

"""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

__UpperCamelCase  :      Any							      =		logging.get_logger(__name__)

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	if isinstance(A_						,     (list, tuple) ) and isinstance(videos[0]						,     (list, tuple) ) and is_valid_image(videos[0][0] ):
		return videos

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

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

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







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       ["pixel_values"]







					def __init__(       self			:				Tuple   ,lowercase_			:				bool = True   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = PILImageResampling.BILINEAR   ,lowercase_			:				bool = True   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				bool = True   ,lowercase_			:				Union[int, float] = 1 / 2_5_5   ,lowercase_			:				bool = True   ,lowercase_			:				bool = True   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,**lowercase_			:				str   ,):
						super().__init__(**lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	size if size is not None else {'''shortest_edge''': 2_5_6}
						lowerCAmelCase__	:						Any  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Dict  				=	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__	:						List[str]  				=	get_size_dict(lowercase_   ,param_name='''crop_size'''		)

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







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				PILImageResampling = PILImageResampling.BILINEAR   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Optional[Any]   ,):
						lowerCAmelCase__	:						Any  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						if "shortest_edge" in size:
							lowerCAmelCase__	:						Any  				=	get_resize_output_image_size(lowercase_   ,size['''shortest_edge''']   ,default_to_square=lowercase_		)
						elif "height" in size and "width" in size:
							lowerCAmelCase__	:						Dict  				=	(size['''height'''], size['''width'''])
						else:
							raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'		)
						return resize(lowercase_   ,size=lowercase_   ,resample=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Dict   ,):
						lowerCAmelCase__	:						List[Any]  				=	get_size_dict(lowercase_		)
						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(lowercase_   ,size=(size['''height'''], size['''width'''])   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[int, float]   ,lowercase_			:				bool = True   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Dict   ,):
						lowerCAmelCase__	:						int  				=	image.astype(np.floataa		)
						if offset:
							lowerCAmelCase__	:						Tuple  				=	image - (scale / 2)
						return rescale(lowercase_   ,scale=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Dict   ,):
						return normalize(lowercase_   ,mean=lowercase_   ,std=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				ImageInput   ,lowercase_			:				bool = None   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = None   ,lowercase_			:				bool = None   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				bool = None   ,lowercase_			:				float = None   ,lowercase_			:				bool = None   ,lowercase_			:				bool = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[ChannelDimension] = ChannelDimension.FIRST   ,):
						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.
						lowerCAmelCase__	:						List[Any]  				=	to_numpy_array(lowercase_		)

						if do_resize:
							lowerCAmelCase__	:						Optional[int]  				=	self.resize(image=lowercase_   ,size=lowercase_   ,resample=lowercase_		)

						if do_center_crop:
							lowerCAmelCase__	:						Union[str, Any]  				=	self.center_crop(lowercase_   ,size=lowercase_		)

						if do_rescale:
							lowerCAmelCase__	:						Any  				=	self.rescale(image=lowercase_   ,scale=lowercase_   ,offset=lowercase_		)

						if do_normalize:
							lowerCAmelCase__	:						List[str]  				=	self.normalize(image=lowercase_   ,mean=lowercase_   ,std=lowercase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	to_channel_dimension_format(lowercase_   ,lowercase_		)
						return image







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				ImageInput   ,lowercase_			:				bool = None   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = None   ,lowercase_			:				bool = None   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				bool = None   ,lowercase_			:				float = None   ,lowercase_			:				bool = None   ,lowercase_			:				bool = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[str, TensorType]] = None   ,lowercase_			:				ChannelDimension = ChannelDimension.FIRST   ,**lowercase_			:				Optional[int]   ,):
						lowerCAmelCase__	:						int  				=	do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__	:						str  				=	resample if resample is not None else self.resample
						lowerCAmelCase__	:						Optional[int]  				=	do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__	:						Optional[Any]  				=	do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__	:						List[str]  				=	rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__	:						str  				=	offset if offset is not None else self.offset
						lowerCAmelCase__	:						Tuple  				=	do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__	:						int  				=	image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__	:						Optional[Any]  				=	image_std if image_std is not None else self.image_std

						lowerCAmelCase__	:						Optional[int]  				=	size if size is not None else self.size
						lowerCAmelCase__	:						Optional[int]  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Any  				=	crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__	:						Dict  				=	get_size_dict(lowercase_   ,param_name='''crop_size'''		)

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

						lowerCAmelCase__	:						Tuple  				=	make_batched(lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	[
						    [
						        self._preprocess_image(
						            image=lowercase_   ,do_resize=lowercase_   ,size=lowercase_   ,resample=lowercase_   ,do_center_crop=lowercase_   ,crop_size=lowercase_   ,do_rescale=lowercase_   ,rescale_factor=lowercase_   ,offset=lowercase_   ,do_normalize=lowercase_   ,image_mean=lowercase_   ,image_std=lowercase_   ,data_format=lowercase_   ,)
						        for img in video
						    ]
						    for video in videos
						]

						lowerCAmelCase__	:						Dict  				=	{'''pixel_values''': videos}
						return BatchFeature(data=lowercase_   ,tensor_type=lowercase_		)









'''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_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "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			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'gptj'
 __lowerCamelCase    :       List[str]          =       {
     'max_position_embeddings': 'n_positions',
     'hidden_size': 'n_embd',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }



 def __init__(self ,   A=50_400 ,   A=2_048 ,   A=4_096 ,   A=28 ,   A=16 ,   A=64 ,   A=None ,   A="gelu_new" ,   A=0.0 ,   A=0.0 ,   A=0.0 ,   A=1E-5 ,   A=0.02 ,   A=True ,   A=50_256 ,   A=50_256 ,   A=False ,   **A ,   )    ->     Tuple:


      """simple docstring"""



      _a      =  vocab_size
      _a      =  n_positions
      _a      =  n_embd
      _a      =  n_layer
      _a      =  n_head
      _a      =  n_inner
      _a      =  rotary_dim
      _a      =  activation_function
      _a      =  resid_pdrop
      _a      =  embd_pdrop
      _a      =  attn_pdrop
      _a      =  layer_norm_epsilon
      _a      =  initializer_range
      _a      =  use_cache

      _a      =  bos_token_id
      _a      =  eos_token_id

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






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A ,   A = "default" ,   A = None ,   A = False ,   )    ->     List[str]:


      """simple docstring"""



      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?
           _a      =  0



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


      """simple docstring"""



      _a      =  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
      if self.use_past:
           self.fill_with_past_key_values_(A ,   direction='''inputs'''       )
           _a      =  {0: '''batch''', 1: '''past_sequence + sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}

      return common_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_layer



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_head



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


      """simple docstring"""



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

      _a      =  common_inputs['''attention_mask''']
      if self.use_past:
           _a      =  ordered_inputs['''attention_mask'''].dtype
           _a      =  torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(A ,   A ,   dtype=A       )] ,   dim=1       )

      return ordered_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return 13



