






"""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()
lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''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''',
}
lowerCamelCase_       			=   [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def        snake_case   (     A__			,A__			,A__			,A__			,A__		):
			for attribute in key.split("."		):
						UpperCAmelCase_  :				str      =      getattr(A__			,A__		)

			if weight_type is not None:
						UpperCAmelCase_  :				List[str]      =      getattr(A__			,A__		).shape
			else:
						UpperCAmelCase_  :				str      =      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_  :				List[str]      =      value
			elif weight_type == "weight_g":
						UpperCAmelCase_  :				Tuple      =      value
			elif weight_type == "weight_v":
						UpperCAmelCase_  :				str      =      value
			elif weight_type == "bias":
						UpperCAmelCase_  :				List[str]      =      value
			elif weight_type == "running_mean":
						UpperCAmelCase_  :				Union[str, Any]      =      value
			elif weight_type == "running_var":
						UpperCAmelCase_  :				str      =      value
			elif weight_type == "num_batches_tracked":
						UpperCAmelCase_  :				Optional[Any]      =      value
			elif weight_type == "inv_freq":
						UpperCAmelCase_  :				Optional[int]      =      value
			else:
						UpperCAmelCase_  :				int      =      value

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

def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				List[str]      =      []
			UpperCAmelCase_  :				Any      =      fairseq_model.state_dict()

			UpperCAmelCase_  :				int      =      hf_model.wavaveca_conformer.feature_extractor

			for name, value in fairseq_dict.items():
						UpperCAmelCase_  :				Any      =      False
						if "conv_layers" in name:
									load_conv_layer(
									    A__			,A__			,A__			,A__			,hf_model.config.feat_extract_norm == "group"			,)
									UpperCAmelCase_  :				int      =      True
						else:
									for key, mapped_key in MAPPING.items():
												UpperCAmelCase_  :				Dict      =      "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_  :				List[str]      =      True
															if "*" in mapped_key:
																		UpperCAmelCase_  :				Optional[Any]      =      name.split(A__		)[0].split("."		)[-2]
																		UpperCAmelCase_  :				List[str]      =      mapped_key.replace("*"			,A__		)
															if "pos_bias_u" in name:
																		UpperCAmelCase_  :				Any      =      None
															elif "pos_bias_v" in name:
																		UpperCAmelCase_  :				List[str]      =      None
															elif "weight_g" in name:
																		UpperCAmelCase_  :				List[str]      =      "weight_g"
															elif "weight_v" in name:
																		UpperCAmelCase_  :				str      =      "weight_v"
															elif "bias" in name:
																		UpperCAmelCase_  :				int      =      "bias"
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		UpperCAmelCase_  :				Union[str, Any]      =      "weight"
															elif "running_mean" in name:
																		UpperCAmelCase_  :				List[Any]      =      "running_mean"
															elif "inv_freq" in name:
																		UpperCAmelCase_  :				Optional[int]      =      "inv_freq"
															elif "running_var" in name:
																		UpperCAmelCase_  :				List[str]      =      "running_var"
															elif "num_batches_tracked" in name:
																		UpperCAmelCase_  :				int      =      "num_batches_tracked"
															else:
																		UpperCAmelCase_  :				List[str]      =      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        snake_case   (     A__			,A__			,A__			,A__			,A__		):
			UpperCAmelCase_  :				Optional[Any]      =      full_name.split("conv_layers."		)[-1]
			UpperCAmelCase_  :				Optional[Any]      =      name.split("."		)
			UpperCAmelCase_  :				str      =      int(items[0]		)
			UpperCAmelCase_  :				int      =      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_  :				Optional[int]      =      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_  :				Tuple      =      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_  :				List[str]      =      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_  :				Tuple      =      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        snake_case   (     A__			,A__			,A__=None			,A__=None			,A__=True		):
			if config_path is not None:
						UpperCAmelCase_  :				Any      =      WavaVecaConformerConfig.from_pretrained(A__			,hidden_act="swish"		)
			else:
						UpperCAmelCase_  :				Any      =      WavaVecaConformerConfig()

			if "rope" in checkpoint_path:
						UpperCAmelCase_  :				List[str]      =      "rotary"

			if is_finetuned:
						if dict_path:
									UpperCAmelCase_  :				int      =      Dictionary.load(A__		)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									UpperCAmelCase_  :				Tuple      =      target_dict.pad_index
									UpperCAmelCase_  :				Dict      =      target_dict.bos_index
									UpperCAmelCase_  :				int      =      target_dict.eos_index
									UpperCAmelCase_  :				Dict      =      len(target_dict.symbols		)
									UpperCAmelCase_  :				Optional[int]      =      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_  :				int      =      target_dict.indices

									# fairseq has the <pad> and <s> switched
									UpperCAmelCase_  :				Any      =      0
									UpperCAmelCase_  :				List[str]      =      1
									with open(A__			,"w"			,encoding="utf-8"		) as vocab_handle:
												json.dump(A__			,A__		)
									UpperCAmelCase_  :				Any      =      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_  :				List[str]      =      True if config.feat_extract_norm == "layer" else False
									UpperCAmelCase_  :				int      =      WavaVecaFeatureExtractor(
									    feature_size=1			,sampling_rate=1_60_00			,padding_value=0			,do_normalize=A__			,return_attention_mask=A__			,)
									UpperCAmelCase_  :				Any      =      WavaVecaProcessor(feature_extractor=A__			,tokenizer=A__		)
									processor.save_pretrained(A__		)

						UpperCAmelCase_  :				Union[str, Any]      =      WavaVecaConformerForCTC(A__		)
			else:
						UpperCAmelCase_  :				Optional[Any]      =      WavaVecaConformerForPreTraining(A__		)

			if is_finetuned:
						UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				Optional[Any]      =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]			,arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
			else:
						UpperCAmelCase_  :				Union[str, Any]      =      argparse.Namespace(task="audio_pretraining"		)
						UpperCAmelCase_  :				Dict      =      fairseq.tasks.setup_task(A__		)

						UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				Any      =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,task=A__		)

			UpperCAmelCase_  :				str      =      model[0].eval()

			recursively_load_weights(A__			,A__			,not is_finetuned		)

			hf_wavavec.save_pretrained(A__		)


if __name__ == "__main__":
			lowerCamelCase_       			=   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'''
			)
			lowerCamelCase_       			=   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 transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference







class     UpperCamelCase_ (__A       ,		unittest.TestCase  ):
	__magic_name__			   =       ShapEPipeline
	__magic_name__			   =       ['''prompt''']
	__magic_name__			   =       ['''prompt''']
	__magic_name__			   =       [
	    '''num_images_per_prompt''',
	    '''num_inference_steps''',
	    '''generator''',
	    '''latents''',
	    '''guidance_scale''',
	    '''frame_size''',
	    '''output_type''',
	    '''return_dict''',
	]
	__magic_name__			   =       False





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Dict:
				return 32





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Tuple:
				return 32





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Optional[int]:
				return self.time_input_dim * 4





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[Any]:
				return 8





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    int:
				UpperCAmelCase_  :				str      =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
				return tokenizer





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[str]:
				torch.manual_seed(0		)
				UpperCAmelCase_  :				Dict      =      CLIPTextConfig(
				    bos_token_id=0			,			eos_token_id=2			,			hidden_size=self.text_embedder_hidden_size			,			projection_dim=self.text_embedder_hidden_size			,			intermediate_size=37			,			layer_norm_eps=1e-05			,			num_attention_heads=4			,			num_hidden_layers=5			,			pad_token_id=1			,			vocab_size=1_000			,			)
				return CLIPTextModelWithProjection(lowerCAmelCase_		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Optional[Any]:
				torch.manual_seed(0		)

				UpperCAmelCase_  :				str      =      {
				    "num_attention_heads": 2,
				    "attention_head_dim": 16,
				    "embedding_dim": self.time_input_dim,
				    "num_embeddings": 32,
				    "embedding_proj_dim": self.text_embedder_hidden_size,
				    "time_embed_dim": self.time_embed_dim,
				    "num_layers": 1,
				    "clip_embed_dim": self.time_input_dim * 2,
				    "additional_embeddings": 0,
				    "time_embed_act_fn": "gelu",
				    "norm_in_type": "layer",
				    "encoder_hid_proj_type": None,
				    "added_emb_type": None,
				}

				UpperCAmelCase_  :				List[Any]      =      PriorTransformer(**lowerCAmelCase_		)
				return model





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    int:
				torch.manual_seed(0		)

				UpperCAmelCase_  :				Any      =      {
				    "param_shapes": (
				        (self.renderer_dim, 93),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				    ),
				    "d_latent": self.time_input_dim,
				    "d_hidden": self.renderer_dim,
				    "n_output": 12,
				    "background": (
				        0.1,
				        0.1,
				        0.1,
				    ),
				}
				UpperCAmelCase_  :				Tuple      =      ShapERenderer(**lowerCAmelCase_		)
				return model





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Tuple:
				UpperCAmelCase_  :				str      =      self.dummy_prior
				UpperCAmelCase_  :				Dict      =      self.dummy_text_encoder
				UpperCAmelCase_  :				Any      =      self.dummy_tokenizer
				UpperCAmelCase_  :				int      =      self.dummy_renderer

				UpperCAmelCase_  :				Optional[int]      =      HeunDiscreteScheduler(
				    beta_schedule="exp"			,			num_train_timesteps=1_024			,			prediction_type="sample"			,			use_karras_sigmas=lowerCAmelCase_			,			clip_sample=lowerCAmelCase_			,			clip_sample_range=1.0			,			)
				UpperCAmelCase_  :				str      =      {
				    "prior": prior,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "renderer": renderer,
				    "scheduler": scheduler,
				}

				return components





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Any=0		)		->    Union[str, Any]:
				if str(lowerCAmelCase_		).startswith("mps"		):
							UpperCAmelCase_  :				str      =      torch.manual_seed(lowerCAmelCase_		)
				else:
							UpperCAmelCase_  :				Union[str, Any]      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      {
				    "prompt": "horse",
				    "generator": generator,
				    "num_inference_steps": 1,
				    "frame_size": 32,
				    "output_type": "np",
				}
				return inputs





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    List[Any]:
				UpperCAmelCase_  :				Any      =      "cpu"

				UpperCAmelCase_  :				Union[str, Any]      =      self.get_dummy_components()

				UpperCAmelCase_  :				Tuple      =      self.pipeline_class(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      pipe.to(lowerCAmelCase_		)

				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				int      =      pipe(**self.get_dummy_inputs(lowerCAmelCase_		)		)
				UpperCAmelCase_  :				Dict      =      output.images[0]
				UpperCAmelCase_  :				Any      =      image[0, -3:, -3:, -1]

				assert image.shape == (20, 32, 32, 3)

				UpperCAmelCase_  :				Optional[int]      =      np.array(
				    [
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				    ]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Optional[Any]:
				# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
				self._test_inference_batch_consistent(batch_sizes=[1, 2]		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    List[str]:
				UpperCAmelCase_  :				Optional[Any]      =      torch_device == "cpu"
				UpperCAmelCase_  :				Dict      =      True

				self._test_inference_batch_single_identical(
				    batch_size=2			,			test_max_difference=lowerCAmelCase_			,			relax_max_difference=lowerCAmelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[str]:
				UpperCAmelCase_  :				int      =      self.get_dummy_components()
				UpperCAmelCase_  :				Tuple      =      self.pipeline_class(**lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				List[str]      =      1
				UpperCAmelCase_  :				List[Any]      =      2

				UpperCAmelCase_  :				int      =      self.get_dummy_inputs(lowerCAmelCase_		)

				for key in inputs.keys():
							if key in self.batch_params:
										UpperCAmelCase_  :				int      =      batch_size * [inputs[key]]

				UpperCAmelCase_  :				int      =      pipe(**lowerCAmelCase_			,			num_images_per_prompt=lowerCAmelCase_		)[0]

				assert images.shape[0] == batch_size * num_images_per_prompt







@slow
@require_torch_gpu
class     UpperCamelCase_ (unittest.TestCase  ):





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Any:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Any      =      load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/shap_e/test_shap_e_np_out.npy"		)
				UpperCAmelCase_  :				str      =      ShapEPipeline.from_pretrained("openai/shap-e"		)
				UpperCAmelCase_  :				Union[str, Any]      =      pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				Union[str, Any]      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(0		)

				UpperCAmelCase_  :				Optional[int]      =      pipe(
				    "a shark"			,			generator=lowerCAmelCase_			,			guidance_scale=1_5.0			,			num_inference_steps=64			,			frame_size=64			,			output_type="np"			,			).images[0]

				assert images.shape == (20, 64, 64, 3)

				assert_mean_pixel_difference(lowerCAmelCase_			,			lowerCAmelCase_		)



