


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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      ["""pixel_values"""]


     def __init__(	self  ,       __A = True  ,       __A = None  ,       __A = PILImageResampling.BILINEAR  ,       __A = True  ,       __A = None  ,       __A = True  ,       __A = 1 / 255  ,       __A = True  ,       __A = None  ,       __A = None  ,       **__A  ,       ):
        super().__init__(**__A    )
        __a					=  size if size is not None else {"""shortest_edge""": 256}
        __a					=  get_size_dict(__A  ,       default_to_square=__A    )
        __a					=  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        __a					=  get_size_dict(__A    )
        __a					=  do_resize
        __a					=  size
        __a					=  resample
        __a					=  do_center_crop
        __a					=  crop_size
        __a					=  do_rescale
        __a					=  rescale_factor
        __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  ,       __A  ,       __A  ,       __A = PILImageResampling.BICUBIC  ,       __A = None  ,       **__A  ,       ):
        __a					=  get_size_dict(__A  ,       default_to_square=__A    )
        if "shortest_edge" not in size:
           raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
        __a					=  get_resize_output_image_size(__A  ,       size=size["""shortest_edge"""]  ,       default_to_square=__A    )
        return resize(__A  ,       size=__A  ,       resample=__A  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = None  ,       **__A  ,       ):
        __a					=  get_size_dict(__A    )
        return center_crop(__A  ,       size=(size["""height"""], size["""width"""])  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = None  ,       **__A    ):
        return rescale(__A  ,       scale=__A  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A = None  ,       **__A  ,       ):
        return normalize(__A  ,       mean=__A  ,       std=__A  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = ChannelDimension.FIRST  ,       **__A  ,       ):
        __a					=  do_resize if do_resize is not None else self.do_resize
        __a					=  size if size is not None else self.size
        __a					=  get_size_dict(__A  ,       default_to_square=__A    )
        __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					=  crop_size if crop_size is not None else self.crop_size
        __a					=  get_size_dict(__A    )
        __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					=  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					=  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.
        __a					=  [to_numpy_array(__A    ) for image in images]

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

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

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

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

        __a					=  [to_channel_dimension_format(__A  ,       __A    ) for image in images]

        __a					=  {"""pixel_values""": images}
        return BatchFeature(data=__A  ,       tensor_type=__A    )









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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, TensorType

__a							     =					logging.get_logger(__name__)

__a							     =					{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''imagegpt'''
    lowerCAmelCase         = ['''past_key_values''']
    lowerCAmelCase         = {
        '''hidden_size''': '''n_embd''',
        '''max_position_embeddings''': '''n_positions''',
        '''num_attention_heads''': '''n_head''',
        '''num_hidden_layers''': '''n_layer''',
    }





    def __init__( self  ,_SCREAMING_SNAKE_CASE=512 + 1  ,_SCREAMING_SNAKE_CASE=32 * 32  ,_SCREAMING_SNAKE_CASE=512  ,_SCREAMING_SNAKE_CASE=24  ,_SCREAMING_SNAKE_CASE=8  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE="quick_gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=1e-5  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=False  ,**_SCREAMING_SNAKE_CASE  ,)					->						Optional[int]:
         UpperCAmelCase_     :				Optional[int]  =     vocab_size
         UpperCAmelCase_     :				Union[str, Any]  =     n_positions
         UpperCAmelCase_     :				Union[str, Any]  =     n_embd
         UpperCAmelCase_     :				Any  =     n_layer
         UpperCAmelCase_     :				Optional[Any]  =     n_head
         UpperCAmelCase_     :				Union[str, Any]  =     n_inner
         UpperCAmelCase_     :				List[Any]  =     activation_function
         UpperCAmelCase_     :				List[str]  =     resid_pdrop
         UpperCAmelCase_     :				str  =     embd_pdrop
         UpperCAmelCase_     :				Optional[Any]  =     attn_pdrop
         UpperCAmelCase_     :				Dict  =     layer_norm_epsilon
         UpperCAmelCase_     :				Union[str, Any]  =     initializer_range
         UpperCAmelCase_     :				Dict  =     scale_attn_weights
         UpperCAmelCase_     :				Any  =     use_cache
         UpperCAmelCase_     :				List[str]  =     scale_attn_by_inverse_layer_idx
         UpperCAmelCase_     :				Tuple  =     reorder_and_upcast_attn
         UpperCAmelCase_     :				int  =     tie_word_embeddings

         super().__init__(tie_word_embeddings=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )


class     __a(	_a  ):






    """simple docstring"""





    @property
    def 				a__ ( self    )					->						Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
             ]    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = 1  ,_SCREAMING_SNAKE_CASE = -1  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = 3  ,_SCREAMING_SNAKE_CASE = 32  ,_SCREAMING_SNAKE_CASE = 32  ,)					->						Mapping[str, Any]:
         UpperCAmelCase_     :				Any  =     self._generate_dummy_images(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[int]  =     dict(preprocessor(images=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE    )    )

         return inputs