"""simple docstring"""



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__lowercase		:		Tuple														=							logging.get_logger(__name__)

__lowercase		:		Any														=							{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					int							=					'''gptj'''
    UpperCamelCase_	:					int							=					{
        '''max_position_embeddings''': '''n_positions''',
        '''hidden_size''': '''n_embd''',
        '''num_attention_heads''': '''n_head''',
        '''num_hidden_layers''': '''n_layer''',
    }
    def __init__(		self							:     Any	,      A_							:     Optional[int]=50_400	,      A_							:     Tuple=2_048	,      A_							:     Dict=4_096	,      A_							:     Dict=28	,      A_							:     Tuple=16	,      A_							:     int=64	,      A_							:     Any=None	,      A_							:     Union[str, Any]="gelu_new"	,      A_							:     List[Any]=0.0	,      A_							:     Dict=0.0	,      A_							:     int=0.0	,      A_							:     str=1E-5	,      A_							:     List[str]=0.02	,      A_							:     Any=True	,      A_							:     List[str]=50_256	,      A_							:     Union[str, Any]=50_256	,      A_							:     Any=False	,      **A_							:     List[str]	,      )    ->       Dict:
         __snake_case				=							vocab_size
         __snake_case				=							n_positions
         __snake_case				=							n_embd
         __snake_case				=							n_layer
         __snake_case				=							n_head
         __snake_case				=							n_inner
         __snake_case				=							rotary_dim
         __snake_case				=							activation_function
         __snake_case				=							resid_pdrop
         __snake_case				=							embd_pdrop
         __snake_case				=							attn_pdrop
         __snake_case				=							layer_norm_epsilon
         __snake_case				=							initializer_range
         __snake_case				=							use_cache

         __snake_case				=							bos_token_id
         __snake_case				=							eos_token_id

         super().__init__(
             bos_token_id=A_	,      eos_token_id=A_	,      tie_word_embeddings=A_	,      **A_    )






class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    def __init__(		self							:     Any	,      A_							:     PretrainedConfig	,      A_							:     str = "default"	,      A_							:     List[PatchingSpec] = None	,      A_							:     bool = False	,      )    ->       str:
         super().__init__(A_	,      task=A_	,      patching_specs=A_	,      use_past=A_    )
         if not getattr(self._config	,      '''pad_token_id'''	,      A_    ):
              # TODO: how to do that better?
              __snake_case				=							0
    @property
    def  lowercase					(		self							:     Any    )    ->       Mapping[str, Mapping[int, str]]:
         __snake_case				=							OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}    )
         if self.use_past:
              self.fill_with_past_key_values_(A_	,      direction='''inputs'''    )
              __snake_case				=							{0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
              __snake_case				=							{0: '''batch''', 1: '''sequence'''}

         return common_inputs
    @property
    def  lowercase					(		self							:     Dict    )    ->       int:
         return self._config.n_layer
    @property
    def  lowercase					(		self							:     Any    )    ->       int:
         return self._config.n_head
    def  lowercase					(		self							:     Any	,      A_							:     PreTrainedTokenizer	,      A_							:     int = -1	,      A_							:     int = -1	,      A_							:     bool = False	,      A_							:     Optional[TensorType] = None	,      )    ->       Mapping[str, Any]:
         __snake_case				=							super(A_	,      self    ).generate_dummy_inputs(
             A_	,      batch_size=A_	,      seq_length=A_	,      is_pair=A_	,      framework=A_    )

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

         return ordered_inputs







    @property
    def  lowercase					(		self							:     List[Any]    )    ->       int:
         return 13
"""simple docstring"""



from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase		:		str														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					['''pixel_values''']
    def __init__(		self							:     Optional[int]	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     bool = True	,      A_							:     Union[int, float] = 1 / 255	,      A_							:     bool = True	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD	,      **A_							:     Dict	,      )    ->       None:
         super().__init__(**A_    )
         __snake_case				=							size if size is not None else {'''shortest_edge''': 224}
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __snake_case				=							do_resize
         __snake_case				=							size
         __snake_case				=							resample
         __snake_case				=							do_center_crop
         __snake_case				=							crop_size
         __snake_case				=							do_rescale
         __snake_case				=							rescale_factor
         __snake_case				=							do_normalize
         __snake_case				=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         __snake_case				=							image_std if image_std is not None else IMAGENET_DEFAULT_STD
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     int	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
              __snake_case				=							int((256 / 224) * size['''shortest_edge''']    )
              __snake_case				=							get_resize_output_image_size(A_	,      size=A_	,      default_to_square=A_    )
              __snake_case				=							{'''height''': output_size[0], '''width''': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
              raise ValueError(
                  f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"    )
         return resize(
             A_	,      size=(size_dict['''height'''], size_dict['''width'''])	,      resample=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     List[Any]	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_    )
         if "height" not in size or "width" not in size:
              raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"    )
         return center_crop(A_	,      size=(size['''height'''], size['''width'''])	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     np.ndarray	,      A_							:     Union[int, float]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[Any]	,      )    ->       np.ndarray:
         return rescale(A_	,      scale=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Dict	,      A_							:     np.ndarray	,      A_							:     Union[float, List[float]]	,      A_							:     Union[float, List[float]]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[int]	,      )    ->       np.ndarray:
         return normalize(A_	,      mean=A_	,      std=A_	,      data_format=A_	,      **A_    )







    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     ImageInput	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     PILImageResampling = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[float] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[TensorType] = None	,      A_							:     ChannelDimension = ChannelDimension.FIRST	,      **A_							:     Any	,      )    ->       BatchFeature:
         __snake_case				=							do_resize if do_resize is not None else self.do_resize
         __snake_case				=							resample if resample is not None else self.resample
         __snake_case				=							do_center_crop if do_center_crop is not None else self.do_center_crop
         __snake_case				=							do_rescale if do_rescale is not None else self.do_rescale
         __snake_case				=							rescale_factor if rescale_factor is not None else self.rescale_factor
         __snake_case				=							do_normalize if do_normalize is not None else self.do_normalize
         __snake_case				=							image_mean if image_mean is not None else self.image_mean
         __snake_case				=							image_std if image_std is not None else self.image_std

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

         __snake_case				=							make_list_of_images(A_    )

         if not valid_images(A_    ):
              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:
              raise ValueError('''Size 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.
         __snake_case				=							[to_numpy_array(A_    ) for image in images]

         if do_resize:
              __snake_case				=							[self.resize(A_	,      A_	,      A_    ) for image in images]

         if do_center_crop:
              __snake_case				=							[self.center_crop(A_	,      A_    ) for image in images]

         if do_rescale:
              __snake_case				=							[self.rescale(A_	,      A_    ) for image in images]

         if do_normalize:
              __snake_case				=							[self.normalize(A_	,      A_	,      A_    ) for image in images]

         __snake_case				=							[to_channel_dimension_format(A_	,      A_    ) for image in images]

         __snake_case				=							{'''pixel_values''': images}
         return BatchFeature(data=A_	,      tensor_type=A_    )