

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				:    List[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     __lowercase							(					UpperCAmelCase__      ):




			"""simple docstring"""







			_UpperCAmelCase  :				Any     				=	'''gpt_neo'''
			_UpperCAmelCase  :				List[Any]     				=	['''past_key_values''']
			_UpperCAmelCase  :				Dict     				=	{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



			def __init__(					self    :	Optional[Any]  ,		lowerCAmelCase__    :	List[str]=5_0257  ,		lowerCAmelCase__    :	Optional[int]=2048  ,		lowerCAmelCase__    :	List[Any]=2048  ,		lowerCAmelCase__    :	Any=24  ,		lowerCAmelCase__    :	Optional[int]=[[["global", "local"], 12]]  ,		lowerCAmelCase__    :	List[Any]=16  ,		lowerCAmelCase__    :	Dict=None  ,		lowerCAmelCase__    :	Union[str, Any]=256  ,		lowerCAmelCase__    :	Tuple="gelu_new"  ,		lowerCAmelCase__    :	Optional[Any]=0.0  ,		lowerCAmelCase__    :	Optional[int]=0.0  ,		lowerCAmelCase__    :	Optional[int]=0.0  ,		lowerCAmelCase__    :	List[Any]=0.1  ,		lowerCAmelCase__    :	List[str]=1E-5  ,		lowerCAmelCase__    :	Union[str, Any]=0.02  ,		lowerCAmelCase__    :	List[Any]=True  ,		lowerCAmelCase__    :	int=5_0256  ,		lowerCAmelCase__    :	List[Any]=5_0256  ,		**lowerCAmelCase__    :	List[str]  ,		):
									SCREAMING_SNAKE_CASE_:					List[Any]    						=			vocab_size
									SCREAMING_SNAKE_CASE_:					Any    						=			max_position_embeddings
									SCREAMING_SNAKE_CASE_:					Dict    						=			hidden_size
									SCREAMING_SNAKE_CASE_:					int    						=			num_layers
									SCREAMING_SNAKE_CASE_:					List[Any]    						=			num_heads
									SCREAMING_SNAKE_CASE_:					Tuple    						=			intermediate_size
									SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			window_size
									SCREAMING_SNAKE_CASE_:					Optional[int]    						=			activation_function
									SCREAMING_SNAKE_CASE_:					int    						=			resid_dropout
									SCREAMING_SNAKE_CASE_:					str    						=			embed_dropout
									SCREAMING_SNAKE_CASE_:					int    						=			attention_dropout
									SCREAMING_SNAKE_CASE_:					Any    						=			classifier_dropout
									SCREAMING_SNAKE_CASE_:					int    						=			layer_norm_epsilon
									SCREAMING_SNAKE_CASE_:					int    						=			initializer_range
									SCREAMING_SNAKE_CASE_:					List[str]    						=			use_cache

									SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			bos_token_id
									SCREAMING_SNAKE_CASE_:					Dict    						=			eos_token_id

									SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			attention_types
									SCREAMING_SNAKE_CASE_:					Any    						=			self.expand_attention_types_params(__lowercase)

									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=__lowercase  ,		eos_token_id=__lowercase  ,		**__lowercase)




			@staticmethod
			def 			_SCREAMING_SNAKE_CASE     (					lowerCAmelCase__    :	Dict):
									SCREAMING_SNAKE_CASE_:					Dict    						=			[]
									for item in attention_types:
															for _ in range(item[1]):
																					attentions.extend(item[0])
									return attentions







def      A_ (       _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase       ):
						import torch

						SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			input.size()
						SCREAMING_SNAKE_CASE_:					str    						=			len(_A       )
						SCREAMING_SNAKE_CASE_:					Dict    						=			shape[dimension]

						SCREAMING_SNAKE_CASE_:					List[Any]    						=			torch.arange(0							,      _A							,      _A       )
						SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			torch.div(sizedim - size							,      _A							,      rounding_mode="floor"       ) + 1
						SCREAMING_SNAKE_CASE_:					int    						=			torch.arange(_A       ) + low_indices[:min_length][:, None]

						SCREAMING_SNAKE_CASE_:					Tuple    						=			[slice(_A       )] * rank
						SCREAMING_SNAKE_CASE_:					List[Any]    						=			indices
						SCREAMING_SNAKE_CASE_:					List[str]    						=			input[s]

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

						return sliced.permute(_A       )







def      A_ (       _UpperCAmelCase							,      _UpperCAmelCase       ):
						import torch

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



class     __lowercase							(					UpperCAmelCase__      ):




			"""simple docstring"""







			@property
			def 			_SCREAMING_SNAKE_CASE     (					self    :	int):
									SCREAMING_SNAKE_CASE_:					Optional[int]    						=			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
									if self.use_past:
															self.fill_with_past_key_values_(__lowercase  ,		direction="inputs")
															SCREAMING_SNAKE_CASE_:					Any    						=			{0: "batch", 1: "past_sequence + sequence"}
									else:
															SCREAMING_SNAKE_CASE_:					Tuple    						=			{0: "batch", 1: "sequence"}

									return common_inputs



			@property
			def 			_SCREAMING_SNAKE_CASE     (					self    :	int):
									return self._config.num_heads



			def 			_SCREAMING_SNAKE_CASE     (					self    :	int  ,		lowerCAmelCase__    :	PreTrainedTokenizer  ,		lowerCAmelCase__    :	int = -1  ,		lowerCAmelCase__    :	int = -1  ,		lowerCAmelCase__    :	bool = False  ,		lowerCAmelCase__    :	Optional[TensorType] = None  ,		):
									SCREAMING_SNAKE_CASE_:					Dict    						=			super(__lowercase  ,		self).generate_dummy_inputs(
									    __lowercase  ,		batch_size=__lowercase  ,		seq_length=__lowercase  ,		is_pair=__lowercase  ,		framework=__lowercase)

									# We need to order the input in the way they appears in the forward()
									SCREAMING_SNAKE_CASE_:					Dict    						=			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[int]    						=			common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					SCREAMING_SNAKE_CASE_:					List[Any]    						=			seqlen + 2
																					SCREAMING_SNAKE_CASE_:					List[str]    						=			(
																					    batch,
																					    self.num_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // self.num_attention_heads,
																					)
																					SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			[
																					    (torch.zeros(__lowercase), torch.zeros(__lowercase)) for _ in range(self.num_layers)
																					]

									SCREAMING_SNAKE_CASE_:					Tuple    						=			common_inputs["attention_mask"]
									if self.use_past:
															SCREAMING_SNAKE_CASE_:					List[Any]    						=			ordered_inputs["attention_mask"].dtype
															SCREAMING_SNAKE_CASE_:					Dict    						=			torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(__lowercase  ,		__lowercase  ,		dtype=__lowercase)]  ,		dim=1)

									return ordered_inputs




			@property
			def 			_SCREAMING_SNAKE_CASE     (					self    :	List[Any]):
									return 13












from typing import TYPE_CHECKING

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


lowercase__							:	Union[str, Any]											=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Tuple											=		["DPTFeatureExtractor"]
		lowercase__							:	Optional[Any]											=		["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Dict											=		[
		    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DPTForDepthEstimation",
		    "DPTForSemanticSegmentation",
		    "DPTModel",
		    "DPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

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