






import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case								=		logging.get_logger(__name__)


snake_case								=		"""https://openaipublic.azureedge.net/jukebox/models/"""
snake_case								=		{
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  if key.endswith(".model.1.bias"				) and len(key.split("."				)				) > 10:
    SCREAMING_SNAKE_CASE       :      int							=    key.replace(".model.1.bias"						,     ".conv1d_1.bias"				)
  elif key.endswith(".model.1.weight"				) and len(key.split("."				)				) > 10:
    SCREAMING_SNAKE_CASE       :      Dict							=    key.replace(".model.1.weight"						,     ".conv1d_1.weight"				)
  elif key.endswith(".model.3.bias"				) and len(key.split("."				)				) > 10:
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    key.replace(".model.3.bias"						,     ".conv1d_2.bias"				)
  elif key.endswith(".model.3.weight"				) and len(key.split("."				)				) > 10:
    SCREAMING_SNAKE_CASE       :      Dict							=    key.replace(".model.3.weight"						,     ".conv1d_2.weight"				)

  if "conditioner_blocks.0." in key:
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    key.replace("conditioner_blocks.0"						,     "conditioner_blocks"				)

  if "prime_prior" in key:
    SCREAMING_SNAKE_CASE       :      Dict							=    key.replace("prime_prior"						,     "encoder"				)

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    key.replace(".emb."						,     "."				)

  if key.endswith("k"				):  # replace vqvae.X.k with vqvae.X.codebook
    return key.replace(".k"						,     ".codebook"				)
  if "y_emb." in key:
    return key.replace("y_emb."						,     "metadata_embedding."				)

  if "x_emb.emb." in key:
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    key.replace("0.x_emb.emb"						,     "embed_tokens"				)

  if "prime_state_ln" in key:
    return key.replace("prime_state_ln"						,     "encoder.final_layer_norm"				)
  if ".ln" in key:
    return key.replace(".ln"						,     ".layer_norm"				)
  if "_ln" in key:
    return key.replace("_ln"						,     "_layer_norm"				)

  if "prime_state_proj" in key:
    return key.replace("prime_state_proj"						,     "encoder.proj_in"				)
  if "prime_x_out" in key:
    return key.replace("prime_x_out"						,     "encoder.lm_head"				)
  if "prior.x_out" in key:
    return key.replace("x_out"						,     "fc_proj_out"				)
  if "x_emb" in key:
    return key.replace("x_emb"						,     "embed_tokens"				)

  return key


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      int							=    {}
  import re

  SCREAMING_SNAKE_CASE       :      Tuple							=    re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"				)
  SCREAMING_SNAKE_CASE       :      Dict							=    re.compile(
      R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"				)
  SCREAMING_SNAKE_CASE       :      str							=    re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"				)

  SCREAMING_SNAKE_CASE       :      Tuple							=    re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"				)
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    re.compile(
      R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"				)
  SCREAMING_SNAKE_CASE       :      List[Any]							=    re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"				)

  SCREAMING_SNAKE_CASE       :      str							=    re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"				)
  SCREAMING_SNAKE_CASE       :      Any							=    re.compile(
      R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"				)
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"				)

  for original_key, value in state_dict.items():
    # rename vqvae.encoder keys
    if re_encoder_block_conv_in.fullmatch(lowercase				):
      SCREAMING_SNAKE_CASE       :      Optional[int]							=    re_encoder_block_conv_in.match(lowercase				)
      SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    regex_match.groups()
      SCREAMING_SNAKE_CASE       :      int							=    int(groups[2]				) * 2 + int(groups[3]				)
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
      SCREAMING_SNAKE_CASE       :      Dict							=    re_encoder_block_conv_in.sub(lowercase						,     lowercase				)

    elif re_encoder_block_resnet.fullmatch(lowercase				):
      SCREAMING_SNAKE_CASE       :      int							=    re_encoder_block_resnet.match(lowercase				)
      SCREAMING_SNAKE_CASE       :      Any							=    regex_match.groups()
      SCREAMING_SNAKE_CASE       :      List[Any]							=    int(groups[2]				) * 2 + int(groups[3]				)
      SCREAMING_SNAKE_CASE       :      Any							=    {"1": 1, "3": 2}[groups[-2]]
      SCREAMING_SNAKE_CASE       :      int							=    F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
      SCREAMING_SNAKE_CASE       :      Any							=    prefix + resnet_block
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    re_encoder_block_resnet.sub(lowercase						,     lowercase				)

    elif re_encoder_block_proj_out.fullmatch(lowercase				):
      SCREAMING_SNAKE_CASE       :      Dict							=    re_encoder_block_proj_out.match(lowercase				)
      SCREAMING_SNAKE_CASE       :      List[Any]							=    regex_match.groups()
      SCREAMING_SNAKE_CASE       :      int							=    F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
      SCREAMING_SNAKE_CASE       :      List[Any]							=    re_encoder_block_proj_out.sub(lowercase						,     lowercase				)

    # rename vqvae.decoder keys
    elif re_decoder_block_conv_out.fullmatch(lowercase				):
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    re_decoder_block_conv_out.match(lowercase				)
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    regex_match.groups()
      SCREAMING_SNAKE_CASE       :      List[str]							=    int(groups[2]				) * 2 + int(groups[3]				) - 2
      SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
      SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    re_decoder_block_conv_out.sub(lowercase						,     lowercase				)

    elif re_decoder_block_resnet.fullmatch(lowercase				):
      SCREAMING_SNAKE_CASE       :      List[Any]							=    re_decoder_block_resnet.match(lowercase				)
      SCREAMING_SNAKE_CASE       :      List[Any]							=    regex_match.groups()
      SCREAMING_SNAKE_CASE       :      Any							=    int(groups[2]				) * 2 + int(groups[3]				) - 2
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    {"1": 1, "3": 2}[groups[-2]]
      SCREAMING_SNAKE_CASE       :      Tuple							=    F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
      SCREAMING_SNAKE_CASE       :      Any							=    F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
      SCREAMING_SNAKE_CASE       :      List[str]							=    prefix + resnet_block
      SCREAMING_SNAKE_CASE       :      Optional[int]							=    re_decoder_block_resnet.sub(lowercase						,     lowercase				)

    elif re_decoder_block_proj_in.fullmatch(lowercase				):
      SCREAMING_SNAKE_CASE       :      List[Any]							=    re_decoder_block_proj_in.match(lowercase				)
      SCREAMING_SNAKE_CASE       :      List[str]							=    regex_match.groups()
      SCREAMING_SNAKE_CASE       :      int							=    F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
      SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    re_decoder_block_proj_in.sub(lowercase						,     lowercase				)

    # rename prior cond.model to upsampler.upsample_block and resnet
    elif re_prior_cond_conv_out.fullmatch(lowercase				):
      SCREAMING_SNAKE_CASE       :      Optional[int]							=    re_prior_cond_conv_out.match(lowercase				)
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    regex_match.groups()
      SCREAMING_SNAKE_CASE       :      Optional[int]							=    int(groups[1]				) * 2 + int(groups[2]				) - 2
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    F'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
      SCREAMING_SNAKE_CASE       :      List[str]							=    re_prior_cond_conv_out.sub(lowercase						,     lowercase				)

    elif re_prior_cond_resnet.fullmatch(lowercase				):
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    re_prior_cond_resnet.match(lowercase				)
      SCREAMING_SNAKE_CASE       :      Tuple							=    regex_match.groups()
      SCREAMING_SNAKE_CASE       :      List[Any]							=    int(groups[1]				) * 2 + int(groups[2]				) - 2
      SCREAMING_SNAKE_CASE       :      List[str]							=    {"1": 1, "3": 2}[groups[-2]]
      SCREAMING_SNAKE_CASE       :      Tuple							=    F'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
      SCREAMING_SNAKE_CASE       :      str							=    F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
      SCREAMING_SNAKE_CASE       :      Any							=    prefix + resnet_block
      SCREAMING_SNAKE_CASE       :      Any							=    re_prior_cond_resnet.sub(lowercase						,     lowercase				)

    elif re_prior_cond_proj_in.fullmatch(lowercase				):
      SCREAMING_SNAKE_CASE       :      Any							=    re_prior_cond_proj_in.match(lowercase				)
      SCREAMING_SNAKE_CASE       :      Any							=    regex_match.groups()
      SCREAMING_SNAKE_CASE       :      int							=    F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
      SCREAMING_SNAKE_CASE       :      List[Any]							=    re_prior_cond_proj_in.sub(lowercase						,     lowercase				)

    # keep original key
    else:
      SCREAMING_SNAKE_CASE       :      Any							=    original_key

    SCREAMING_SNAKE_CASE       :      Any							=    replace_key(lowercase				)

    if F'''{key_prefix}.{key}''' not in model_state_dict or key is None:
      print(F'''failed converting {original_key} to {key}, does not match'''				)

    # handle missmatched shape
    elif value.shape != model_state_dict[F'''{key_prefix}.{key}'''].shape:
      SCREAMING_SNAKE_CASE       :      List[Any]							=    model_state_dict[F'''{key_prefix}.{key}''']
      print(F'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''				)
      SCREAMING_SNAKE_CASE       :      Dict							=    original_key

    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    original_key
    SCREAMING_SNAKE_CASE       :      Tuple							=    value

  return new_dict


@torch.no_grad()
def   lowerCamelCase__			(     lowercase=None						,     lowercase=None				):

  """simple docstring"""






  for file in MODEL_MAPPING[model_name]:
    if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split('/'				)[-1]}'''				):
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    requests.get(F'''{PREFIX}{file}'''						,     allow_redirects=lowercase				)
      os.makedirs(F'''{pytorch_dump_folder_path}/'''						,     exist_ok=lowercase				)
      open(F'''{pytorch_dump_folder_path}/{file.split('/'				)[-1]}'''						,     "wb"				).write(r.content				)

  SCREAMING_SNAKE_CASE       :      int							=    MODEL_MAPPING[model_name.split("/"				)[-1]]

  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    JukeboxConfig.from_pretrained(lowercase				)
  SCREAMING_SNAKE_CASE       :      int							=    JukeboxModel(lowercase				)

  SCREAMING_SNAKE_CASE       :      List[Any]							=    []
  SCREAMING_SNAKE_CASE       :      Dict							=    {}
  for i, dict_name in enumerate(lowercase				):
    SCREAMING_SNAKE_CASE       :      Any							=    torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split('/'				)[-1]}'''				)["model"]

    SCREAMING_SNAKE_CASE       :      List[Any]							=    {}
    for k in old_dic.keys():
      if k.endswith(".b"				):
        SCREAMING_SNAKE_CASE       :      Tuple							=    old_dic[k]
      elif k.endswith(".w"				):
        SCREAMING_SNAKE_CASE       :      Optional[int]							=    old_dic[k]
      elif "level_2" not in dict_name and "cond.model." in k:
        SCREAMING_SNAKE_CASE       :      Optional[Any]							=    old_dic[k]
      else:
        SCREAMING_SNAKE_CASE       :      Optional[int]							=    old_dic[k]

    SCREAMING_SNAKE_CASE       :      List[Any]							=    "vqvae" if i == 0 else F'''priors.{3 - i}'''
    SCREAMING_SNAKE_CASE       :      str							=    fix_jukebox_keys(lowercase						,     model.state_dict()						,     lowercase						,     lowercase				)
    weight_dict.append(lowercase				)

  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    weight_dict.pop(0				)
  model.vqvae.load_state_dict(lowercase				)
  for i in range(len(lowercase				)				):
    model.priors[i].load_state_dict(weight_dict[2 - i]				)

  Path(lowercase				).mkdir(exist_ok=lowercase				)
  with open(F'''{pytorch_dump_folder_path}/mapping.json'''						,     "w"				) as txtfile:
    json.dump(lowercase						,     lowercase				)

  print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
  model.save_pretrained(lowercase				)

  return weight_dict


if __name__ == "__main__":
   snake_case								=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""jukebox-5b-lyrics""",
       type=str,
       help="""Name of the model you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default="""jukebox-5b-lyrics-converted""",
       type=str,
       help="""Path to the output PyTorch model directory.""",
   )
   snake_case								=		parser.parse_args()
   convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)














import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DonutImageProcessor


class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Tuple=7					, UpperCAmelCase_       :	Dict=3					, UpperCAmelCase_       :	Any=18					, UpperCAmelCase_       :	Optional[int]=30					, UpperCAmelCase_       :	Any=400					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Union[str, Any]=None					, UpperCAmelCase_       :	int=True					, UpperCAmelCase_       :	Optional[int]=False					, UpperCAmelCase_       :	List[Any]=True					, UpperCAmelCase_       :	Optional[Any]=True					, UpperCAmelCase_       :	Any=[0.5, 0.5, 0.5]					, UpperCAmelCase_       :	Dict=[0.5, 0.5, 0.5]					, ):
       SCREAMING_SNAKE_CASE       :      Any							=    parent
       SCREAMING_SNAKE_CASE       :      int							=    batch_size
       SCREAMING_SNAKE_CASE       :      Dict							=    num_channels
       SCREAMING_SNAKE_CASE       :      str							=    image_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    min_resolution
       SCREAMING_SNAKE_CASE       :      Any							=    max_resolution
       SCREAMING_SNAKE_CASE       :      int							=    do_resize
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    size if size is not None else {"height": 18, "width": 20}
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    do_thumbnail
       SCREAMING_SNAKE_CASE       :      Tuple							=    do_align_axis
       SCREAMING_SNAKE_CASE       :      List[str]							=    do_pad
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    do_normalize
       SCREAMING_SNAKE_CASE       :      Any							=    image_mean
       SCREAMING_SNAKE_CASE       :      Any							=    image_std






     def 				_A       (				self       :	str      ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }





@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		str			=	DonutImageProcessor if is_vision_available() else None


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    DonutImageProcessingTester(self      )


     @property
     def 				_A       (				self       :	Tuple      ):
       return self.image_processor_tester.prepare_image_processor_dict()


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_resize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "size"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_thumbnail"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_align_long_axis"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_pad"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_normalize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_mean"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_std"      )      )


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size					, {"height": 18, "width": 20}      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=42      )
       self.assertEqual(image_processor.size					, {"height": 42, "width": 42}      )

       # Previous config had dimensions in (width, height) order
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=(42, 84)      )
       self.assertEqual(image_processor.size					, {"height": 84, "width": 42}      )


     def 				_A       (				self       :	Dict      ):
       pass


     @is_flaky()
     def 				_A       (				self       :	Optional[Any]      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, Image.Image      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      List[str]							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Dict							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )


     @is_flaky()
     def 				_A       (				self       :	List[str]      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Dict							=    self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE       :      List[Any]							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, numpify=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, np.ndarray      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      Any							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )






     @is_flaky()
     def 				_A       (				self       :	Dict      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, torchify=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, torch.Tensor      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      int							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )






