


import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


__lowerCAmelCase	: Optional[Any]  				='base_with_context'


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']      )      )
						__SCREAMING_SNAKE_CASE						:     int					=		nn.Parameter(
						    torch.FloatTensor(weights['''Embed_0''']['''embedding''']      )    ,       requires_grad=lowercase__      )
						for lyr_num, lyr in enumerate(model.encoders      ):
												__SCREAMING_SNAKE_CASE						:     str					=		weights[F'''layers_{lyr_num}''']
												__SCREAMING_SNAKE_CASE						:     int					=		nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']      )      )

												__SCREAMING_SNAKE_CASE						:     List[Any]					=		ly_weight['''attention''']
												__SCREAMING_SNAKE_CASE						:     str					=		nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )

												__SCREAMING_SNAKE_CASE						:     List[str]					=		nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )

												__SCREAMING_SNAKE_CASE						:     str					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     str					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T      )      )

						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']      )      )
						return model


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T      )      )

						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		nn.Parameter(
						    torch.FloatTensor(weights['''Embed_0''']['''embedding''']      )    ,       requires_grad=lowercase__      )

						for lyr_num, lyr in enumerate(model.encoders      ):
												__SCREAMING_SNAKE_CASE						:     List[str]					=		weights[F'''layers_{lyr_num}''']
												__SCREAMING_SNAKE_CASE						:     int					=		ly_weight['''attention''']

												__SCREAMING_SNAKE_CASE						:     List[Any]					=		nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Any					=		nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     List[str]					=		nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']      )      )

												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Any					=		nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']      )      )

						return model


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T      )      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T      )      )

						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		nn.Parameter(
						    torch.FloatTensor(weights['''Embed_0''']['''embedding''']      )    ,       requires_grad=lowercase__      )

						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		nn.Parameter(
						    torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T      )      )

						for lyr_num, lyr in enumerate(model.decoders      ):
												__SCREAMING_SNAKE_CASE						:     Dict					=		weights[F'''layers_{lyr_num}''']
												__SCREAMING_SNAKE_CASE						:     int					=		nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']      )      )

												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		nn.Parameter(
												    torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T      )      )

												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		ly_weight['''self_attention''']
												__SCREAMING_SNAKE_CASE						:     Any					=		nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     List[str]					=		nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )

												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		ly_weight['''MultiHeadDotProductAttention_0''']
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Any					=		nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']      )      )

												__SCREAMING_SNAKE_CASE						:     Tuple					=		nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )
												__SCREAMING_SNAKE_CASE						:     int					=		nn.Parameter(
												    torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     int					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T      )      )

						__SCREAMING_SNAKE_CASE						:     List[Any]					=		nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']      )      )

						__SCREAMING_SNAKE_CASE						:     int					=		nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T      )      )

						return model


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		checkpoints.load_tax_checkpoint(args.checkpoint_path      )
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		jnp.tree_util.tree_map(onp.array    ,       lowercase__      )

						__SCREAMING_SNAKE_CASE						:     str					=		[
						    '''from __gin__ import dynamic_registration''',
						    '''from music_spectrogram_diffusion.models.diffusion import diffusion_utils''',
						    '''diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0''',
						    '''diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()''',
						]

						__SCREAMING_SNAKE_CASE						:     Dict					=		os.path.join(args.checkpoint_path    ,       '''..'''    ,       '''config.gin'''      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		inference.parse_training_gin_file(lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		inference.InferenceModel(args.checkpoint_path    ,       lowercase__      )

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''    ,       variance_type='''fixed_large'''      )

						__SCREAMING_SNAKE_CASE						:     List[Any]					=		SpectrogramNotesEncoder(
						    max_length=synth_model.sequence_length['''inputs''']    ,       vocab_size=synth_model.model.module.config.vocab_size    ,       d_model=synth_model.model.module.config.emb_dim    ,       dropout_rate=synth_model.model.module.config.dropout_rate    ,       num_layers=synth_model.model.module.config.num_encoder_layers    ,       num_heads=synth_model.model.module.config.num_heads    ,       d_kv=synth_model.model.module.config.head_dim    ,       d_ff=synth_model.model.module.config.mlp_dim    ,       feed_forward_proj='''gated-gelu'''    ,       )

						__SCREAMING_SNAKE_CASE						:     str					=		SpectrogramContEncoder(
						    input_dims=synth_model.audio_codec.n_dims    ,       targets_context_length=synth_model.sequence_length['''targets_context''']    ,       d_model=synth_model.model.module.config.emb_dim    ,       dropout_rate=synth_model.model.module.config.dropout_rate    ,       num_layers=synth_model.model.module.config.num_encoder_layers    ,       num_heads=synth_model.model.module.config.num_heads    ,       d_kv=synth_model.model.module.config.head_dim    ,       d_ff=synth_model.model.module.config.mlp_dim    ,       feed_forward_proj='''gated-gelu'''    ,       )

						__SCREAMING_SNAKE_CASE						:     Any					=		TaFilmDecoder(
						    input_dims=synth_model.audio_codec.n_dims    ,       targets_length=synth_model.sequence_length['''targets_context''']    ,       max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time    ,       d_model=synth_model.model.module.config.emb_dim    ,       num_layers=synth_model.model.module.config.num_decoder_layers    ,       num_heads=synth_model.model.module.config.num_heads    ,       d_kv=synth_model.model.module.config.head_dim    ,       d_ff=synth_model.model.module.config.mlp_dim    ,       dropout_rate=synth_model.model.module.config.dropout_rate    ,       )

						__SCREAMING_SNAKE_CASE						:     int					=		load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Dict					=		load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		load_decoder(ta_checkpoint['''target''']['''decoder''']    ,       lowercase__      )

						__SCREAMING_SNAKE_CASE						:     int					=		OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''      )

						__SCREAMING_SNAKE_CASE						:     Dict					=		SpectrogramDiffusionPipeline(
						    notes_encoder=lowercase__    ,       continuous_encoder=lowercase__    ,       decoder=lowercase__    ,       scheduler=lowercase__    ,       melgan=lowercase__    ,       )
						if args.save:
												pipe.save_pretrained(args.output_path      )


if __name__ == "__main__":
						__lowerCAmelCase	: str  				=argparse.ArgumentParser()

						parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
						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=f"""{MODEL}/checkpoint_500000""",
						    type=str,
						    required=False,
						    help='Path to the original jax model checkpoint.',
						)
						__lowerCAmelCase	: Tuple  				=parser.parse_args()

						main(args)




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_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VivitImageProcessor
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def __init__(       self			:Any			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:Tuple=7			,			lowerCAmelCase__			:List[Any]=3			,			lowerCAmelCase__			:Any=10			,			lowerCAmelCase__			:Optional[int]=18			,			lowerCAmelCase__			:Dict=30			,			lowerCAmelCase__			:Tuple=400			,			lowerCAmelCase__			:List[Any]=True			,			lowerCAmelCase__			:Tuple=None			,			lowerCAmelCase__			:str=True			,			lowerCAmelCase__			:List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase__			:List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase__			:Optional[Any]=None			,			)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		size if size is not None else {'''shortest_edge''': 18}
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

										__SCREAMING_SNAKE_CASE						:     Tuple					=		parent
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		batch_size
										__SCREAMING_SNAKE_CASE						:     List[str]					=		num_channels
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		num_frames
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_size
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		min_resolution
										__SCREAMING_SNAKE_CASE						:     Any					=		max_resolution
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		do_resize
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		size
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		do_normalize
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		image_mean
										__SCREAMING_SNAKE_CASE						:     List[str]					=		image_std
										__SCREAMING_SNAKE_CASE						:     str					=		crop_size



				def 		__magic_name__(       self			:Tuple	)		->			Any:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}




@require_torch
@require_vision
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[str]						=		VivitImageProcessor if is_vision_available() else None


				def 		__magic_name__(       self			:List[str]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		VivitImageProcessingTester(self	)


				@property
				def 		__magic_name__(       self			:int	)		->			Union[str, Any]:
										return self.image_processor_tester.prepare_image_processor_dict()


				def 		__magic_name__(       self			:List[str]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''image_mean'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''image_std'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_normalize'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_resize'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_center_crop'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''size'''	)	)


				def 		__magic_name__(       self			:Optional[Any]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.image_processing_class.from_dict(self.image_processor_dict	)
										self.assertEqual(image_processor.size			,			{'''shortest_edge''': 18}	)
										self.assertEqual(image_processor.crop_size			,			{'''height''': 18, '''width''': 18}	)

										__SCREAMING_SNAKE_CASE						:     Tuple					=		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 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PIL videos
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			Image.Image	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     List[str]					=		image_processing(video_inputs[0]			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        1,
										        self.image_processor_tester.num_frames,
										        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(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)


				def 		__magic_name__(       self			:str	)		->			int:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Any					=		self.image_processing_class(**self.image_processor_dict	)
										# create random numpy tensors
										__SCREAMING_SNAKE_CASE						:     List[str]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__			,			numpify=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			np.ndarray	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		image_processing(video_inputs[0]			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        1,
										        self.image_processor_tester.num_frames,
										        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						:     Any					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)



				def 		__magic_name__(       self			:Any	)		->			List[str]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PyTorch tensors
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__			,			torchify=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			torch.Tensor	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_processing(video_inputs[0]			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        1,
										        self.image_processor_tester.num_frames,
										        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[int]					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)
