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, 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, is_vision_available, logging


if is_vision_available():
     import PIL


UpperCAmelCase							=       logging.get_logger(__name__)



class 					snake_case__       (				UpperCamelCase_							):
   _SCREAMING_SNAKE_CASE	: Any						    =      ["pixel_values"]



   def __init__(				self   :   List[Any]			,	A__   :   int = True			,	A__   :   Dict = None			,	A__   :   Union[str, Any] = PIL.Image.NEAREST			,	A__   :   Optional[int] = False			,	A__   :   List[Any] = None			,	A__   :   int = 1 / 2_55			,	A__   :   List[str] = False			,	A__   :   Dict = True			,	A__   :   Any = True			,	A__   :   str = None			,	A__   :   Tuple = None			,	A__   :   Optional[Any] = True			,	**A__   :   Optional[int]			,	)							->     None:




         '''simple docstring'''




         super().__init__(**__UpperCamelCase   )
         snake_case_     :			str       =       size if size is not None else {'''height''': 3_46, '''width''': 3_46}
         snake_case_     :			List[Any]       =       get_size_dict(__UpperCamelCase   )
         snake_case_     :			Dict       =       crop_size if crop_size is not None else {'''height''': 2_89, '''width''': 2_89}
         snake_case_     :			Optional[int]       =       get_size_dict(__UpperCamelCase			,	param_name="crop_size"   )

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



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   Union[str, Any]			,	A__   :   Tuple			,	A__   :   Dict = PIL.Image.NEAREST			,	A__   :   Optional[Any] = None			,	**A__   :   str			,	)							->     np.ndarray:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       get_size_dict(__UpperCamelCase   )
         if "height" not in size or "width" not in size:
               raise ValueError(f"The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"   )
         return resize(
             __UpperCamelCase			,	size=(size["height"], size["width"])			,	resample=__UpperCamelCase			,	data_format=__UpperCamelCase			,	**__UpperCamelCase   )



   def 					UpperCAmelCase__       (				self   :   Tuple			,	A__   :   Any			,	A__   :   Any			,	A__   :   List[str] = None			,	**A__   :   Union[str, Any]			,	)							->     np.ndarray:




         '''simple docstring'''




         snake_case_     :			Tuple       =       get_size_dict(__UpperCamelCase   )
         if "height" not in size or "width" not in size:
               raise ValueError(f"The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"   )
         return center_crop(__UpperCamelCase			,	size=(size["height"], size["width"])			,	data_format=__UpperCamelCase			,	**__UpperCamelCase   )



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   Union[str, Any]			,	A__   :   Optional[Any]			,	A__   :   List[str] = True			,	A__   :   Tuple = None			,	**A__   :   Any			,	)							->     List[str]:




         '''simple docstring'''




         if offset:
               snake_case_     :			str       =       (image - 127.5) * scale
               if data_format is not None:
                     snake_case_     :			Optional[Any]       =       to_channel_dimension_format(__UpperCamelCase			,	__UpperCamelCase   )
               snake_case_     :			Any       =       rescaled_image.astype(np.floataa   )
         else:
               snake_case_     :			List[Any]       =       rescale(__UpperCamelCase			,	scale=__UpperCamelCase			,	data_format=__UpperCamelCase			,	**__UpperCamelCase   )
         return rescaled_image



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   List[Any]			,	A__   :   int			,	A__   :   Union[str, Any]			,	A__   :   Optional[Any] = None			,	**A__   :   Any			,	)							->     np.ndarray:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   Optional[int]			,	A__   :   Union[str, Any] = None			,	A__   :   Optional[Any] = None			,	A__   :   Optional[Any]=None			,	A__   :   Any = None			,	A__   :   List[str] = None			,	A__   :   List[Any] = None			,	A__   :   Dict = None			,	A__   :   str = None			,	A__   :   Optional[Any] = None			,	A__   :   List[str] = None			,	A__   :   Any = None			,	A__   :   str = None			,	A__   :   Optional[Any] = None			,	A__   :   int = ChannelDimension.FIRST			,	**A__   :   Dict			,	)							->     PIL.Image.Image:




         '''simple docstring'''




         snake_case_     :			List[str]       =       do_resize if do_resize is not None else self.do_resize
         snake_case_     :			List[Any]       =       resample if resample is not None else self.resample
         snake_case_     :			int       =       do_center_crop if do_center_crop is not None else self.do_center_crop
         snake_case_     :			int       =       do_rescale if do_rescale is not None else self.do_rescale
         snake_case_     :			Optional[Any]       =       rescale_factor if rescale_factor is not None else self.rescale_factor
         snake_case_     :			Optional[Any]       =       rescale_offset if rescale_offset is not None else self.rescale_offset
         snake_case_     :			Optional[int]       =       do_normalize if do_normalize is not None else self.do_normalize
         snake_case_     :			int       =       image_mean if image_mean is not None else self.image_mean
         snake_case_     :			Optional[Any]       =       image_std if image_std is not None else self.image_std
         snake_case_     :			Dict       =       include_top if include_top is not None else self.include_top

         snake_case_     :			List[Any]       =       size if size is not None else self.size
         snake_case_     :			Union[str, Any]       =       get_size_dict(__UpperCamelCase   )
         snake_case_     :			Optional[Any]       =       crop_size if crop_size is not None else self.crop_size
         snake_case_     :			Union[str, Any]       =       get_size_dict(__UpperCamelCase			,	param_name="crop_size"   )

         snake_case_     :			Tuple       =       make_list_of_images(__UpperCamelCase   )

         if not valid_images(__UpperCamelCase   ):
               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 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."   )

         # All transformations expect numpy arrays.
         snake_case_     :			int       =       [to_numpy_array(__UpperCamelCase   ) for image in images]

         if do_resize:
               snake_case_     :			Dict       =       [self.resize(image=__UpperCamelCase			,	size=__UpperCamelCase			,	resample=__UpperCamelCase   ) for image in images]

         if do_center_crop:
               snake_case_     :			int       =       [self.center_crop(image=__UpperCamelCase			,	size=__UpperCamelCase   ) for image in images]

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

         if do_normalize:
               snake_case_     :			Union[str, Any]       =       [self.normalize(image=__UpperCamelCase			,	mean=__UpperCamelCase			,	std=__UpperCamelCase   ) for image in images]

         if include_top:
               snake_case_     :			Tuple       =       [self.normalize(image=__UpperCamelCase			,	mean=[0, 0, 0]			,	std=__UpperCamelCase   ) for image in images]

         snake_case_     :			Optional[int]       =       [to_channel_dimension_format(__UpperCamelCase			,	__UpperCamelCase   ) for image in images]

         snake_case_     :			Optional[int]       =       {'''pixel_values''': images}
         return BatchFeature(data=__UpperCamelCase			,	tensor_type=__UpperCamelCase   )




import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
     import torch

     from transformers import GPTaLMHeadModel



@require_torch
@require_sentencepiece
@require_tokenizers
class 					snake_case__       (				unittest.TestCase							):



   @slow
   @unittest.skip("Model is not available."   )
   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			str       =       "nvidia/megatron-gpt2-345m/"
         if "MYDIR" in os.environ:
               snake_case_     :			int       =       os.path.join(os.environ["MYDIR"]			,	A__   )
         snake_case_     :			str       =       GPTaLMHeadModel.from_pretrained(A__   )
         model.to(A__   )
         model.half()

         snake_case_     :			Optional[int]       =       torch.tensor(
             [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]			,	device=A__			,	dtype=torch.long			,	)

         with torch.no_grad():
               snake_case_     :			Union[str, Any]       =       model(A__   ).logits

         snake_case_     :			List[str]       =       torch.Size((1, 9, 5_02_57)   )
         self.assertEqual(output.shape			,	A__   )

         snake_case_     :			Dict       =       torch.tensor(
             [
                 4.9414,
                 -0.2920,
                 -1.2148,
                 -4.0273,
                 -0.5161,
                 -5.2109,
                 -1.2412,
                 -1.8301,
                 -1.7734,
                 -4.7148,
                 -0.2317,
                 -1.0811,
                 -2.1777,
                 0.4141,
                 -3.7969,
                 -4.0586,
                 -2.5332,
                 -3.3809,
                 4.3867,
             ]			,	device=A__			,	dtype=torch.half			,	)

         for i in range(19   ):
               snake_case_  ,snake_case_     :			Dict       =       8 * i // 17, 27_92 * i  # along the diagonal
               snake_case_  ,snake_case_     :			Any       =       output[0, r, c], expected_diag[i]
               snake_case_     :			Dict       =       f"row={r} col={c} computed={computed} expected={expected}"
               self.assertAlmostEqual(A__			,	A__			,	delta=1E-4			,	msg=A__   )



