

"""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


A_							:			Optional[int]											= logging.get_logger(__name__)

A_							:			Tuple											= {
    "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    lowerCamelCase						(A__			):
			lowerCamelCase__   :		List[Any]							=							'gpt_neo'
			lowerCamelCase__   :		Any							=							['past_key_values']
			lowerCamelCase__   :		Tuple							=							{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}

			def __init__(					self				:						Any    ,				__UpperCAmelCase				:						str=5_0_2_5_7    ,				__UpperCAmelCase				:						Any=2_0_4_8    ,				__UpperCAmelCase				:						Dict=2_0_4_8    ,				__UpperCAmelCase				:						List[str]=2_4    ,				__UpperCAmelCase				:						List[Any]=[[["global", "local"], 1_2]]    ,				__UpperCAmelCase				:						int=1_6    ,				__UpperCAmelCase				:						Optional[int]=None    ,				__UpperCAmelCase				:						Tuple=2_5_6    ,				__UpperCAmelCase				:						Optional[int]="gelu_new"    ,				__UpperCAmelCase				:						List[str]=0.0    ,				__UpperCAmelCase				:						int=0.0    ,				__UpperCAmelCase				:						Any=0.0    ,				__UpperCAmelCase				:						str=0.1    ,				__UpperCAmelCase				:						List[str]=1e-5    ,				__UpperCAmelCase				:						Union[str, Any]=0.02    ,				__UpperCAmelCase				:						str=True    ,				__UpperCAmelCase				:						Any=5_0_2_5_6    ,				__UpperCAmelCase				:						Dict=5_0_2_5_6    ,				**__UpperCAmelCase				:						Dict    ,				)   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      vocab_size
						SCREAMING_SNAKE_CASE__								=      max_position_embeddings
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      num_layers
						SCREAMING_SNAKE_CASE__								=      num_heads
						SCREAMING_SNAKE_CASE__								=      intermediate_size
						SCREAMING_SNAKE_CASE__								=      window_size
						SCREAMING_SNAKE_CASE__								=      activation_function
						SCREAMING_SNAKE_CASE__								=      resid_dropout
						SCREAMING_SNAKE_CASE__								=      embed_dropout
						SCREAMING_SNAKE_CASE__								=      attention_dropout
						SCREAMING_SNAKE_CASE__								=      classifier_dropout
						SCREAMING_SNAKE_CASE__								=      layer_norm_epsilon
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      use_cache

						SCREAMING_SNAKE_CASE__								=      bos_token_id
						SCREAMING_SNAKE_CASE__								=      eos_token_id

						SCREAMING_SNAKE_CASE__								=      attention_types
						SCREAMING_SNAKE_CASE__								=      self.expand_attention_types_params(__UpperCAmelCase       )

						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=__UpperCAmelCase    ,				eos_token_id=__UpperCAmelCase    ,				**__UpperCAmelCase       )





			@staticmethod
			def 					SCREAMING_SNAKE_CASE				(					__UpperCAmelCase				:						List[Any]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      []
						for item in attention_types:
									for _ in range(item[1]       ):
												attentions.extend(item[0]       )
						return attentions

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			import torch

			SCREAMING_SNAKE_CASE__								=      input.size()
			SCREAMING_SNAKE_CASE__								=      len(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      shape[dimension]

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

			SCREAMING_SNAKE_CASE__								=      [slice(snake_case__    )] * rank
			SCREAMING_SNAKE_CASE__								=      indices
			SCREAMING_SNAKE_CASE__								=      input[s]

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

			return sliced.permute(snake_case__    )

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			import torch

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





class    lowerCamelCase						(A__			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Mapping[str, Mapping[int, str]]:
						SCREAMING_SNAKE_CASE__								=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}       )
						if self.use_past:
									self.fill_with_past_key_values_(__UpperCAmelCase    ,				direction="""inputs"""       )
									SCREAMING_SNAKE_CASE__								=      {0: """batch""", 1: """past_sequence + sequence"""}
						else:
									SCREAMING_SNAKE_CASE__								=      {0: """batch""", 1: """sequence"""}

						return common_inputs

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    int:
						return self._config.num_heads

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						PreTrainedTokenizer    ,				__UpperCAmelCase				:						int = -1    ,				__UpperCAmelCase				:						int = -1    ,				__UpperCAmelCase				:						bool = False    ,				__UpperCAmelCase				:						Optional[TensorType] = None    ,				)   ->    Mapping[str, Any]:
						SCREAMING_SNAKE_CASE__								=      super(__UpperCAmelCase    ,				self       ).generate_dummy_inputs(
						    __UpperCAmelCase    ,				batch_size=__UpperCAmelCase    ,				seq_length=__UpperCAmelCase    ,				is_pair=__UpperCAmelCase    ,				framework=__UpperCAmelCase       )

						# We need to order the input in the way they appears in the forward()
						SCREAMING_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

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

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

						return ordered_inputs





			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    int:
						return 1_3





"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_							:			Dict											= {
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:			List[Any]											= ["EfficientNetImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:			Dict											= [
						    "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "EfficientNetForImageClassification",
						    "EfficientNetModel",
						    "EfficientNetPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_efficientnet import (
						    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    EfficientNetConfig,
						    EfficientNetOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_efficientnet import EfficientNetImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_efficientnet import (
												    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    EfficientNetForImageClassification,
												    EfficientNetModel,
												    EfficientNetPreTrainedModel,
												)

else:
						import sys

						A_							:			List[Any]											= _LazyModule(__name__, globals()["__file__"], _import_structure)


