
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


lowercase__							:							Dict									=			{
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 4_8_0_0_0,
        "sample_size": 6_5_5_3_6,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 4_8_0_0_0,
        "sample_size": 6_5_5_3_6,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 4_8_0_0_0,
        "sample_size": 1_3_1_0_7_2,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 1_6_0_0_0,
        "sample_size": 6_5_5_3_6,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 1_6_0_0_0,
        "sample_size": 6_5_5_3_6,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 1_6_0_0_0,
        "sample_size": 6_5_5_3_6,
    },
}




def 	A_ (							snake_case							:   List[str] ,      snake_case							:   Optional[Any] )      ->    Tuple:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Optional[int] )      ->    Any:



						'''simple docstring'''

						__UpperCamelCase       =			torch.sin(t * math.pi / 2 ) ** 2
						__UpperCamelCase       =			(1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(snake_case ,      snake_case )






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							pass






class        SCREAMING_SNAKE_CASE__    (							nn.Module    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													super().__init__()

													__UpperCamelCase       =			DiffusionAttnUnetaD(SCREAMING_SNAKE_CASE_     ,	n_attn_layers=4			)
													__UpperCamelCase       =			deepcopy(self.diffusion			)
													__UpperCamelCase       =			torch.quasirandom.SobolEngine(1     ,	scramble=SCREAMING_SNAKE_CASE_			)





def 	A_ (							snake_case							:   Union[str, Any] )      ->    List[str]:



						'''simple docstring'''

						__UpperCamelCase       =			MODELS_MAP[model_name]['''url''']
						os.system(f"wget {url} ./" )

						return f"./{model_name}.ckpt"


lowercase__							:							List[Any]									=			{
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
lowercase__							:							str									=			{
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
lowercase__							:							Optional[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",
}
lowercase__							:							Optional[int]									=			{
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

lowercase__							:							Union[str, Any]									=			{
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

lowercase__							:							Dict									=			{
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}




def 	A_ (							snake_case							:   str )      ->    Optional[int]:



						'''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 	A_ (							snake_case							:   Tuple )      ->    Dict:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Union[str, Any] ,      snake_case							:   Optional[Any]=13 )      ->    Optional[int]:



						'''simple docstring'''

						__UpperCamelCase       =			input_string

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

						__UpperCamelCase       =			0
						if string.startswith('''net.3.''' ):
												depth += 1
												__UpperCamelCase       =			string[6:]
						elif string.startswith('''net.''' ):
												__UpperCamelCase       =			string[4:]

						while string.startswith('''main.7.''' ):
												depth += 1
												__UpperCamelCase       =			string[7:]

						if string.startswith('''main.''' ):
												__UpperCamelCase       =			string[5:]

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

						if depth == max_depth:
												__UpperCamelCase       =			MID_NUM_TO_LAYER[layer_num]
												__UpperCamelCase       =			'''mid_block'''
						elif depth > 0 and int(snake_case ) < 7:
												__UpperCamelCase       =			DOWN_NUM_TO_LAYER[layer_num]
												__UpperCamelCase       =			f"down_blocks.{depth}"
						elif depth > 0 and int(snake_case ) > 7:
												__UpperCamelCase       =			UP_NUM_TO_LAYER[layer_num]
												__UpperCamelCase       =			f"up_blocks.{max_depth - depth - 1}"
						elif depth == 0:
												__UpperCamelCase       =			DEPTH_0_TO_LAYER[layer_num]
												__UpperCamelCase       =			f"up_blocks.{max_depth - 1}" if int(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}." )

						__UpperCamelCase       =			string_left[1:]

						if "resnets" in new_layer:
												__UpperCamelCase       =			convert_resconv_naming(snake_case )
						elif "attentions" in new_layer:
												__UpperCamelCase       =			convert_attn_naming(snake_case )
												__UpperCamelCase       =			new_string_left

						if not isinstance(snake_case ,      snake_case ):
												__UpperCamelCase       =			prefix + '''.''' + new_layer + '''.''' + string_left
						else:
												__UpperCamelCase       =			[prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
						return new_string




def 	A_ (							snake_case							:   Tuple )      ->    Optional[int]:



						'''simple docstring'''

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

												__UpperCamelCase       =			rename(snake_case )

												# check if we need to transform from Conv => Linear for attention
												if isinstance(snake_case ,      snake_case ):
																		__UpperCamelCase       =			transform_conv_attns(snake_case ,      snake_case ,      snake_case )
												else:
																		__UpperCamelCase       =			v

						return new_state_dict




def 	A_ (							snake_case							:   str ,      snake_case							:   Dict ,      snake_case							:   List[Any] )      ->    Tuple:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Tuple )      ->    List[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )

						__UpperCamelCase       =			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()}"
												__UpperCamelCase       =			download(snake_case )

						__UpperCamelCase       =			MODELS_MAP[model_name]['''sample_rate''']
						__UpperCamelCase       =			MODELS_MAP[model_name]['''sample_size''']

						__UpperCamelCase       =			Object()
						__UpperCamelCase       =			sample_size
						__UpperCamelCase       =			sample_rate
						__UpperCamelCase       =			0

						__UpperCamelCase       =			UNetaDModel(sample_size=snake_case ,      sample_rate=snake_case )
						__UpperCamelCase       =			diffusers_model.state_dict()

						__UpperCamelCase       =			DiffusionUncond(snake_case )
						orig_model.load_state_dict(torch.load(args.model_path ,      map_location=snake_case )['''state_dict'''] )
						__UpperCamelCase       =			orig_model.diffusion_ema.eval()
						__UpperCamelCase       =			orig_model.state_dict()
						__UpperCamelCase       =			rename_orig_weights(snake_case )

						__UpperCamelCase       =			set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
						__UpperCamelCase       =			set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )

						assert len(snake_case ) == 0, f"Problem with {renamed_minus_diffusers}"
						assert all(k.endswith('''kernel''' ) for k in list(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":
																		__UpperCamelCase       =			value.squeeze()

												__UpperCamelCase       =			value

						diffusers_model.load_state_dict(snake_case )

						__UpperCamelCase       =			100
						__UpperCamelCase       =			33

						__UpperCamelCase       =			IPNDMScheduler(num_train_timesteps=snake_case )

						__UpperCamelCase       =			torch.manual_seed(snake_case )
						__UpperCamelCase       =			torch.randn([1, 2, config.sample_size] ,      generator=snake_case ).to(snake_case )

						__UpperCamelCase       =			torch.linspace(1 ,      0 ,      steps + 1 ,      device=snake_case )[:-1]
						__UpperCamelCase       =			get_crash_schedule(snake_case )

						__UpperCamelCase       =			DanceDiffusionPipeline(unet=snake_case ,      scheduler=snake_case )

						__UpperCamelCase       =			torch.manual_seed(33 )
						__UpperCamelCase       =			pipe(num_inference_steps=snake_case ,      generator=snake_case ).audios

						__UpperCamelCase       =			sampling.iplms_sample(snake_case ,      snake_case ,      snake_case ,      {} )
						__UpperCamelCase       =			generated.clamp(-1 ,      1 )

						__UpperCamelCase       =			(generated - audio).abs().sum()
						__UpperCamelCase       =			(generated - audio).abs().max()

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

						print('''Diff sum''' ,      snake_case )
						print('''Diff max''' ,      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__":
				lowercase__							:							Tuple									=			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.")
				lowercase__							:							Optional[Any]									=			parser.parse_args()

				main(args)






import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ASTForAudioClassification, ASTModel
				from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				)


if is_torchaudio_available():
				import torchaudio

				from transformers import ASTFeatureExtractor






class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=24     ,	SCREAMING_SNAKE_CASE_=16     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=2     ,	)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			parent
													__UpperCamelCase       =			batch_size
													__UpperCamelCase       =			patch_size
													__UpperCamelCase       =			max_length
													__UpperCamelCase       =			num_mel_bins
													__UpperCamelCase       =			is_training
													__UpperCamelCase       =			use_labels
													__UpperCamelCase       =			hidden_size
													__UpperCamelCase       =			num_hidden_layers
													__UpperCamelCase       =			num_attention_heads
													__UpperCamelCase       =			intermediate_size
													__UpperCamelCase       =			hidden_act
													__UpperCamelCase       =			hidden_dropout_prob
													__UpperCamelCase       =			attention_probs_dropout_prob
													__UpperCamelCase       =			type_sequence_label_size
													__UpperCamelCase       =			initializer_range
													__UpperCamelCase       =			scope
													__UpperCamelCase       =			frequency_stride
													__UpperCamelCase       =			time_stride

													# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
													__UpperCamelCase       =			(self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
													__UpperCamelCase       =			(self.max_length - self.patch_size) // self.time_stride + 1
													__UpperCamelCase       =			frequency_out_dimension * time_out_dimension
													__UpperCamelCase       =			num_patches + 2



							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]			)

													__UpperCamelCase       =			None
													if self.use_labels:
																			__UpperCamelCase       =			ids_tensor([self.batch_size]     ,	self.type_sequence_label_size			)

													__UpperCamelCase       =			self.get_config()

													return config, input_values, labels



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													return ASTConfig(
													    patch_size=self.patch_size     ,	max_length=self.max_length     ,	num_mel_bins=self.num_mel_bins     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	is_decoder=SCREAMING_SNAKE_CASE_     ,	initializer_range=self.initializer_range     ,	frequency_stride=self.frequency_stride     ,	time_stride=self.time_stride     ,	)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			ASTModel(config=SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.eval()
													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)
													self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			self.prepare_config_and_inputs()
													(
													    (
													    __UpperCamelCase
													)  ,							(
													    __UpperCamelCase
													)  ,							(
													    __UpperCamelCase
													)  ,							
													)       =			config_and_inputs
													__UpperCamelCase       =			{'''input_values''': input_values}
													return config, inputs_dict






@require_torch
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  (
							    (
							        ASTModel,
							        ASTForAudioClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_snake_case     		=  (
							    {'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
							    if is_torch_available()
							    else {}
							)
							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													if pipeline_test_casse_name == "AudioClassificationPipelineTests":
																			return True

													return False



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			ASTModelTester(self			)
													__UpperCamelCase       =			ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_     ,	hidden_size=37			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							@unittest.skip(reason='''AST does not use inputs_embeds'''			)
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													pass



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)
																			self.assertIsInstance(model.get_input_embeddings()     ,	(nn.Module)			)
																			__UpperCamelCase       =			model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_     ,	nn.Linear			)			)



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			inspect.signature(model.forward			)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__UpperCamelCase       =			[*signature.parameters.keys()]

																			__UpperCamelCase       =			['''input_values''']
																			self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)



							@slow
							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCamelCase       =			ASTModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)





def 	A_ (							)      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			hf_hub_download(
						    repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' ,      filename='''sample_audio.flac''' ,      repo_type='''dataset''' )

						__UpperCamelCase  ,							__UpperCamelCase       =			torchaudio.load(snake_case )

						return audio, sampling_rate






@require_torch
@require_torchaudio
class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							@cached_property
							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													return (
													    ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593'''			)
													    if is_torchaudio_available()
													    else None
													)



							@slow
							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			self.default_feature_extractor
													__UpperCamelCase       =			ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593'''			).to(SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			self.default_feature_extractor
													__UpperCamelCase  ,							__UpperCamelCase       =			prepare_audio()
													__UpperCamelCase       =			audio.squeeze().numpy()
													__UpperCamelCase       =			feature_extractor(SCREAMING_SNAKE_CASE_     ,	sampling_rate=SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''			).to(SCREAMING_SNAKE_CASE_			)

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)

													# verify the logits
													__UpperCamelCase       =			torch.Size((1, 527)			)
													self.assertEqual(outputs.logits.shape     ,	SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2]			).to(SCREAMING_SNAKE_CASE_			)

													self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-4			)			)




