



"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowercase  							=					[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]




def 	UpperCAmelCase     (		A	:						Optional[Any]     ,				A	:						Optional[Any]     ,				A	:						List[str]     ,				A	:						Dict     ,				A	:						Tuple       ):

		'''simple docstring'''


		for attribute in key.split('.'       ):
				_UpperCAmelCase							 =		getattr(A     ,				A       )

		if weight_type is not None:
				_UpperCAmelCase							 =		getattr(A     ,				A       ).shape
		else:
				_UpperCAmelCase							 =		hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
				    f' {value.shape} for {full_name}'       )

		if weight_type == "weight":
				_UpperCAmelCase							 =		value
		elif weight_type == "weight_g":
				_UpperCAmelCase							 =		value
		elif weight_type == "weight_v":
				_UpperCAmelCase							 =		value
		elif weight_type == "bias":
				_UpperCAmelCase							 =		value
		elif weight_type == "running_mean":
				_UpperCAmelCase							 =		value
		elif weight_type == "running_var":
				_UpperCAmelCase							 =		value
		elif weight_type == "num_batches_tracked":
				_UpperCAmelCase							 =		value
		elif weight_type == "inv_freq":
				_UpperCAmelCase							 =		value
		else:
				_UpperCAmelCase							 =		value

		logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'       )




def 	UpperCAmelCase     (		A	:						Any     ,				A	:						List[Any]     ,				A	:						Union[str, Any]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		[]
		_UpperCAmelCase							 =		fairseq_model.state_dict()

		_UpperCAmelCase							 =		hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				_UpperCAmelCase							 =		False
				if "conv_layers" in name:
						load_conv_layer(
						    A     ,				A     ,				A     ,				A     ,				hf_model.config.feat_extract_norm == 'group'     ,				)
						_UpperCAmelCase							 =		True
				else:
						for key, mapped_key in MAPPING.items():
								_UpperCAmelCase							 =		'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split('w2v_model.'       )[-1] == name.split('.'       )[0]:
										_UpperCAmelCase							 =		True
										if "*" in mapped_key:
												_UpperCAmelCase							 =		name.split(A       )[0].split('.'       )[-2]
												_UpperCAmelCase							 =		mapped_key.replace('*'     ,				A       )
										if "pos_bias_u" in name:
												_UpperCAmelCase							 =		None
										elif "pos_bias_v" in name:
												_UpperCAmelCase							 =		None
										elif "weight_g" in name:
												_UpperCAmelCase							 =		'weight_g'
										elif "weight_v" in name:
												_UpperCAmelCase							 =		'weight_v'
										elif "bias" in name:
												_UpperCAmelCase							 =		'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												_UpperCAmelCase							 =		'weight'
										elif "running_mean" in name:
												_UpperCAmelCase							 =		'running_mean'
										elif "inv_freq" in name:
												_UpperCAmelCase							 =		'inv_freq'
										elif "running_var" in name:
												_UpperCAmelCase							 =		'running_var'
										elif "num_batches_tracked" in name:
												_UpperCAmelCase							 =		'num_batches_tracked'
										else:
												_UpperCAmelCase							 =		None
										set_recursively(A     ,				A     ,				A     ,				A     ,				A       )
								continue
				if not is_used:
						unused_weights.append(A       )

		logger.warning(f'Unused weights: {unused_weights}'       )




def 	UpperCAmelCase     (		A	:						Dict     ,				A	:						List[Any]     ,				A	:						List[Any]     ,				A	:						Dict     ,				A	:						Optional[Any]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		full_name.split('conv_layers.'       )[-1]
		_UpperCAmelCase							 =		name.split('.'       )
		_UpperCAmelCase							 =		int(items[0]       )
		_UpperCAmelCase							 =		int(items[1]       )

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    f'{full_name} has size {value.shape}, but'
								    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'       )
						_UpperCAmelCase							 =		value
						logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    f'{full_name} has size {value.shape}, but'
								    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'       )
						_UpperCAmelCase							 =		value
						logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    f'{full_name} has size {value.shape}, but'
								    f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'       )
						_UpperCAmelCase							 =		value
						logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    f'{full_name} has size {value.shape}, but'
								    f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'       )
						_UpperCAmelCase							 =		value
						logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
		else:
				unused_weights.append(A       )




@torch.no_grad()
def 	UpperCAmelCase     (		A	:						List[str]     ,				A	:						Dict     ,				A	:						Dict=None     ,				A	:						Optional[Any]=None     ,				A	:						int=True       ):

		'''simple docstring'''


		if config_path is not None:
				_UpperCAmelCase							 =		WavaVecaConformerConfig.from_pretrained(A     ,				hidden_act='swish'       )
		else:
				_UpperCAmelCase							 =		WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				_UpperCAmelCase							 =		'rotary'

		if is_finetuned:
				if dict_path:
						_UpperCAmelCase							 =		Dictionary.load(A       )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						_UpperCAmelCase							 =		target_dict.pad_index
						_UpperCAmelCase							 =		target_dict.bos_index
						_UpperCAmelCase							 =		target_dict.eos_index
						_UpperCAmelCase							 =		len(target_dict.symbols       )
						_UpperCAmelCase							 =		os.path.join(A     ,				'vocab.json'       )
						if not os.path.isdir(A       ):
								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A       )       )
								return
						os.makedirs(A     ,				exist_ok=A       )
						_UpperCAmelCase							 =		target_dict.indices

						# fairseq has the <pad> and <s> switched
						_UpperCAmelCase							 =		0
						_UpperCAmelCase							 =		1
						with open(A     ,				'w'     ,				encoding='utf-8'       ) as vocab_handle:
								json.dump(A     ,				A       )
						_UpperCAmelCase							 =		WavaVecaCTCTokenizer(
						    A     ,				unk_token=target_dict.unk_word     ,				pad_token=target_dict.pad_word     ,				bos_token=target_dict.bos_word     ,				eos_token=target_dict.eos_word     ,				word_delimiter_token='|'     ,				do_lower_case=A     ,				)
						_UpperCAmelCase							 =		True if config.feat_extract_norm == 'layer' else False
						_UpperCAmelCase							 =		WavaVecaFeatureExtractor(
						    feature_size=1     ,				sampling_rate=1_6000     ,				padding_value=0     ,				do_normalize=A     ,				return_attention_mask=A     ,				)
						_UpperCAmelCase							 =		WavaVecaProcessor(feature_extractor=A     ,				tokenizer=A       )
						processor.save_pretrained(A       )

				_UpperCAmelCase							 =		WavaVecaConformerForCTC(A       )
		else:
				_UpperCAmelCase							 =		WavaVecaConformerForPreTraining(A       )

		if is_finetuned:
				_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]     ,				arg_overrides={'data': '/'.join(dict_path.split('/'       )[:-1]       )}       )
		else:
				_UpperCAmelCase							 =		argparse.Namespace(task='audio_pretraining'       )
				_UpperCAmelCase							 =		fairseq.tasks.setup_task(A       )

				_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,				task=A       )

		_UpperCAmelCase							 =		model[0].eval()

		recursively_load_weights(A     ,				A     ,				not is_finetuned       )

		hf_wavavec.save_pretrained(A       )


if __name__ == "__main__":
					lowercase  							=					argparse.ArgumentParser()
					parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
					parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
					parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
					parser.add_argument(
					    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
					)
					lowercase  							=					parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)







"""simple docstring"""





import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					DiTPipeline
							_UpperCAmelCase      				=					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							_UpperCAmelCase      				=					PipelineTesterMixin.required_optional_params - {
							    '''latents''',
							    '''num_images_per_prompt''',
							    '''callback''',
							    '''callback_steps''',
							}
							_UpperCAmelCase      				=					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							_UpperCAmelCase      				=					False






							def        lowerCamelCase_   (  self						)			->							str:
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		TransformeraDModel(
									    sample_size=16	,       num_layers=2	,       patch_size=4	,       attention_head_dim=8	,       num_attention_heads=2	,       in_channels=4	,       out_channels=8	,       attention_bias=snake_case	,       activation_fn='gelu-approximate'	,       num_embeds_ada_norm=1000	,       norm_type='ada_norm_zero'	,       norm_elementwise_affine=snake_case	,       )
									_UpperCAmelCase							 =		AutoencoderKL()
									_UpperCAmelCase							 =		DDIMScheduler()
									_UpperCAmelCase							 =		{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
									return components






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=0						)			->							Optional[Any]:
									if str(snake_case						).startswith('mps'						):
											_UpperCAmelCase							 =		torch.manual_seed(snake_case						)
									else:
											_UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(snake_case						)
									_UpperCAmelCase							 =		{
									    'class_labels': [1],
									    'generator': generator,
									    'num_inference_steps': 2,
									    'output_type': 'numpy',
									}
									return inputs






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		'cpu'

									_UpperCAmelCase							 =		self.get_dummy_components()
									_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
									pipe.to(snake_case						)
									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case						)
									_UpperCAmelCase							 =		pipe(**snake_case						).images
									_UpperCAmelCase							 =		image[0, -3:, -3:, -1]

									self.assertEqual(image.shape	,       (1, 16, 16, 3)						)
									_UpperCAmelCase							 =		np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]						)
									_UpperCAmelCase							 =		np.abs(image_slice.flatten() - expected_slice						).max()
									self.assertLessEqual(snake_case	,       1E-3						)






							def        lowerCamelCase_   (  self						)			->							Any:
									self._test_inference_batch_single_identical(relax_max_difference=snake_case	,       expected_max_diff=1E-3						)






							@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[int]:
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)







@require_torch_gpu
@slow
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							int:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		torch.manual_seed(0						)

									_UpperCAmelCase							 =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'						)
									pipe.to('cuda'						)

									_UpperCAmelCase							 =		['vase', 'umbrella', 'white shark', 'white wolf']
									_UpperCAmelCase							 =		pipe.get_label_ids(snake_case						)

									_UpperCAmelCase							 =		pipe(snake_case	,       generator=snake_case	,       num_inference_steps=40	,       output_type='np'						).images

									for word, image in zip(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		load_numpy(
											    f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'						)
											assert np.abs((expected_image - image).max()						) < 1E-2






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'						)
									_UpperCAmelCase							 =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
									pipe.to('cuda'						)

									_UpperCAmelCase							 =		['vase', 'umbrella']
									_UpperCAmelCase							 =		pipe.get_label_ids(snake_case						)

									_UpperCAmelCase							 =		torch.manual_seed(0						)
									_UpperCAmelCase							 =		pipe(snake_case	,       generator=snake_case	,       num_inference_steps=25	,       output_type='np'						).images

									for word, image in zip(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    f'/dit/{word}_512.npy'						)

											assert np.abs((expected_image - image).max()						) < 1E-1


