
"""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 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Tuple     							=   'gpt_neo'
					_SCREAMING_SNAKE_CASE       :					str     							=   ['past_key_values']
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





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

												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=snake_case_							,						eos_token_id=snake_case_							,						**snake_case_    )





					@staticmethod
					def 							lowerCAmelCase      (				snake_case_   :							Optional[int]    ):
												__snake_case     			=       []
												for item in attention_types:
																			for _ in range(item[1]    ):
																										attentions.extend(item[0]    )
												return attentions




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							import torch

							__snake_case     			=       input.size()
							__snake_case     			=       len(SCREAMING_SNAKE_CASE )
							__snake_case     			=       shape[dimension]

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

							__snake_case     			=       [slice(SCREAMING_SNAKE_CASE )] * rank
							__snake_case     			=       indices
							__snake_case     			=       input[s]

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

							return sliced.permute(SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							import torch

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







class 							__magic_name__     (		lowercase__   ):





					@property
					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}    )
												if self.use_past:
																			self.fill_with_past_key_values_(snake_case_							,						direction="inputs"    )
																			__snake_case     			=       {0: "batch", 1: "past_sequence + sequence"}
												else:
																			__snake_case     			=       {0: "batch", 1: "sequence"}

												return common_inputs





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												return self._config.num_heads





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							PreTrainedTokenizer							,						snake_case_   :							int = -1							,						snake_case_   :							int = -1							,						snake_case_   :							bool = False							,						snake_case_   :							Optional[TensorType] = None							,						):
												__snake_case     			=       super(snake_case_							,						self    ).generate_dummy_inputs(
												    snake_case_							,						batch_size=snake_case_							,						seq_length=snake_case_							,						is_pair=snake_case_							,						framework=snake_case_    )

												# 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(snake_case_    ), torch.zeros(snake_case_    )) 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(snake_case_							,						snake_case_							,						dtype=snake_case_    )]							,						dim=1    )

												return ordered_inputs





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												return 13


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      ["""MobileViTFeatureExtractor"""]
		_SCREAMING_SNAKE_CASE	       =      ["""MobileViTImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """MobileViTForImageClassification""",
		    """MobileViTForSemanticSegmentation""",
		    """MobileViTModel""",
		    """MobileViTPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFMobileViTForImageClassification""",
		    """TFMobileViTForSemanticSegmentation""",
		    """TFMobileViTModel""",
		    """TFMobileViTPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilevit import MobileViTFeatureExtractor
				from .image_processing_mobilevit import MobileViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevit import (
				    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTForImageClassification,
				    MobileViTForSemanticSegmentation,
				    MobileViTModel,
				    MobileViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilevit import (
				    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileViTForImageClassification,
				    TFMobileViTForSemanticSegmentation,
				    TFMobileViTModel,
				    TFMobileViTPreTrainedModel,
				)


else:
		import sys

		_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
