


"""simple docstring"""

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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase__					=logging.get_logger(__name__)





class  lowerCamelCase__    (		_a     ):
      a			:  str           =		["""pixel_values"""]
      def __init__(		self						:						Union[str, Any]   ,							A_						:						bool = True   ,							A_						:						Optional[Dict[str, int]] = None   ,							A_						:						PILImageResampling = PILImageResampling.BICUBIC   ,							A_						:						bool = True   ,							A_						:						bool = True   ,							A_						:						Union[int, float] = 1 / 2_5_5   ,							A_						:						Dict[str, int] = None   ,							A_						:						bool = True   ,							A_						:						Optional[Union[float, List[float]]] = None   ,							A_						:						Optional[Union[float, List[float]]] = None   ,							**A_						:						int   ,							):






       '''simple docstring'''


       super().__init__(**A_				)
       __lowercase						=						size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
       __lowercase						=						get_size_dict(A_				)
       __lowercase						=						crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
       __lowercase						=						get_size_dict(A_   ,							default_to_square=A_   ,							param_name="""crop_size"""				)

       __lowercase						=						do_resize
       __lowercase						=						do_rescale
       __lowercase						=						do_normalize
       __lowercase						=						do_center_crop
       __lowercase						=						crop_size
       __lowercase						=						size
       __lowercase						=						resample
       __lowercase						=						rescale_factor
       __lowercase						=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       __lowercase						=						image_std if image_std is not None else IMAGENET_DEFAULT_STD
      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						np.ndarray   ,							A_						:						Dict[str, int]   ,							A_						:						PILImageResampling = PILImageResampling.BILINEAR   ,							A_						:						Optional[Union[str, ChannelDimension]] = None   ,							**A_						:						str   ,							):






       '''simple docstring'''


       __lowercase						=						get_size_dict(A_				)

       if "shortest_edge" in size:
        __lowercase						=						get_resize_output_image_size(A_   ,							size=size["""shortest_edge"""]   ,							default_to_square=A_				)
        # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
       elif "height" in size and "width" in size:
        __lowercase						=						(size["""height"""], size["""width"""])
       else:
        raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''				)
       return resize(A_   ,							size=A_   ,							resample=A_   ,							data_format=A_   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						np.ndarray   ,							A_						:						Dict[str, int]   ,							A_						:						Optional[Union[str, ChannelDimension]] = None   ,							**A_						:						Any   ,							):






       '''simple docstring'''


       __lowercase						=						get_size_dict(A_				)
       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(A_   ,							size=(size["""height"""], size["""width"""])   ,							data_format=A_   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						str   ,							A_						:						np.ndarray   ,							A_						:						float   ,							A_						:						Optional[Union[str, ChannelDimension]] = None   ,							**A_						:						Dict				):






       '''simple docstring'''


       return rescale(A_   ,							scale=A_   ,							data_format=A_   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						np.ndarray   ,							A_						:						Union[float, List[float]]   ,							A_						:						Union[float, List[float]]   ,							A_						:						Optional[Union[str, ChannelDimension]] = None   ,							**A_						:						str   ,							):






       '''simple docstring'''


       return normalize(A_   ,							mean=A_   ,							std=A_   ,							data_format=A_   ,							**A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						str   ,							A_						:						ImageInput   ,							A_						:						Optional[bool] = None   ,							A_						:						Dict[str, int] = None   ,							A_						:						PILImageResampling = None   ,							A_						:						bool = None   ,							A_						:						int = None   ,							A_						:						Optional[bool] = None   ,							A_						:						Optional[float] = None   ,							A_						:						Optional[bool] = None   ,							A_						:						Optional[Union[float, List[float]]] = None   ,							A_						:						Optional[Union[float, List[float]]] = None   ,							A_						:						Optional[Union[str, TensorType]] = None   ,							A_						:						Union[str, ChannelDimension] = ChannelDimension.FIRST   ,							**A_						:						Dict   ,							):






       '''simple docstring'''


       __lowercase						=						do_resize if do_resize is not None else self.do_resize
       __lowercase						=						do_rescale if do_rescale is not None else self.do_rescale
       __lowercase						=						do_normalize if do_normalize is not None else self.do_normalize
       __lowercase						=						do_center_crop if do_center_crop is not None else self.do_center_crop
       __lowercase						=						crop_size if crop_size is not None else self.crop_size
       __lowercase						=						get_size_dict(A_   ,							param_name="""crop_size"""   ,							default_to_square=A_				)
       __lowercase						=						resample if resample is not None else self.resample
       __lowercase						=						rescale_factor if rescale_factor is not None else self.rescale_factor
       __lowercase						=						image_mean if image_mean is not None else self.image_mean
       __lowercase						=						image_std if image_std is not None else self.image_std

       __lowercase						=						size if size is not None else self.size
       __lowercase						=						get_size_dict(A_				)

       if not is_batched(A_				):
        __lowercase						=						[images]

       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."""				)

       # All transformations expect numpy arrays.
       __lowercase						=						[to_numpy_array(A_				) for image in images]

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

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

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

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

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

       __lowercase						=						{"""pixel_values""": images}
       return BatchFeature(data=A_   ,							tensor_type=A_				)




"""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


UpperCAmelCase__					=logging.get_logger(__name__)

UpperCAmelCase__					={
    "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  lowerCamelCase__    (		_a     ):
      a			:  Tuple           =		"""gptj"""
      a			:  Union[str, Any]           =		{
          """max_position_embeddings""": """n_positions""",
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }
      def __init__(		self						:						int   ,							A_						:						Optional[int]=5_0_4_0_0   ,							A_						:						Optional[Any]=2_0_4_8   ,							A_						:						Optional[Any]=4_0_9_6   ,							A_						:						Any=2_8   ,							A_						:						Union[str, Any]=1_6   ,							A_						:						int=6_4   ,							A_						:						int=None   ,							A_						:						str="gelu_new"   ,							A_						:						str=0.0   ,							A_						:						Optional[Any]=0.0   ,							A_						:						Dict=0.0   ,							A_						:						Dict=1e-5   ,							A_						:						Optional[int]=0.02   ,							A_						:						List[str]=True   ,							A_						:						List[Any]=5_0_2_5_6   ,							A_						:						Optional[int]=5_0_2_5_6   ,							A_						:						List[Any]=False   ,							**A_						:						int   ,							):






       '''simple docstring'''


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

       __lowercase						=						bos_token_id
       __lowercase						=						eos_token_id

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






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						int   ,							A_						:						PretrainedConfig   ,							A_						:						str = "default"   ,							A_						:						List[PatchingSpec] = None   ,							A_						:						bool = False   ,							):






       '''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?
        __lowercase						=						0
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}				)
       if self.use_past:
        self.fill_with_past_key_values_(A_   ,							direction="""inputs"""				)
        __lowercase						=						{0: """batch""", 1: """past_sequence + sequence"""}
       else:
        __lowercase						=						{0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       return self._config.n_layer
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       return self._config.n_head
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						PreTrainedTokenizer   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional[TensorType] = None   ,							):






       '''simple docstring'''


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

       __lowercase						=						common_inputs["""attention_mask"""]
       if self.use_past:
        __lowercase						=						ordered_inputs["""attention_mask"""].dtype
        __lowercase						=						torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(A_   ,							A_   ,							dtype=A_				)]   ,							dim=1				)

       return ordered_inputs






      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return 1_3
