCAmelCase=5_0256						,			__lowerCAmelCase=5_0256						,			**__lowerCAmelCase						,			):







            """simple docstring"""



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

            lowercase								=      bos_token_id
            lowercase								=      eos_token_id

            lowercase								=      attention_types
            lowercase								=      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		):







            """simple docstring"""



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






def      UpperCAmelCase__			(							lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:Union[str, Any]   ,						lowerCAmelCase__	:Union[str, Any]						)					->       Dict:
      '''simple docstring'''


      import torch

      lowercase								=      input.size()
      lowercase								=      len(lowerCAmelCase__						)
      lowercase								=      shape[dimension]

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

      lowercase								=      [slice(lowerCAmelCase__						)] * rank
      lowercase								=      indices
      lowercase								=      input[s]

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

      return sliced.permute(lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:List[Any]						)					->       Optional[int]:
      '''simple docstring'''


      import torch

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






class 					_A		(			lowerCAmelCase      ):

      @property
      def 							A__			(		self		):







            """simple docstring"""



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

            return common_inputs

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self._config.num_heads

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = -1						,			__lowerCAmelCase = -1						,			__lowerCAmelCase = False						,			__lowerCAmelCase = None						,			):







            """simple docstring"""



            lowercase								=      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()
            lowercase								=      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

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

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

            return ordered_inputs







      @property
      def 							A__			(		self		):







            """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__)



