

'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_						     =    logging.get_logger(__name__)

a_						     =    {
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='data2vec-vision'
			def __init__(			self							: Tuple      ,      a							: Any=768      ,      a							: Dict=12      ,      a							: Tuple=12      ,      a							: Any=3072      ,      a							: Any="gelu"      ,      a							: Dict=0.0      ,      a							: Optional[int]=0.0      ,      a							: Union[str, Any]=0.02      ,      a							: List[Any]=1e-12      ,      a							: str=224      ,      a							: List[Any]=16      ,      a							: str=3      ,      a							: Union[str, Any]=False      ,      a							: List[str]=False      ,      a							: int=False      ,      a							: Any=False      ,      a							: int=0.1      ,      a							: Any=0.1      ,      a							: Optional[Any]=True      ,      a							: Union[str, Any]=[3, 5, 7, 11]      ,      a							: Any=[1, 2, 3, 6]      ,      a							: str=True      ,      a							: int=0.4      ,      a							: Optional[int]=256      ,      a							: Tuple=1      ,      a							: Dict=False      ,      a							: str=255      ,      **a							: Tuple      ,      )							->       Dict:

				"""simple docstring"""

				super().__init__(**a	)

				SCREAMING_SNAKE_CASE   :      int				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      Any				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      str				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      int				     =       intermediate_size
				SCREAMING_SNAKE_CASE   :      List[str]				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      Tuple				     =       hidden_dropout_prob
				SCREAMING_SNAKE_CASE   :      List[str]				     =       attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       layer_norm_eps

				SCREAMING_SNAKE_CASE   :      List[str]				     =       image_size
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       patch_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_channels
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       use_mask_token
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       use_absolute_position_embeddings
				SCREAMING_SNAKE_CASE   :      Dict				     =       use_relative_position_bias
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       use_shared_relative_position_bias
				SCREAMING_SNAKE_CASE   :      Any				     =       layer_scale_init_value
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       drop_path_rate
				SCREAMING_SNAKE_CASE   :      Tuple				     =       use_mean_pooling
				# decode head attributes (semantic segmentation)
				SCREAMING_SNAKE_CASE   :      Any				     =       out_indices
				SCREAMING_SNAKE_CASE   :      int				     =       pool_scales
				# auxiliary head attributes (semantic segmentation)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       use_auxiliary_head
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       auxiliary_loss_weight
				SCREAMING_SNAKE_CASE   :      str				     =       auxiliary_channels
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       auxiliary_num_convs
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       auxiliary_concat_input
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       semantic_loss_ignore_index






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =version.parse('1.11'   )
			@property
			def 							__UpperCamelCase (			self							: List[str]	)							->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]	)






			@property
			def 							__UpperCamelCase (			self							: Any	)							->       float:

				"""simple docstring"""

				return 1e-4


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

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


a_						     =    logging.get_logger(__name__)

a_						     =    {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='gptj'
			lowerCamelCase__           ={
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}
			def __init__(			self							: Dict      ,      a							: Optional[Any]=5_0400      ,      a							: List[str]=2048      ,      a							: List[Any]=4096      ,      a							: int=28      ,      a							: Union[str, Any]=16      ,      a							: List[Any]=64      ,      a							: int=None      ,      a							: Optional[int]="gelu_new"      ,      a							: Optional[Any]=0.0      ,      a							: Any=0.0      ,      a							: Union[str, Any]=0.0      ,      a							: Union[str, Any]=1e-5      ,      a							: Any=0.02      ,      a							: Optional[int]=True      ,      a							: Tuple=5_0256      ,      a							: Union[str, Any]=5_0256      ,      a							: List[Any]=False      ,      **a							: str      ,      )							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      int				     =       n_positions
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_embd
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_layer
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       n_head
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_inner
				SCREAMING_SNAKE_CASE   :      Any				     =       rotary_dim
				SCREAMING_SNAKE_CASE   :      str				     =       activation_function
				SCREAMING_SNAKE_CASE   :      int				     =       resid_pdrop
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       embd_pdrop
				SCREAMING_SNAKE_CASE   :      Tuple				     =       attn_pdrop
				SCREAMING_SNAKE_CASE   :      List[str]				     =       layer_norm_epsilon
				SCREAMING_SNAKE_CASE   :      int				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Tuple				     =       use_cache

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       bos_token_id
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       eos_token_id

				super().__init__(
				    bos_token_id=a      ,      eos_token_id=a      ,      tie_word_embeddings=a      ,      **a	)






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: Optional[int]      ,      a							: PretrainedConfig      ,      a							: str = "default"      ,      a							: List[PatchingSpec] = None      ,      a							: bool = False      ,      )							->       Any:

				"""simple docstring"""

				super().__init__(a      ,      task=a      ,      patching_specs=a      ,      use_past=a	)
				if not getattr(self._config      ,      "pad_token_id"      ,      a	):
					# TODO: how to do that better?
					SCREAMING_SNAKE_CASE   :      Dict				     =       0
			@property
			def 							__UpperCamelCase (			self							: Any	)							->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
				if self.use_past:
					self.fill_with_past_key_values_(a      ,      direction="inputs"	)
					SCREAMING_SNAKE_CASE   :      int				     =       {0: "batch", 1: "past_sequence + sequence"}
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       {0: "batch", 1: "sequence"}

				return common_inputs
			@property
			def 							__UpperCamelCase (			self							: Any	)							->       int:

				"""simple docstring"""

				return self._config.n_layer
			@property
			def 							__UpperCamelCase (			self							: str	)							->       int:

				"""simple docstring"""

				return self._config.n_head
			def 							__UpperCamelCase (			self							: str      ,      a							: PreTrainedTokenizer      ,      a							: int = -1      ,      a							: int = -1      ,      a							: bool = False      ,      a							: Optional[TensorType] = None      ,      )							->       Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       super(a      ,      self	).generate_dummy_inputs(
				    a      ,      batch_size=a      ,      seq_length=a      ,      is_pair=a      ,      framework=a	)

				# We need to order the input in the way they appears in the forward()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       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

						SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       common_inputs["input_ids"].shape
						# Not using the same length for past_key_values
						SCREAMING_SNAKE_CASE   :      Any				     =       seqlen + 2
						SCREAMING_SNAKE_CASE   :      Dict				     =       (
						    batch,
						    self.num_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // self.num_attention_heads,
						)
						SCREAMING_SNAKE_CASE   :      str				     =       [
						    (torch.zeros(a	), torch.zeros(a	)) for _ in range(self.num_layers	)
						]

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       common_inputs["attention_mask"]
				if self.use_past:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       ordered_inputs["attention_mask"].dtype
					SCREAMING_SNAKE_CASE   :      Any				     =       torch.cat(
					    [ordered_inputs["attention_mask"], torch.ones(a      ,      a      ,      dtype=a	)]      ,      dim=1	)

				return ordered_inputs






			@property
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       int:

				"""simple docstring"""

				return 13