






import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 		_snake_case	(	A__ ,     unittest.TestCase       ):
			_lowercase		:     Optional[int]        =				AudioLDMPipeline
			_lowercase		:     Union[str, Any]        =				TEXT_TO_AUDIO_PARAMS
			_lowercase		:     List[str]        =				TEXT_TO_AUDIO_BATCH_PARAMS
			_lowercase		:     Tuple        =				frozenset(
			    [
			        '''num_inference_steps''',
			        '''num_waveforms_per_prompt''',
			        '''generator''',
			        '''latents''',
			        '''output_type''',
			        '''return_dict''',
			        '''callback''',
			        '''callback_steps''',
			    ]       )






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Dict:
						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE							=			UNetaDConditionModel(
						    block_out_channels=(32, 64)		,       layers_per_block=2		,       sample_size=32		,       in_channels=4		,       out_channels=4		,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,       cross_attention_dim=(32, 64)		,       class_embed_type='simple_projection'		,       projection_class_embeddings_input_dim=32		,       class_embeddings_concat=a		,       )
						SCREAMING_SNAKE_CASE							=			DDIMScheduler(
						    beta_start=0.0_00_85		,       beta_end=0.0_12		,       beta_schedule='scaled_linear'		,       clip_sample=a		,       set_alpha_to_one=a		,       )
						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE							=			AutoencoderKL(
						    block_out_channels=[32, 64]		,       in_channels=1		,       out_channels=1		,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,       latent_channels=4		,       )
						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE							=			ClapTextConfig(
						    bos_token_id=0		,       eos_token_id=2		,       hidden_size=32		,       intermediate_size=37		,       layer_norm_eps=1E-05		,       num_attention_heads=4		,       num_hidden_layers=5		,       pad_token_id=1		,       vocab_size=1000		,       projection_dim=32		,       )
						SCREAMING_SNAKE_CASE							=			ClapTextModelWithProjection(a)
						SCREAMING_SNAKE_CASE							=			RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'		,       model_max_length=77)

						SCREAMING_SNAKE_CASE							=			SpeechTaHifiGanConfig(
						    model_in_dim=8		,       sampling_rate=1_6000		,       upsample_initial_channel=16		,       upsample_rates=[2, 2]		,       upsample_kernel_sizes=[4, 4]		,       resblock_kernel_sizes=[3, 7]		,       resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]		,       normalize_before=a		,       )

						SCREAMING_SNAKE_CASE							=			SpeechTaHifiGan(a)

						SCREAMING_SNAKE_CASE							=			{
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'vocoder': vocoder,
						}
						return components






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a=0)   ->							int:
						if str(a).startswith('mps'):
									SCREAMING_SNAKE_CASE							=			torch.manual_seed(a)
						else:
									SCREAMING_SNAKE_CASE							=			torch.Generator(device=a).manual_seed(a)
						SCREAMING_SNAKE_CASE							=			{
						    'prompt': 'A hammer hitting a wooden surface',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						}
						return inputs






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						SCREAMING_SNAKE_CASE							=			'cpu'  # ensure determinism for the device-dependent torch.Generator

						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						assert audio.ndim == 1
						assert len(a) == 256

						SCREAMING_SNAKE_CASE							=			audio[:10]
						SCREAMING_SNAKE_CASE							=			np.array(
						    [-0.00_50, 0.00_50, -0.00_60, 0.00_33, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_33])

						assert np.abs(audio_slice - expected_slice).max() < 1E-2






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			3 * [inputs['prompt']]

						# forward
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			3 * [inputs.pop('prompt')]

						SCREAMING_SNAKE_CASE							=			audioldm_pipe.tokenizer(
						    a		,       padding='max_length'		,       max_length=audioldm_pipe.tokenizer.model_max_length		,       truncation=a		,       return_tensors='pt'		,       )
						SCREAMING_SNAKE_CASE							=			text_inputs['input_ids'].to(a)

						SCREAMING_SNAKE_CASE							=			audioldm_pipe.text_encoder(
						    a		,       )
						SCREAMING_SNAKE_CASE							=			prompt_embeds.text_embeds
						# additional L_2 normalization over each hidden-state
						SCREAMING_SNAKE_CASE							=			F.normalize(a		,       dim=-1)

						SCREAMING_SNAKE_CASE							=			prompt_embeds

						# forward
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						assert np.abs(audio_a - audio_a).max() < 1E-2






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			3 * ['this is a negative prompt']
						SCREAMING_SNAKE_CASE							=			negative_prompt
						SCREAMING_SNAKE_CASE							=			3 * [inputs['prompt']]

						# forward
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			3 * [inputs.pop('prompt')]

						SCREAMING_SNAKE_CASE							=			[]
						for p in [prompt, negative_prompt]:
									SCREAMING_SNAKE_CASE							=			audioldm_pipe.tokenizer(
									    a		,       padding='max_length'		,       max_length=audioldm_pipe.tokenizer.model_max_length		,       truncation=a		,       return_tensors='pt'		,       )
									SCREAMING_SNAKE_CASE							=			text_inputs['input_ids'].to(a)

									SCREAMING_SNAKE_CASE							=			audioldm_pipe.text_encoder(
									    a		,       )
									SCREAMING_SNAKE_CASE							=			text_embeds.text_embeds
									# additional L_2 normalization over each hidden-state
									SCREAMING_SNAKE_CASE							=			F.normalize(a		,       dim=-1)

									embeds.append(a)

						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			embeds

						# forward
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						assert np.abs(audio_a - audio_a).max() < 1E-2






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Dict:
						SCREAMING_SNAKE_CASE							=			'cpu'  # ensure determinism for the device-dependent torch.Generator
						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			PNDMScheduler(skip_prk_steps=a)
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			'egg cracking'
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a		,       negative_prompt=a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						assert audio.ndim == 1
						assert len(a) == 256

						SCREAMING_SNAKE_CASE							=			audio[:10]
						SCREAMING_SNAKE_CASE							=			np.array(
						    [-0.00_51, 0.00_50, -0.00_60, 0.00_34, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_32])

						assert np.abs(audio_slice - expected_slice).max() < 1E-2






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						SCREAMING_SNAKE_CASE							=			'cpu'  # ensure determinism for the device-dependent torch.Generator
						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			PNDMScheduler(skip_prk_steps=a)
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			'A hammer hitting a wooden surface'

						# test num_waveforms_per_prompt=1 (default)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(a		,       num_inference_steps=2).audios

						assert audios.shape == (1, 256)

						# test num_waveforms_per_prompt=1 (default) for batch of prompts
						SCREAMING_SNAKE_CASE							=			2
						SCREAMING_SNAKE_CASE							=			audioldm_pipe([prompt] * batch_size		,       num_inference_steps=2).audios

						assert audios.shape == (batch_size, 256)

						# test num_waveforms_per_prompt for single prompt
						SCREAMING_SNAKE_CASE							=			2
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(a		,       num_inference_steps=2		,       num_waveforms_per_prompt=a).audios

						assert audios.shape == (num_waveforms_per_prompt, 256)

						# test num_waveforms_per_prompt for batch of prompts
						SCREAMING_SNAKE_CASE							=			2
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(
						    [prompt] * batch_size		,       num_inference_steps=2		,       num_waveforms_per_prompt=a).audios

						assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						SCREAMING_SNAKE_CASE							=			'cpu'  # ensure determinism for the device-dependent torch.Generator
						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.vocoder.config.sampling_rate

						SCREAMING_SNAKE_CASE							=			self.get_dummy_inputs(a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(audio_length_in_s=0.0_16		,       **a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						assert audio.ndim == 1
						assert len(a) / vocoder_sampling_rate == 0.0_16

						SCREAMING_SNAKE_CASE							=			audioldm_pipe(audio_length_in_s=0.0_32		,       **a)
						SCREAMING_SNAKE_CASE							=			output.audios[0]

						assert audio.ndim == 1
						assert len(a) / vocoder_sampling_rate == 0.0_32






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						SCREAMING_SNAKE_CASE							=			self.get_dummy_components()
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline(**a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			['hey']

						SCREAMING_SNAKE_CASE							=			audioldm_pipe(a		,       num_inference_steps=1)
						SCREAMING_SNAKE_CASE							=			output.audios.shape
						assert audio_shape == (1, 256)

						SCREAMING_SNAKE_CASE							=			audioldm_pipe.vocoder.config
						config.model_in_dim *= 2
						SCREAMING_SNAKE_CASE							=			SpeechTaHifiGan(a).to(a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(a		,       num_inference_steps=1)
						SCREAMING_SNAKE_CASE							=			output.audios.shape
						# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
						assert audio_shape == (1, 256)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Dict:
						self._test_attention_slicing_forward_pass(test_mean_pixel_difference=a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						self._test_inference_batch_single_identical(test_mean_pixel_difference=a)







			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available()		,       reason='XFormers attention is only available with CUDA and `xformers` installed'		,       )
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=a)



@slow
class 		_snake_case	(	unittest.TestCase       ):






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a="cpu"		,       a=torch.floataa		,       a=0)   ->							Dict:
						SCREAMING_SNAKE_CASE							=			torch.Generator(device=a).manual_seed(a)
						SCREAMING_SNAKE_CASE							=			np.random.RandomState(a).standard_normal((1, 8, 128, 16))
						SCREAMING_SNAKE_CASE							=			torch.from_numpy(a).to(device=a		,       dtype=a)
						SCREAMING_SNAKE_CASE							=			{
						    'prompt': 'A hammer hitting a wooden surface',
						    'latents': latents,
						    'generator': generator,
						    'num_inference_steps': 3,
						    'guidance_scale': 2.5,
						}
						return inputs






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline.from_pretrained('cvssp/audioldm')
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			self.get_inputs(a)
						SCREAMING_SNAKE_CASE							=			25
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a).audios[0]

						assert audio.ndim == 1
						assert len(a) == 8_1920

						SCREAMING_SNAKE_CASE							=			audio[7_7230:7_7240]
						SCREAMING_SNAKE_CASE							=			np.array(
						    [-0.48_84, -0.46_07, 0.00_23, 0.50_07, 0.58_96, 0.51_51, 0.38_13, -0.02_08, -0.36_87, -0.43_15])
						SCREAMING_SNAKE_CASE							=			np.abs(expected_slice - audio_slice).max()
						assert max_diff < 1E-2







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						SCREAMING_SNAKE_CASE							=			AudioLDMPipeline.from_pretrained('cvssp/audioldm')
						SCREAMING_SNAKE_CASE							=			LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe.to(a)
						audioldm_pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE							=			self.get_inputs(a)
						SCREAMING_SNAKE_CASE							=			audioldm_pipe(**a).audios[0]

						assert audio.ndim == 1
						assert len(a) == 8_1920

						SCREAMING_SNAKE_CASE							=			audio[2_7780:2_7790]
						SCREAMING_SNAKE_CASE							=			np.array([-0.21_31, -0.08_73, -0.01_24, -0.01_89, 0.05_69, 0.13_73, 0.18_83, 0.28_86, 0.32_97, 0.22_12])
						SCREAMING_SNAKE_CASE							=			np.abs(expected_slice - audio_slice).max()
						assert max_diff < 3E-2









import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_lowerCamelCase    :		Dict		      =    {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def __init__(  self    :				Optional[Any]       ,	snake_case    :				int	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torchvision.models.resnetaaa(pretrained=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				list(model.children()	)[:-2]
											SCREAMING_SNAKE_CASE							:     List[str]						=				nn.Sequential(*snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.pool(self.model(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Any						=				torch.flatten(snake_case       ,	start_dim=2	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				out.transpose(1       ,	2	).contiguous()
											return out  # BxNx2048


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						def __init__(  self    :				str       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				int       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				[json.loads(snake_case	) for l in open(snake_case	)]
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				os.path.dirname(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				tokenizer
											SCREAMING_SNAKE_CASE							:     Dict						=				labels
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				len(snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				max_seq_length

											SCREAMING_SNAKE_CASE							:     List[Any]						=				transforms







						def __len__(  self    :				str	):



											'''simple docstring'''


											return len(self.data	)







						def __getitem__(  self    :				Dict       ,	snake_case    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,	add_special_tokens=snake_case	)	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[Any]						=				sentence[0], sentence[1:-1], sentence[-1]
											SCREAMING_SNAKE_CASE							:     str						=				sentence[: self.max_seq_length]

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.zeros(self.n_classes	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				1

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				Image.open(os.path.join(self.data_dir       ,	self.data[index]['img']	)	).convert('RGB'	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				self.transforms(snake_case	)

											return {
											    "image_start_token": start_token,
											    "image_end_token": end_token,
											    "sentence": sentence,
											    "image": image,
											    "label": label,
											}







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				Counter()
											for row in self.data:
																label_freqs.update(row['label']	)
											return label_freqs


def        __a			(   __lowerCAmelCase   )     ->     Union[str, Any]:
					SCREAMING_SNAKE_CASE							:     str						=				[len(row['sentence']   ) for row in batch]
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Dict						=				len(__lowerCAmelCase   ), max(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.zeros(__lowerCAmelCase      ,	__lowerCAmelCase      ,	dtype=torch.long   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				torch.zeros(__lowerCAmelCase      ,	__lowerCAmelCase      ,	dtype=torch.long   )

					for i_batch, (input_row, length) in enumerate(zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ):
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				input_row['sentence']
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				1

					SCREAMING_SNAKE_CASE							:     List[str]						=				torch.stack([row['image'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.stack([row['label'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Tuple						=				torch.stack([row['image_start_token'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Any						=				torch.stack([row['image_end_token'] for row in batch]   )

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor

def        __a			(   )     ->     str:
					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]



def        __a			(   )     ->     Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(256   ),
					        transforms.CenterCrop(224   ),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]      ,	std=[0.12_221_994, 0.12_145_835, 0.14_380_469]      ,	),
					    ]   )