
'''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


lowercase			:				List[str]    		=			logging.get_logger(__name__)

lowercase			:				str    		=			{
    '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				(							__snake_case						):
     __magic_name__						=		'''gpt_neo'''
     __magic_name__						=		['''past_key_values''']
     __magic_name__						=		{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





     def __init__(							self						,			SCREAMING_SNAKE_CASE=50257						,			SCREAMING_SNAKE_CASE=2048						,			SCREAMING_SNAKE_CASE=2048						,			SCREAMING_SNAKE_CASE=24						,			SCREAMING_SNAKE_CASE=[[["global", "local"], 12]]						,			SCREAMING_SNAKE_CASE=16						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=256						,			SCREAMING_SNAKE_CASE="gelu_new"						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=1e-5						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=50256						,			SCREAMING_SNAKE_CASE=50256						,			**SCREAMING_SNAKE_CASE						,			) ->				int:






         """simple docstring"""






         A       :   Union[str, Any]					=						vocab_size
         A       :   Optional[Any]					=						max_position_embeddings
         A       :   Dict					=						hidden_size
         A       :   Optional[Any]					=						num_layers
         A       :   Tuple					=						num_heads
         A       :   int					=						intermediate_size
         A       :   Optional[Any]					=						window_size
         A       :   List[Any]					=						activation_function
         A       :   Union[str, Any]					=						resid_dropout
         A       :   Any					=						embed_dropout
         A       :   List[Any]					=						attention_dropout
         A       :   str					=						classifier_dropout
         A       :   List[Any]					=						layer_norm_epsilon
         A       :   str					=						initializer_range
         A       :   List[str]					=						use_cache

         A       :   Optional[int]					=						bos_token_id
         A       :   List[Any]					=						eos_token_id

         A       :   int					=						attention_types
         A       :   int					=						self.expand_attention_types_params(SCREAMING_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=SCREAMING_SNAKE_CASE						,			eos_token_id=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   List[str]					=						[]
         for item in attention_types:
             for _ in range(item[1]	):
                 attentions.extend(item[0]	)
         return attentions







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    import torch

    A       :   Tuple					=						input.size()
    A       :   Union[str, Any]					=						len(snake_case__						)
    A       :   List[str]					=						shape[dimension]

    A       :   Union[str, Any]					=						torch.arange(0		,				snake_case__		,				snake_case__						)
    A       :   List[str]					=						torch.div(sizedim - size		,				snake_case__		,				rounding_mode='''floor'''						) + 1
    A       :   Optional[int]					=						torch.arange(snake_case__						) + low_indices[:min_length][:, None]

    A       :   str					=						[slice(snake_case__						)] * rank
    A       :   List[Any]					=						indices
    A       :   Union[str, Any]					=						input[s]

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

    return sliced.permute(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    import torch

    A       :   List[str]					=						torch.arange(1		,				snake_case__						)
    A       :   Optional[int]					=						torch.remainder(snake_case__		,				snake_case__						)
    A       :   Optional[int]					=						remainders == 0
    A       :   Optional[Any]					=						candidates[divisor_indices]
    A       :   Optional[int]					=						torch.max(snake_case__						)
    return largest_divisor, torch.div(snake_case__		,				snake_case__		,				rounding_mode='''floor'''						)







class  A				(							__snake_case						):





     @property
     def 				__lowerCAmelCase							(							self	) ->				Mapping[str, Mapping[int, str]]:






         """simple docstring"""






         A       :   Tuple					=						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
         if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE						,			direction='''inputs'''	)
             A       :   Optional[Any]					=						{0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
             A       :   Dict					=						{0: '''batch''', 1: '''sequence'''}

         return common_inputs





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return self._config.num_heads





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = -1						,			SCREAMING_SNAKE_CASE = -1						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			) ->				Mapping[str, Any]:






         """simple docstring"""






         A       :   List[str]					=						super(SCREAMING_SNAKE_CASE						,			self	).generate_dummy_inputs(
             SCREAMING_SNAKE_CASE						,			batch_size=SCREAMING_SNAKE_CASE						,			seq_length=SCREAMING_SNAKE_CASE						,			is_pair=SCREAMING_SNAKE_CASE						,			framework=SCREAMING_SNAKE_CASE	)

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

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

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

         return ordered_inputs





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return 13





"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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


if is_vision_available():
				import PIL

__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if isinstance(UpperCamelCase__				,			(list, tuple) ) and isinstance(videos[0]				,			(list, tuple) ) and is_valid_image(videos[0][0] ):
														return videos

							elif isinstance(UpperCamelCase__				,			(list, tuple) ) and is_valid_image(videos[0] ):
														return [videos]

							elif is_valid_image(UpperCamelCase__ ):
														return [[videos]]

							raise ValueError(F'''Could not make batched video from {videos}''' )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = 1 / 2_55						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'shortest_edge': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													A__		   =							do_resize
													A__		   =							size
													A__		   =							do_center_crop
													A__		   =							crop_size
													A__		   =							resample
													A__		   =							do_rescale
													A__		   =							rescale_factor
													A__		   =							offset
													A__		   =							do_normalize
													A__		   =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A__		   =							image_std if image_std is not None else IMAGENET_STANDARD_STD





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													if "shortest_edge" in size:
																				A__		   =							get_resize_output_image_size(__UpperCAmelCase						,size['shortest_edge']						,default_to_square=__UpperCAmelCase   )
													elif "height" in size and "width" in size:
																				A__		   =							(size['height'], size['width'])
													else:
																				raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''   )
													return resize(__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													if "height" not in size or "width" not in size:
																				raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''   )
													return center_crop(__UpperCAmelCase						,size=(size['height'], size['width'])						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> Optional[Any]:
													A__		   =							image.astype(np.floataa   )
													if offset:
																				A__		   =							image - (scale / 2)
													return rescale(__UpperCAmelCase						,scale=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													return normalize(__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,)    -> np.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.'   )

													if offset and not do_rescale:
																				raise ValueError('For offset, do_rescale must also be set to True.'   )

													# All transformations expect numpy arrays.
													A__		   =							to_numpy_array(__UpperCAmelCase   )

													if do_resize:
																				A__		   =							self.resize(image=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase   )

													if do_center_crop:
																				A__		   =							self.center_crop(__UpperCAmelCase						,size=__UpperCAmelCase   )

													if do_rescale:
																				A__		   =							self.rescale(image=__UpperCAmelCase						,scale=__UpperCAmelCase						,offset=__UpperCAmelCase   )

													if do_normalize:
																				A__		   =							self.normalize(image=__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase   )

													A__		   =							to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   )
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_center_crop if do_center_crop is not None else self.do_center_crop
													A__		   =							do_rescale if do_rescale is not None else self.do_rescale
													A__		   =							rescale_factor if rescale_factor is not None else self.rescale_factor
													A__		   =							offset if offset is not None else self.offset
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							image_mean if image_mean is not None else self.image_mean
													A__		   =							image_std if image_std is not None else self.image_std

													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else self.crop_size
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

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

													A__		   =							make_batched(__UpperCAmelCase   )

													A__		   =							[
													    [
													        self._preprocess_image(
													            image=__UpperCAmelCase						,do_resize=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,do_center_crop=__UpperCAmelCase						,crop_size=__UpperCAmelCase						,do_rescale=__UpperCAmelCase						,rescale_factor=__UpperCAmelCase						,offset=__UpperCAmelCase						,do_normalize=__UpperCAmelCase						,image_mean=__UpperCAmelCase						,image_std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,)
													        for img in video
													    ]
													    for video in videos
													]

													A__		   =							{'pixel_values': videos}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





