

"""simple docstring"""





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 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       AudioLDMPipeline
					lowercase__				 =       TEXT_TO_AUDIO_PARAMS
					lowercase__				 =       TEXT_TO_AUDIO_BATCH_PARAMS
					lowercase__				 =       frozenset(
					    [
					        "num_inference_steps",
					        "num_waveforms_per_prompt",
					        "generator",
					        "latents",
					        "output_type",
					        "return_dict",
					        "callback",
					        "callback_steps",
					    ]       )







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						torch.manual_seed(0		)
						lowerCAmelCase__	:						List[str]  				=	UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)   ,layers_per_block=2   ,sample_size=3_2   ,in_channels=4   ,out_channels=4   ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,cross_attention_dim=(3_2, 6_4)   ,class_embed_type='''simple_projection'''   ,projection_class_embeddings_input_dim=3_2   ,class_embeddings_concat=lowercase_   ,)
						lowerCAmelCase__	:						List[Any]  				=	DDIMScheduler(
						    beta_start=0.0_0085   ,beta_end=0.012   ,beta_schedule='''scaled_linear'''   ,clip_sample=lowercase_   ,set_alpha_to_one=lowercase_   ,)
						torch.manual_seed(0		)
						lowerCAmelCase__	:						Any  				=	AutoencoderKL(
						    block_out_channels=[3_2, 6_4]   ,in_channels=1   ,out_channels=1   ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,latent_channels=4   ,)
						torch.manual_seed(0		)
						lowerCAmelCase__	:						Dict  				=	ClapTextConfig(
						    bos_token_id=0   ,eos_token_id=2   ,hidden_size=3_2   ,intermediate_size=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_0_0_0   ,projection_dim=3_2   ,)
						lowerCAmelCase__	:						int  				=	ClapTextModelWithProjection(lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta'''   ,model_max_length=7_7		)

						lowerCAmelCase__	:						Union[str, Any]  				=	SpeechTaHifiGanConfig(
						    model_in_dim=8   ,sampling_rate=1_6_0_0_0   ,upsample_initial_channel=1_6   ,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=lowercase_   ,)

						lowerCAmelCase__	:						Any  				=	SpeechTaHifiGan(lowercase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	{
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''vae''': vae,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''vocoder''': vocoder,
						}
						return components







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				Dict   ,lowercase_			:				Optional[Any]=0		):
						if str(lowercase_		).startswith('''mps'''		):
							lowerCAmelCase__	:						Optional[Any]  				=	torch.manual_seed(lowercase_		)
						else:
							lowerCAmelCase__	:						Union[str, Any]  				=	torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	{
						    '''prompt''': '''A hammer hitting a wooden surface''',
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''guidance_scale''': 6.0,
						}
						return inputs







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						int  				=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator

						lowerCAmelCase__	:						Tuple  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Optional[Any]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						str  				=	output.audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) == 2_5_6

						lowerCAmelCase__	:						str  				=	audio[:1_0]
						lowerCAmelCase__	:						str  				=	np.array(
						    [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]		)

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







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Any  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						int  				=	audioldm_pipe.to(lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Tuple  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	3 * [inputs['''prompt''']]

						# forward
						lowerCAmelCase__	:						int  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	output.audios[0]

						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						str  				=	3 * [inputs.pop('''prompt'''		)]

						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe.tokenizer(
						    lowercase_   ,padding='''max_length'''   ,max_length=audioldm_pipe.tokenizer.model_max_length   ,truncation=lowercase_   ,return_tensors='''pt'''   ,)
						lowerCAmelCase__	:						Tuple  				=	text_inputs['''input_ids'''].to(lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe.text_encoder(
						    lowercase_   ,)
						lowerCAmelCase__	:						Tuple  				=	prompt_embeds.text_embeds
						# additional L_2 normalization over each hidden-state
						lowerCAmelCase__	:						Tuple  				=	F.normalize(lowercase_   ,dim=-1		)

						lowerCAmelCase__	:						List[Any]  				=	prompt_embeds

						# forward
						lowerCAmelCase__	:						Union[str, Any]  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						Any  				=	output.audios[0]

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







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_dummy_components()
						lowerCAmelCase__	:						List[Any]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	audioldm_pipe.to(lowercase_		)
						lowerCAmelCase__	:						Dict  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						str  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						int  				=	3 * ['''this is a negative prompt''']
						lowerCAmelCase__	:						Any  				=	negative_prompt
						lowerCAmelCase__	:						int  				=	3 * [inputs['''prompt''']]

						# forward
						lowerCAmelCase__	:						Tuple  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	output.audios[0]

						lowerCAmelCase__	:						Optional[Any]  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	3 * [inputs.pop('''prompt'''		)]

						lowerCAmelCase__	:						Optional[Any]  				=	[]
						for p in [prompt, negative_prompt]:
							lowerCAmelCase__	:						int  				=	audioldm_pipe.tokenizer(
							    lowercase_   ,padding='''max_length'''   ,max_length=audioldm_pipe.tokenizer.model_max_length   ,truncation=lowercase_   ,return_tensors='''pt'''   ,)
							lowerCAmelCase__	:						Optional[Any]  				=	text_inputs['''input_ids'''].to(lowercase_		)

							lowerCAmelCase__	:						Dict  				=	audioldm_pipe.text_encoder(
							    lowercase_   ,)
							lowerCAmelCase__	:						Dict  				=	text_embeds.text_embeds
							# additional L_2 normalization over each hidden-state
							lowerCAmelCase__	:						Any  				=	F.normalize(lowercase_   ,dim=-1		)

							embeds.append(lowercase_		)

						lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	embeds

						# forward
						lowerCAmelCase__	:						List[str]  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	output.audios[0]

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







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Dict  				=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__	:						Optional[int]  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Union[str, Any]  				=	PNDMScheduler(skip_prk_steps=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Dict  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	'''egg cracking'''
						lowerCAmelCase__	:						Tuple  				=	audioldm_pipe(**lowercase_   ,negative_prompt=lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	output.audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) == 2_5_6

						lowerCAmelCase__	:						Any  				=	audio[:1_0]
						lowerCAmelCase__	:						List[Any]  				=	np.array(
						    [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]		)

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







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						str  				=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__	:						Tuple  				=	self.get_dummy_components()
						lowerCAmelCase__	:						int  				=	PNDMScheduler(skip_prk_steps=lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	'''A hammer hitting a wooden surface'''

						# test num_waveforms_per_prompt=1 (default)
						lowerCAmelCase__	:						Dict  				=	audioldm_pipe(lowercase_   ,num_inference_steps=2		).audios

						assert audios.shape == (1, 2_5_6)

						# test num_waveforms_per_prompt=1 (default) for batch of prompts
						lowerCAmelCase__	:						List[Any]  				=	2
						lowerCAmelCase__	:						Union[str, Any]  				=	audioldm_pipe([prompt] * batch_size   ,num_inference_steps=2		).audios

						assert audios.shape == (batch_size, 2_5_6)

						# test num_waveforms_per_prompt for single prompt
						lowerCAmelCase__	:						Dict  				=	2
						lowerCAmelCase__	:						int  				=	audioldm_pipe(lowercase_   ,num_inference_steps=2   ,num_waveforms_per_prompt=lowercase_		).audios

						assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

						# test num_waveforms_per_prompt for batch of prompts
						lowerCAmelCase__	:						Optional[int]  				=	2
						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe(
						    [prompt] * batch_size   ,num_inference_steps=2   ,num_waveforms_per_prompt=lowercase_		).audios

						assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Optional[int]  				=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__	:						Optional[int]  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Optional[Any]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)
						lowerCAmelCase__	:						Any  				=	audioldm_pipe.vocoder.config.sampling_rate

						lowerCAmelCase__	:						Any  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						Dict  				=	audioldm_pipe(audio_length_in_s=0.016   ,**lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	output.audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) / vocoder_sampling_rate == 0.016

						lowerCAmelCase__	:						Union[str, Any]  				=	audioldm_pipe(audio_length_in_s=0.032   ,**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	output.audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) / vocoder_sampling_rate == 0.032







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						int  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Tuple  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						str  				=	['''hey''']

						lowerCAmelCase__	:						str  				=	audioldm_pipe(lowercase_   ,num_inference_steps=1		)
						lowerCAmelCase__	:						str  				=	output.audios.shape
						assert audio_shape == (1, 2_5_6)

						lowerCAmelCase__	:						Tuple  				=	audioldm_pipe.vocoder.config
						config.model_in_dim *= 2
						lowerCAmelCase__	:						int  				=	SpeechTaHifiGan(lowercase_		).to(lowercase_		)
						lowerCAmelCase__	:						str  				=	audioldm_pipe(lowercase_   ,num_inference_steps=1		)
						lowerCAmelCase__	:						Dict  				=	output.audios.shape
						# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
						assert audio_shape == (1, 2_5_6)







					def        __lowerCAmelCase (       self			:				Any		):
						self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict		):
						self._test_inference_batch_single_identical(test_mean_pixel_difference=lowercase_		)







					@unittest.skipIf(
					    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowercase_		)







@slow
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def        __lowerCAmelCase (       self			:				str		):
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				Any   ,lowercase_			:				Union[str, Any]="cpu"   ,lowercase_			:				Any=torch.floataa   ,lowercase_			:				Tuple=0		):
						lowerCAmelCase__	:						str  				=	torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
						lowerCAmelCase__	:						str  				=	np.random.RandomState(lowercase_		).standard_normal((1, 8, 1_2_8, 1_6)		)
						lowerCAmelCase__	:						str  				=	torch.from_numpy(lowercase_		).to(device=lowercase_   ,dtype=lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	{
						    '''prompt''': '''A hammer hitting a wooden surface''',
						    '''latents''': latents,
						    '''generator''': generator,
						    '''num_inference_steps''': 3,
						    '''guidance_scale''': 2.5,
						}
						return inputs







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Union[str, Any]  				=	AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''		)
						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_inputs(lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	2_5
						lowerCAmelCase__	:						Optional[Any]  				=	audioldm_pipe(**lowercase_		).audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) == 8_1_9_2_0

						lowerCAmelCase__	:						Optional[Any]  				=	audio[7_7_2_3_0:7_7_2_4_0]
						lowerCAmelCase__	:						Optional[Any]  				=	np.array(
						    [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]		)
						lowerCAmelCase__	:						List[str]  				=	np.abs(expected_slice - audio_slice		).max()
						assert max_diff < 1E-2







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						Optional[int]  				=	AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''		)
						lowerCAmelCase__	:						Any  				=	LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config		)
						lowerCAmelCase__	:						str  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	self.get_inputs(lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	audioldm_pipe(**lowercase_		).audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) == 8_1_9_2_0

						lowerCAmelCase__	:						List[Any]  				=	audio[2_7_7_8_0:2_7_7_9_0]
						lowerCAmelCase__	:						Any  				=	np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]		)
						lowerCAmelCase__	:						Tuple  				=	np.abs(expected_slice - audio_slice		).max()
						assert max_diff < 3E-2






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       if "cls_token" in name:
              lowercase							:     List[Any]       	=      name.replace("""cls_token"""					,	"""vit.embeddings.cls_token"""				)
       if "mask_token" in name:
              lowercase							:     Any       	=      name.replace("""mask_token"""					,	"""decoder.mask_token"""				)
       if "decoder_pos_embed" in name:
              lowercase							:     str       	=      name.replace("""decoder_pos_embed"""					,	"""decoder.decoder_pos_embed"""				)
       if "pos_embed" in name and "decoder" not in name:
              lowercase							:     List[str]       	=      name.replace("""pos_embed"""					,	"""vit.embeddings.position_embeddings"""				)
       if "patch_embed.proj" in name:
              lowercase							:     Tuple       	=      name.replace("""patch_embed.proj"""					,	"""vit.embeddings.patch_embeddings.projection"""				)
       if "patch_embed.norm" in name:
              lowercase							:     int       	=      name.replace("""patch_embed.norm"""					,	"""vit.embeddings.norm"""				)
       if "decoder_blocks" in name:
              lowercase							:     Tuple       	=      name.replace("""decoder_blocks"""					,	"""decoder.decoder_layers"""				)
       if "blocks" in name:
              lowercase							:     List[Any]       	=      name.replace("""blocks"""					,	"""vit.encoder.layer"""				)
       if "attn.proj" in name:
              lowercase							:     List[str]       	=      name.replace("""attn.proj"""					,	"""attention.output.dense"""				)
       if "attn" in name:
              lowercase							:     Union[str, Any]       	=      name.replace("""attn"""					,	"""attention.self"""				)
       if "norm1" in name:
              lowercase							:     Optional[Any]       	=      name.replace("""norm1"""					,	"""layernorm_before"""				)
       if "norm2" in name:
              lowercase							:     Union[str, Any]       	=      name.replace("""norm2"""					,	"""layernorm_after"""				)
       if "mlp.fc1" in name:
              lowercase							:     Dict       	=      name.replace("""mlp.fc1"""					,	"""intermediate.dense"""				)
       if "mlp.fc2" in name:
              lowercase							:     Dict       	=      name.replace("""mlp.fc2"""					,	"""output.dense"""				)
       if "decoder_embed" in name:
              lowercase							:     List[str]       	=      name.replace("""decoder_embed"""					,	"""decoder.decoder_embed"""				)
       if "decoder_norm" in name:
              lowercase							:     Dict       	=      name.replace("""decoder_norm"""					,	"""decoder.decoder_norm"""				)
       if "decoder_pred" in name:
              lowercase							:     List[str]       	=      name.replace("""decoder_pred"""					,	"""decoder.decoder_pred"""				)
       if "norm.weight" in name and "decoder" not in name:
              lowercase							:     Tuple       	=      name.replace("""norm.weight"""					,	"""vit.layernorm.weight"""				)
       if "norm.bias" in name and "decoder" not in name:
              lowercase							:     int       	=      name.replace("""norm.bias"""					,	"""vit.layernorm.bias"""				)

       return name







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       for key in orig_state_dict.copy().keys():
              lowercase							:     List[Any]       	=      orig_state_dict.pop(SCREAMING_SNAKE_CASE__				)

              if "qkv" in key:
                     lowercase							:     int       	=      key.split("""."""				)
                     lowercase							:     List[str]       	=      int(key_split[1]				)
                     if "decoder_blocks" in key:
                            lowercase							:     Tuple       	=      config.decoder_hidden_size
                            lowercase							:     int       	=      """decoder.decoder_layers."""
                            if "weight" in key:
                                   lowercase							:     List[Any]       	=      val[:dim, :]
                                   lowercase							:     Tuple       	=      val[dim : dim * 2, :]
                                   lowercase							:     List[Any]       	=      val[-dim:, :]
                            elif "bias" in key:
                                   lowercase							:     str       	=      val[:dim]
                                   lowercase							:     Dict       	=      val[dim : dim * 2]
                                   lowercase							:     Union[str, Any]       	=      val[-dim:]
                     else:
                            lowercase							:     Tuple       	=      config.hidden_size
                            lowercase							:     Union[str, Any]       	=      """vit.encoder.layer."""
                            if "weight" in key:
                                   lowercase							:     Tuple       	=      val[:dim, :]
                                   lowercase							:     List[str]       	=      val[dim : dim * 2, :]
                                   lowercase							:     Dict       	=      val[-dim:, :]
                            elif "bias" in key:
                                   lowercase							:     Any       	=      val[:dim]
                                   lowercase							:     str       	=      val[dim : dim * 2]
                                   lowercase							:     Union[str, Any]       	=      val[-dim:]

              else:
                     lowercase							:     Union[str, Any]       	=      val

       return orig_state_dict







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     int       	=      ViTMAEConfig()
       if "large" in checkpoint_url:
              lowercase							:     Dict       	=      1_024
              lowercase							:     str       	=      4_096
              lowercase							:     Optional[Any]       	=      24
              lowercase							:     Optional[Any]       	=      16
       elif "huge" in checkpoint_url:
              lowercase							:     int       	=      14
              lowercase							:     List[Any]       	=      1_280
              lowercase							:     int       	=      5_120
              lowercase							:     List[Any]       	=      32
              lowercase							:     Any       	=      16

       lowercase							:     List[str]       	=      ViTMAEForPreTraining(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__					,	map_location="""cpu"""				)["""model"""]

       lowercase							:     Tuple       	=      ViTMAEImageProcessor(size=config.image_size				)

       lowercase							:     Optional[int]       	=      convert_state_dict(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       model.load_state_dict(SCREAMING_SNAKE_CASE__				)
       model.eval()

       lowercase							:     Union[str, Any]       	=      """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

       lowercase							:     Union[str, Any]       	=      Image.open(requests.get(SCREAMING_SNAKE_CASE__					,	stream=SCREAMING_SNAKE_CASE__				).raw				)
       lowercase							:     Optional[Any]       	=      ViTMAEImageProcessor(size=config.image_size				)
       lowercase							:     List[Any]       	=      image_processor(images=SCREAMING_SNAKE_CASE__					,	return_tensors="""pt"""				)

       # forward pass
       torch.manual_seed(2				)
       lowercase							:     int       	=      model(**SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      outputs.logits

       if "large" in checkpoint_url:
              lowercase							:     List[Any]       	=      torch.tensor(
                  [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]				)
       elif "huge" in checkpoint_url:
              lowercase							:     Tuple       	=      torch.tensor(
                  [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]				)
       else:
              lowercase							:     List[str]       	=      torch.tensor(
                  [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]				)

       # verify logits
       assert torch.allclose(logits[0, :3, :3]					,	SCREAMING_SNAKE_CASE__					,	atol=1e-4				)

       print(f"Saving model to {pytorch_dump_folder_path}"				)
       model.save_pretrained(SCREAMING_SNAKE_CASE__				)

       print(f"Saving image processor to {pytorch_dump_folder_path}"				)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  lowercase  :					Union[str, Any]						    =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  lowercase  :					List[Any]						    =							parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
