



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


_lowercase     							=						logging.get_logger(__name__)

_lowercase     							=						{
    '''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					( A_      ):
			UpperCamelCase					:Union[str, Any]     =			'''gpt_neo'''
			UpperCamelCase					:List[str]     =			['''past_key_values''']
			UpperCamelCase					:List[Any]     =			{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





			def __init__(self							,    __magic_name__=50257							,    __magic_name__=2048							,    __magic_name__=2048							,    __magic_name__=24							,    __magic_name__=[[["global", "local"], 12]]							,    __magic_name__=16							,    __magic_name__=None							,    __magic_name__=256							,    __magic_name__="gelu_new"							,    __magic_name__=0.0							,    __magic_name__=0.0							,    __magic_name__=0.0							,    __magic_name__=0.1							,    __magic_name__=1E-5							,    __magic_name__=0.02							,    __magic_name__=True							,    __magic_name__=50256							,    __magic_name__=50256							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Union[str, Any]								=		vocab_size
						lowerCamelCase__ :      Optional[int]								=		max_position_embeddings
						lowerCamelCase__ :      Optional[Any]								=		hidden_size
						lowerCamelCase__ :      Dict								=		num_layers
						lowerCamelCase__ :      Optional[Any]								=		num_heads
						lowerCamelCase__ :      int								=		intermediate_size
						lowerCamelCase__ :      Optional[Any]								=		window_size
						lowerCamelCase__ :      Union[str, Any]								=		activation_function
						lowerCamelCase__ :      Union[str, Any]								=		resid_dropout
						lowerCamelCase__ :      int								=		embed_dropout
						lowerCamelCase__ :      List[str]								=		attention_dropout
						lowerCamelCase__ :      Optional[Any]								=		classifier_dropout
						lowerCamelCase__ :      Any								=		layer_norm_epsilon
						lowerCamelCase__ :      List[str]								=		initializer_range
						lowerCamelCase__ :      Tuple								=		use_cache

						lowerCamelCase__ :      Optional[Any]								=		bos_token_id
						lowerCamelCase__ :      List[str]								=		eos_token_id

						lowerCamelCase__ :      Union[str, Any]								=		attention_types
						lowerCamelCase__ :      str								=		self.expand_attention_types_params(__magic_name__				)

						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=__magic_name__							,    eos_token_id=__magic_name__							,    **__magic_name__				)





			@staticmethod
			def        _snake_case    (__magic_name__				):
						lowerCamelCase__ :      Tuple								=		[]
						for item in attention_types:
									for _ in range(item[1]				):
												attentions.extend(item[0]				)
						return attentions







def      _A				(UpperCamelCase		:      List[Any]      ,			UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Optional[int] )  ->str:



			'''simple docstring'''







			import torch

			lowerCamelCase__ :      Union[str, Any]								=		input.size()
			lowerCamelCase__ :      Any								=		len(UpperCamelCase )
			lowerCamelCase__ :      Optional[Any]								=		shape[dimension]

			lowerCamelCase__ :      Tuple								=		torch.arange(0      ,			UpperCamelCase      ,			UpperCamelCase )
			lowerCamelCase__ :      Tuple								=		torch.div(sizedim - size      ,			UpperCamelCase      ,			rounding_mode="""floor""" ) + 1
			lowerCamelCase__ :      Optional[int]								=		torch.arange(UpperCamelCase ) + low_indices[:min_length][:, None]

			lowerCamelCase__ :      List[Any]								=		[slice(UpperCamelCase )] * rank
			lowerCamelCase__ :      Optional[Any]								=		indices
			lowerCamelCase__ :      Dict								=		input[s]

			lowerCamelCase__ :      List[Any]								=		list(range(0      ,			rank + 1 ) )
			perm.append(perm.pop(dimension + 1 ) )

			return sliced.permute(UpperCamelCase )






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      Any )  ->Any:



			'''simple docstring'''







			import torch

			lowerCamelCase__ :      Any								=		torch.arange(1      ,			UpperCamelCase )
			lowerCamelCase__ :      Dict								=		torch.remainder(UpperCamelCase      ,			UpperCamelCase )
			lowerCamelCase__ :      Dict								=		remainders == 0
			lowerCamelCase__ :      Any								=		candidates[divisor_indices]
			lowerCamelCase__ :      Union[str, Any]								=		torch.max(UpperCamelCase )
			return largest_divisor, torch.div(UpperCamelCase      ,			UpperCamelCase      ,			rounding_mode="""floor""" )
class 				__A					( A_      ):





			@property
			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}				)
						if self.use_past:
									self.fill_with_past_key_values_(__magic_name__							,    direction="""inputs"""				)
									lowerCamelCase__ :      Tuple								=		{0: """batch""", 1: """past_sequence + sequence"""}
						else:
									lowerCamelCase__ :      Tuple								=		{0: """batch""", 1: """sequence"""}

						return common_inputs





			@property
			def        _snake_case    (self				):
						return self._config.num_heads





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = -1							,    __magic_name__ = -1							,    __magic_name__ = False							,    __magic_name__ = None							,    ):
						lowerCamelCase__ :      List[str]								=		super(__magic_name__							,    self				).generate_dummy_inputs(
						    __magic_name__							,    batch_size=__magic_name__							,    seq_length=__magic_name__							,    is_pair=__magic_name__							,    framework=__magic_name__				)

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

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

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

						return ordered_inputs





			@property
			def        _snake_case    (self				):
						return 13








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


_lowercase     							=						logging.get_logger(__name__)
class 				__A					( A_      ):
			UpperCamelCase					:Union[str, Any]     =			['''pixel_values''']





			def __init__(self							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = PIL.Image.BICUBIC							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = 1 / 255							,    __magic_name__ = True							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = None							,    **__magic_name__							,    ):
						super().__init__(**__magic_name__				)
						lowerCamelCase__ :      str								=		size if size is not None else {"""height""": 256, """width""": 256}
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__				)
						lowerCamelCase__ :      Any								=		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

						lowerCamelCase__ :      str								=		do_resize
						lowerCamelCase__ :      Any								=		size
						lowerCamelCase__ :      Tuple								=		resample
						lowerCamelCase__ :      Union[str, Any]								=		do_center_crop
						lowerCamelCase__ :      List[str]								=		crop_size
						lowerCamelCase__ :      int								=		do_rescale
						lowerCamelCase__ :      Any								=		rescale_factor
						lowerCamelCase__ :      Dict								=		do_normalize
						lowerCamelCase__ :      Dict								=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCamelCase__ :      int								=		image_std if image_std is not None else IMAGENET_STANDARD_STD





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = PIL.Image.BICUBIC							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Optional[int]								=		get_size_dict(__magic_name__				)
						if "height" not in size or "width" not in size:
									raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
						return resize(
						    __magic_name__							,    size=(size["""height"""], size["""width"""])							,    resample=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		get_size_dict(__magic_name__				)
						if "height" not in size or "width" not in size:
									raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
						return center_crop(__magic_name__							,    size=(size["""height"""], size["""width"""])							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return rescale(__magic_name__							,    scale=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    **__magic_name__							,    ):
						return normalize(__magic_name__							,    mean=__magic_name__							,    std=__magic_name__							,    data_format=__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__=None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = ChannelDimension.FIRST							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		do_resize if do_resize is not None else self.do_resize
						lowerCamelCase__ :      str								=		resample if resample is not None else self.resample
						lowerCamelCase__ :      str								=		do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCamelCase__ :      Optional[int]								=		do_rescale if do_rescale is not None else self.do_rescale
						lowerCamelCase__ :      str								=		rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCamelCase__ :      Union[str, Any]								=		do_normalize if do_normalize is not None else self.do_normalize
						lowerCamelCase__ :      Tuple								=		image_mean if image_mean is not None else self.image_mean
						lowerCamelCase__ :      List[Any]								=		image_std if image_std is not None else self.image_std

						lowerCamelCase__ :      str								=		size if size is not None else self.size
						lowerCamelCase__ :      List[str]								=		get_size_dict(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		crop_size if crop_size is not None else self.crop_size
						lowerCamelCase__ :      Optional[Any]								=		get_size_dict(__magic_name__							,    param_name="""crop_size"""				)

						lowerCamelCase__ :      Optional[int]								=		make_list_of_images(__magic_name__				)

						if not valid_images(__magic_name__				):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""				)

						if do_resize and size is None or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize is True."""				)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""				)

						# All transformations expect numpy arrays.
						lowerCamelCase__ :      Optional[int]								=		[to_numpy_array(__magic_name__				) for image in images]

						if do_resize:
									lowerCamelCase__ :      int								=		[self.resize(image=__magic_name__							,    size=__magic_name__							,    resample=__magic_name__				) for image in images]

						if do_center_crop:
									lowerCamelCase__ :      List[Any]								=		[self.center_crop(image=__magic_name__							,    size=__magic_name__				) for image in images]

						if do_rescale:
									lowerCamelCase__ :      Dict								=		[self.rescale(image=__magic_name__							,    scale=__magic_name__				) for image in images]

						if do_normalize:
									lowerCamelCase__ :      Optional[Any]								=		[self.normalize(image=__magic_name__							,    mean=__magic_name__							,    std=__magic_name__				) for image in images]

						lowerCamelCase__ :      List[str]								=		[to_channel_dimension_format(__magic_name__							,    __magic_name__				) for image in images]

						lowerCamelCase__ :      Dict								=		{"""pixel_values""": images}
						return BatchFeature(data=__magic_name__							,    tensor_type=__magic_name__				)



