


'''simple docstring'''


import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


UpperCAmelCase_		:   Dict							   =		{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_8000,
        'sample_size': 6_5536,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8000,
        'sample_size': 6_5536,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8000,
        'sample_size': 13_1072,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6000,
        'sample_size': 6_5536,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6000,
        'sample_size': 6_5536,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6000,
        'sample_size': 6_5536,
    },
}







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				return torch.atana(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ) / math.pi * 2







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.sin(t * math.pi / 2     ) ** 2
				_SCREAMING_SNAKE_CASE :       int     		=      (1 - sigma**2) ** 0.5
				return alpha_sigma_to_t(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							pass





class     lowercase__  (						nn.Module					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case  ):
											super().__init__()

											_SCREAMING_SNAKE_CASE :       Any     		=      DiffusionAttnUnetaD(__snake_case    ,					n_attn_layers=4  )
											_SCREAMING_SNAKE_CASE :       str     		=      deepcopy(self.diffusion  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.quasirandom.SobolEngine(1    ,					scramble=__snake_case  )







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      MODELS_MAP[model_name]["""url"""]
				os.system(f"""wget {url} ./"""     )

				return f"""./{model_name}.ckpt"""


UpperCAmelCase_		:   Union[str, Any]							   =		{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
UpperCAmelCase_		:   Tuple							   =		{
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
UpperCAmelCase_		:   int							   =		{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
    '8': 'resnets.3',
    '9': 'attentions.3',
    '10': 'resnets.4',
    '11': 'attentions.4',
    '12': 'resnets.5',
    '13': 'attentions.5',
}
UpperCAmelCase_		:   Union[str, Any]							   =		{
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

UpperCAmelCase_		:   Optional[Any]							   =		{
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

UpperCAmelCase_		:   str							   =		{
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				if name.startswith("""skip"""     ):
								return name.replace("""skip"""							,      RES_CONV_MAP["""skip"""]     )

				# name has to be of format main.{digit}
				if not name.startswith("""main."""     ):
								raise ValueError(f"""ResConvBlock error with {name}"""     )

				return name.replace(name[:6]							,      RES_CONV_MAP[name[:6]]     )







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				for key, value in ATTN_MAP.items():
								if name.startswith(SCREAMING_SNAKE_CASE__     ) and not isinstance(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):
												return name.replace(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
								elif name.startswith(SCREAMING_SNAKE_CASE__     ):
												return [name.replace(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ) for v in value]
				raise ValueError(f"""Attn error with {name}"""     )







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__=13     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       List[str]     		=      input_string

				if string.split("""."""     )[0] == "timestep_embed":
								return string.replace("""timestep_embed"""							,      """time_proj"""     )

				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      0
				if string.startswith("""net.3."""     ):
								depth += 1
								_SCREAMING_SNAKE_CASE :       Any     		=      string[6:]
				elif string.startswith("""net."""     ):
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      string[4:]

				while string.startswith("""main.7."""     ):
								depth += 1
								_SCREAMING_SNAKE_CASE :       List[str]     		=      string[7:]

				if string.startswith("""main."""     ):
								_SCREAMING_SNAKE_CASE :       Any     		=      string[5:]

				# mid block
				if string[:2].isdigit():
								_SCREAMING_SNAKE_CASE :       str     		=      string[:2]
								_SCREAMING_SNAKE_CASE :       Any     		=      string[2:]
				else:
								_SCREAMING_SNAKE_CASE :       str     		=      string[0]
								_SCREAMING_SNAKE_CASE :       str     		=      string[1:]

				if depth == max_depth:
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      MID_NUM_TO_LAYER[layer_num]
								_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      """mid_block"""
				elif depth > 0 and int(SCREAMING_SNAKE_CASE__     ) < 7:
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      DOWN_NUM_TO_LAYER[layer_num]
								_SCREAMING_SNAKE_CASE :       Dict     		=      f"""down_blocks.{depth}"""
				elif depth > 0 and int(SCREAMING_SNAKE_CASE__     ) > 7:
								_SCREAMING_SNAKE_CASE :       List[str]     		=      UP_NUM_TO_LAYER[layer_num]
								_SCREAMING_SNAKE_CASE :       Dict     		=      f"""up_blocks.{max_depth - depth - 1}"""
				elif depth == 0:
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      DEPTH_0_TO_LAYER[layer_num]
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      f"""up_blocks.{max_depth - 1}""" if int(SCREAMING_SNAKE_CASE__     ) > 3 else """down_blocks.0"""

				if not string_left.startswith("""."""     ):
								raise ValueError(f"""Naming error with {input_string} and string_left: {string_left}."""     )

				_SCREAMING_SNAKE_CASE :       Dict     		=      string_left[1:]

				if "resnets" in new_layer:
								_SCREAMING_SNAKE_CASE :       int     		=      convert_resconv_naming(SCREAMING_SNAKE_CASE__     )
				elif "attentions" in new_layer:
								_SCREAMING_SNAKE_CASE :       str     		=      convert_attn_naming(SCREAMING_SNAKE_CASE__     )
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      new_string_left

				if not isinstance(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):
								_SCREAMING_SNAKE_CASE :       Any     		=      prefix + """.""" + new_layer + """.""" + string_left
				else:
								_SCREAMING_SNAKE_CASE :       List[str]     		=      [prefix + """.""" + new_layer + """.""" + s for s in string_left]
				return new_string







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       int     		=      {}
				for k, v in state_dict.items():
								if k.endswith("""kernel"""     ):
												# up- and downsample layers, don't have trainable weights
												continue

								_SCREAMING_SNAKE_CASE :       str     		=      rename(SCREAMING_SNAKE_CASE__     )

								# check if we need to transform from Conv => Linear for attention
								if isinstance(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):
												_SCREAMING_SNAKE_CASE :       Any     		=      transform_conv_attns(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
								else:
												_SCREAMING_SNAKE_CASE :       Tuple     		=      v

				return new_state_dict







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				if len(SCREAMING_SNAKE_CASE__     ) == 1:
								if len(v.shape     ) == 3:
												# weight
												_SCREAMING_SNAKE_CASE :       str     		=      v[:, :, 0]
								else:
												# bias
												_SCREAMING_SNAKE_CASE :       int     		=      v
				else:
								# qkv matrices
								_SCREAMING_SNAKE_CASE :       str     		=      v.shape[0]
								_SCREAMING_SNAKE_CASE :       List[str]     		=      trippled_shape // 3
								for i in range(3     ):
												if len(v.shape     ) == 3:
																_SCREAMING_SNAKE_CASE :       str     		=      v[i * single_shape : (i + 1) * single_shape, :, 0]
												else:
																_SCREAMING_SNAKE_CASE :       List[str]     		=      v[i * single_shape : (i + 1) * single_shape]
				return new_state_dict







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""     )

				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      args.model_path.split("""/"""     )[-1].split("""."""     )[0]
				if not os.path.isfile(args.model_path     ):
								assert (
								    model_name == args.model_path
								), f"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
								_SCREAMING_SNAKE_CASE :       Dict     		=      download(SCREAMING_SNAKE_CASE__     )

				_SCREAMING_SNAKE_CASE :       Tuple     		=      MODELS_MAP[model_name]["""sample_rate"""]
				_SCREAMING_SNAKE_CASE :       List[str]     		=      MODELS_MAP[model_name]["""sample_size"""]

				_SCREAMING_SNAKE_CASE :       Tuple     		=      Object()
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      sample_size
				_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_rate
				_SCREAMING_SNAKE_CASE :       List[Any]     		=      0

				_SCREAMING_SNAKE_CASE :       List[Any]     		=      UNetaDModel(sample_size=SCREAMING_SNAKE_CASE__							,      sample_rate=SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       List[Any]     		=      diffusers_model.state_dict()

				_SCREAMING_SNAKE_CASE :       List[str]     		=      DiffusionUncond(SCREAMING_SNAKE_CASE__     )
				orig_model.load_state_dict(torch.load(args.model_path							,      map_location=SCREAMING_SNAKE_CASE__     )["""state_dict"""]     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      orig_model.diffusion_ema.eval()
				_SCREAMING_SNAKE_CASE :       int     		=      orig_model.state_dict()
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      rename_orig_weights(SCREAMING_SNAKE_CASE__     )

				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      set(renamed_state_dict.keys()     ) - set(diffusers_state_dict.keys()     )
				_SCREAMING_SNAKE_CASE :       int     		=      set(diffusers_state_dict.keys()     ) - set(renamed_state_dict.keys()     )

				assert len(SCREAMING_SNAKE_CASE__     ) == 0, f"""Problem with {renamed_minus_diffusers}"""
				assert all(k.endswith("""kernel"""     ) for k in list(SCREAMING_SNAKE_CASE__     )     ), f"""Problem with {diffusers_minus_renamed}"""

				for key, value in renamed_state_dict.items():
								assert (
								    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
								), f"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
								if key == "time_proj.weight":
												_SCREAMING_SNAKE_CASE :       str     		=      value.squeeze()

								_SCREAMING_SNAKE_CASE :       Tuple     		=      value

				diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE__     )

				_SCREAMING_SNAKE_CASE :       List[Any]     		=      100
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      33

				_SCREAMING_SNAKE_CASE :       str     		=      IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE__     )

				_SCREAMING_SNAKE_CASE :       int     		=      torch.manual_seed(SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      torch.randn([1, 2, config.sample_size]							,      generator=SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     )

				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.linspace(1							,      0							,      steps + 1							,      device=SCREAMING_SNAKE_CASE__     )[:-1]
				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      get_crash_schedule(SCREAMING_SNAKE_CASE__     )

				_SCREAMING_SNAKE_CASE :       List[str]     		=      DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE__							,      scheduler=SCREAMING_SNAKE_CASE__     )

				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      torch.manual_seed(33     )
				_SCREAMING_SNAKE_CASE :       Any     		=      pipe(num_inference_steps=SCREAMING_SNAKE_CASE__							,      generator=SCREAMING_SNAKE_CASE__     ).audios

				_SCREAMING_SNAKE_CASE :       List[Any]     		=      sampling.iplms_sample(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      {}     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      generated.clamp(-1							,      1     )

				_SCREAMING_SNAKE_CASE :       Tuple     		=      (generated - audio).abs().sum()
				_SCREAMING_SNAKE_CASE :       List[str]     		=      (generated - audio).abs().max()

				if args.save:
								pipe.save_pretrained(args.checkpoint_path     )

				print("""Diff sum"""							,      SCREAMING_SNAKE_CASE__     )
				print("""Diff max"""							,      SCREAMING_SNAKE_CASE__     )

				assert diff_max < 1E-3, f"""Diff max: {diff_max} is too much :-/"""

				print(f"""Conversion for {model_name} successful!"""     )


if __name__ == "__main__":
				UpperCAmelCase_		:   List[str]							   =		argparse.ArgumentParser()

				parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
				parser.add_argument(
				    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
				)
				parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
				UpperCAmelCase_		:   Optional[int]							   =		parser.parse_args()

				main(args)








'''simple docstring'''


import unittest

import numpy as np

from transformers.testing_utils import 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 MobileNetVaImageProcessor





class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=7    ,					__snake_case=3    ,					__snake_case=18    ,					__snake_case=30    ,					__snake_case=400    ,					__snake_case=True    ,					__snake_case=None    ,					__snake_case=True    ,					__snake_case=None    ,					):
											_SCREAMING_SNAKE_CASE :       Any     		=      size if size is not None else {"""shortest_edge""": 20}
											_SCREAMING_SNAKE_CASE :       int     		=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											_SCREAMING_SNAKE_CASE :       Dict     		=      parent
											_SCREAMING_SNAKE_CASE :       Any     		=      batch_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_channels
											_SCREAMING_SNAKE_CASE :       Dict     		=      image_size
											_SCREAMING_SNAKE_CASE :       Any     		=      min_resolution
											_SCREAMING_SNAKE_CASE :       Any     		=      max_resolution
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      do_resize
											_SCREAMING_SNAKE_CASE :       Tuple     		=      size
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      do_center_crop
											_SCREAMING_SNAKE_CASE :       str     		=      crop_size






							def UpperCAmelCase_	(    self  ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}





@require_torch
@require_vision
class     lowercase__  (						_snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Union[str, Any]      	=  MobileNetVaImageProcessor if is_vision_available() else None






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      MobileNetVaImageProcessingTester(self  )






							@property
							def UpperCAmelCase_	(    self  ):
											return self.image_processor_tester.prepare_image_processor_dict()






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_resize"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""size"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_center_crop"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""crop_size"""  )  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class.from_dict(self.image_processor_dict  )
											self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 20}  )
											self.assertEqual(image_processor.crop_size    ,					{"""height""": 18, """width""": 18}  )

											_SCREAMING_SNAKE_CASE :       Dict     		=      self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42    ,					crop_size=84  )
											self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42}  )
											self.assertEqual(image_processor.crop_size    ,					{"""height""": 84, """width""": 84}  )






							def UpperCAmelCase_	(    self  ):
											pass






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											_SCREAMING_SNAKE_CASE :       List[str]     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					Image.Image  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       Optional[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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)

											# Test batched
											_SCREAMING_SNAKE_CASE :       int     		=      image_processing(__snake_case    ,					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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random numpy tensors
											_SCREAMING_SNAKE_CASE :       Tuple     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					numpify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					np.ndarray  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       Optional[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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)

											# Test batched
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image_processing(__snake_case    ,					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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)






							def UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PyTorch tensors
											_SCREAMING_SNAKE_CASE :       str     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					torchify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					torch.Tensor  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       List[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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)

											# Test batched
											_SCREAMING_SNAKE_CASE :       str     		=      image_processing(__snake_case    ,					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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )    ,					)




