
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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowercase  :					Union[str, Any]						    =							logging.get_logger(__name__)


if is_vision_available():
  import PIL

class __snake_case		(      lowerCAmelCase     ):
    _a		:						Optional[Any]=				["pixel_values"]
    def __init__(  self		,snake_case = True		,snake_case = None		,snake_case = PILImageResampling.BICUBIC		,snake_case = True		,snake_case = None		,snake_case = True		,snake_case = 1 / 255		,snake_case = True		,snake_case = None		,snake_case = None		,snake_case = True		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case      )
           lowercase							:     List[str]       	=      size if size is not None else {"""shortest_edge""": 224}
           lowercase							:     Optional[int]       	=      get_size_dict(snake_case		,default_to_square=snake_case      )
           lowercase							:     List[Any]       	=      crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           lowercase							:     Optional[Any]       	=      get_size_dict(snake_case		,default_to_square=snake_case		,param_name="""crop_size"""      )

           lowercase							:     Dict       	=      do_resize
           lowercase							:     Dict       	=      size
           lowercase							:     str       	=      resample
           lowercase							:     Tuple       	=      do_center_crop
           lowercase							:     Tuple       	=      crop_size
           lowercase							:     Union[str, Any]       	=      do_rescale
           lowercase							:     List[Any]       	=      rescale_factor
           lowercase							:     Any       	=      do_normalize
           lowercase							:     Any       	=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           lowercase							:     Dict       	=      image_std if image_std is not None else OPENAI_CLIP_STD
           lowercase							:     Dict       	=      do_convert_rgb
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case = PILImageResampling.BICUBIC		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      get_size_dict(snake_case		,default_to_square=snake_case      )
           if "shortest_edge" not in size:
                  raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"      )
           lowercase							:     Optional[Any]       	=      get_resize_output_image_size(snake_case		,size=size["""shortest_edge"""]		,default_to_square=snake_case      )
           return resize(snake_case		,size=snake_case		,resample=snake_case		,data_format=snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     Dict       	=      get_size_dict(snake_case      )
           if "height" not in size or "width" not in size:
                  raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}"      )
           return center_crop(snake_case		,size=(size["""height"""], size["""width"""])		,data_format=snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           return rescale(snake_case		,scale=snake_case		,data_format=snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           return normalize(snake_case		,mean=snake_case		,std=snake_case		,data_format=snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = ChannelDimension.FIRST		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     Dict       	=      do_resize if do_resize is not None else self.do_resize
           lowercase							:     Optional[int]       	=      size if size is not None else self.size
           lowercase							:     Dict       	=      get_size_dict(snake_case		,param_name="""size"""		,default_to_square=snake_case      )
           lowercase							:     Any       	=      resample if resample is not None else self.resample
           lowercase							:     Any       	=      do_center_crop if do_center_crop is not None else self.do_center_crop
           lowercase							:     Tuple       	=      crop_size if crop_size is not None else self.crop_size
           lowercase							:     str       	=      get_size_dict(snake_case		,param_name="""crop_size"""		,default_to_square=snake_case      )
           lowercase							:     str       	=      do_rescale if do_rescale is not None else self.do_rescale
           lowercase							:     List[str]       	=      rescale_factor if rescale_factor is not None else self.rescale_factor
           lowercase							:     str       	=      do_normalize if do_normalize is not None else self.do_normalize
           lowercase							:     Optional[Any]       	=      image_mean if image_mean is not None else self.image_mean
           lowercase							:     List[Any]       	=      image_std if image_std is not None else self.image_std
           lowercase							:     List[Any]       	=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           lowercase							:     List[Any]       	=      make_list_of_images(snake_case      )

           if not valid_images(snake_case      ):
                  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."""      )

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                  lowercase							:     Optional[int]       	=      [convert_to_rgb(snake_case      ) for image in images]

           # All transformations expect numpy arrays.
           lowercase							:     Optional[Any]       	=      [to_numpy_array(snake_case      ) for image in images]

           if do_resize:
                  lowercase							:     str       	=      [self.resize(image=snake_case		,size=snake_case		,resample=snake_case      ) for image in images]

           if do_center_crop:
                  lowercase							:     Tuple       	=      [self.center_crop(image=snake_case		,size=snake_case      ) for image in images]

           if do_rescale:
                  lowercase							:     Union[str, Any]       	=      [self.rescale(image=snake_case		,scale=snake_case      ) for image in images]

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

           lowercase							:     List[Any]       	=      [to_channel_dimension_format(snake_case		,snake_case      ) for image in images]

           lowercase							:     int       	=      {"""pixel_values""": images}
           return BatchFeature(data=snake_case		,tensor_type=snake_case      )


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  :					Optional[Any]						    =							{
    """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 __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				"gpt_neo"
    _a		:						Optional[int]=				["past_key_values"]
    _a		:						Dict=				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
    def __init__(  self		,snake_case=50257		,snake_case=2048		,snake_case=2048		,snake_case=24		,snake_case=[[["global", "local"], 12]]		,snake_case=16		,snake_case=None		,snake_case=256		,snake_case="gelu_new"		,snake_case=0.0		,snake_case=0.0		,snake_case=0.0		,snake_case=0.1		,snake_case=1e-5		,snake_case=0.02		,snake_case=True		,snake_case=50256		,snake_case=50256		,**snake_case		,):



           '''simple docstring'''







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

           lowercase							:     Optional[int]       	=      bos_token_id
           lowercase							:     int       	=      eos_token_id

           lowercase							:     Union[str, Any]       	=      attention_types
           lowercase							:     Dict       	=      self.expand_attention_types_params(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=snake_case		,eos_token_id=snake_case		,**snake_case      )
    @staticmethod
    def       _SCREAMING_SNAKE_CASE       (  snake_case      ):



           '''simple docstring'''







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







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       import torch

       lowercase							:     Tuple       	=      input.size()
       lowercase							:     int       	=      len(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      shape[dimension]

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

       lowercase							:     Union[str, Any]       	=      [slice(SCREAMING_SNAKE_CASE__				)] * rank
       lowercase							:     Optional[Any]       	=      indices
       lowercase							:     List[str]       	=      input[s]

       lowercase							:     Optional[int]       	=      list(range(0					,	rank + 1				)				)
       perm.append(perm.pop(dimension + 1				)				)

       return sliced.permute(SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       import torch

       lowercase							:     Union[str, Any]       	=      torch.arange(1					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.remainder(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      remainders == 0
       lowercase							:     Optional[int]       	=      candidates[divisor_indices]
       lowercase							:     List[Any]       	=      torch.max(SCREAMING_SNAKE_CASE__				)
       return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				)




class __snake_case		(      lowerCAmelCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
           if self.use_past:
                  self.fill_with_past_key_values_(snake_case		,direction="""inputs"""      )
                  lowercase							:     Dict       	=      {0: """batch""", 1: """past_sequence + sequence"""}
           else:
                  lowercase							:     List[str]       	=      {0: """batch""", 1: """sequence"""}

           return common_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self._config.num_heads
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Any       	=      super(snake_case		,self      ).generate_dummy_inputs(
               snake_case		,batch_size=snake_case		,seq_length=snake_case		,is_pair=snake_case		,framework=snake_case      )

           # We need to order the input in the way they appears in the forward()
           lowercase							:     List[str]       	=      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

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

           lowercase							:     Optional[Any]       	=      common_inputs["""attention_mask"""]
           if self.use_past:
                  lowercase							:     Any       	=      ordered_inputs["""attention_mask"""].dtype
                  lowercase							:     Union[str, Any]       	=      torch.cat(
                      [ordered_inputs["""attention_mask"""], torch.ones(snake_case		,snake_case		,dtype=snake_case      )]		,dim=1      )

           return ordered_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return 13
