from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class 					a__     ( __SCREAMING_SNAKE_CASE ):
				_A       					=							['''vqvae''']






				def __init__(    self       :						str  ,							A_       :						Optional[int]  ,							A_       :						Optional[Any]  ,							A_       :						Any  ,							A_       :						List[Any]  ,							)       ->      Union[str, Any]:

										"""simple docstring"""
										super().__init__()
										self.register_modules(unet=A__  ,							scheduler=A__  ,							mel=A__  ,							vqvae=A__							)






				def   lowerCAmelCase (    self       :						int							)       ->      Any:

										"""simple docstring"""
										return 50 if isinstance(self.scheduler  ,							A__							) else 10_00






				@torch.no_grad()
				def __call__(    self       :						List[str]  ,							A_       :						Optional[int] = 1  ,							A_       :						Optional[Any] = None  ,							A_       :						Dict = None  ,							A_       :						str = 0  ,							A_       :						Optional[Any] = 0  ,							A_       :						int = None  ,							A_       :						str = None  ,							A_       :						int = 0  ,							A_       :						str = 0  ,							A_       :						int = None  ,							A_       :						Dict = 0  ,							A_       :						Union[str, Any] = None  ,							A_       :						int = None  ,							A_       :						Tuple=True  ,							)       ->      int:

										"""simple docstring"""
										lowerCamelCase_:       Union[str, Any]					     = steps or self.get_default_steps()
										self.scheduler.set_timesteps(A__							)
										lowerCamelCase_:       Union[str, Any]					     = step_generator or generator
										# For backwards compatibility
										if type(self.unet.config.sample_size							) == int:
																lowerCamelCase_:       List[Any]					     = (self.unet.config.sample_size, self.unet.config.sample_size)
										if noise is None:
																lowerCamelCase_:       Optional[Any]					     = randn_tensor(
																    (
																        batch_size,
																        self.unet.config.in_channels,
																        self.unet.config.sample_size[0],
																        self.unet.config.sample_size[1],
																    )  ,							generator=A__  ,							device=self.device  ,							)
										lowerCamelCase_:       List[Any]					     = noise
										lowerCamelCase_:       int					     = None

										if audio_file is not None or raw_audio is not None:
																self.mel.load_audio(A__  ,							A__							)
																lowerCamelCase_:       Optional[int]					     = self.mel.audio_slice_to_image(A__							)
																lowerCamelCase_:       Tuple					     = np.frombuffer(input_image.tobytes()  ,							dtype="""uint8"""							).reshape(
																    (input_image.height, input_image.width)							)
																lowerCamelCase_:       List[Any]					     = (input_image / 2_55) * 2 - 1
																lowerCamelCase_:       Any					     = torch.tensor(input_image[np.newaxis, :, :]  ,							dtype=torch.float							).to(self.device							)

																if self.vqvae is not None:
																						lowerCamelCase_:       int					     = self.vqvae.encode(torch.unsqueeze(A__  ,							0							)							).latent_dist.sample(
																						    generator=A__							)[0]
																						lowerCamelCase_:       Optional[int]					     = self.vqvae.config.scaling_factor * input_images

																if start_step > 0:
																						lowerCamelCase_:       str					     = self.scheduler.add_noise(A__  ,							A__  ,							self.scheduler.timesteps[start_step - 1]							)

																lowerCamelCase_:       Any					     = (
																    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
																)
																lowerCamelCase_:       Tuple					     = int(mask_start_secs * pixels_per_second							)
																lowerCamelCase_:       Optional[Any]					     = int(mask_end_secs * pixels_per_second							)
																lowerCamelCase_:       List[str]					     = self.scheduler.add_noise(A__  ,							A__  ,							torch.tensor(self.scheduler.timesteps[start_step:]							)							)

										for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
																if isinstance(self.unet  ,							A__							):
																						lowerCamelCase_:       List[str]					     = self.unet(A__  ,							A__  ,							A__							)["""sample"""]
																else:
																						lowerCamelCase_:       Tuple					     = self.unet(A__  ,							A__							)["""sample"""]

																if isinstance(self.scheduler  ,							A__							):
																						lowerCamelCase_:       List[Any]					     = self.scheduler.step(
																						    model_output=A__  ,							timestep=A__  ,							sample=A__  ,							eta=A__  ,							generator=A__  ,							)["""prev_sample"""]
																else:
																						lowerCamelCase_:       List[Any]					     = self.scheduler.step(
																						    model_output=A__  ,							timestep=A__  ,							sample=A__  ,							generator=A__  ,							)["""prev_sample"""]

																if mask is not None:
																						if mask_start > 0:
																												lowerCamelCase_:       Union[str, Any]					     = mask[:, step, :, :mask_start]
																						if mask_end > 0:
																												lowerCamelCase_:       Union[str, Any]					     = mask[:, step, :, -mask_end:]

										if self.vqvae is not None:
																# 0.18215 was scaling factor used in training to ensure unit variance
																lowerCamelCase_:       Any					     = 1 / self.vqvae.config.scaling_factor * images
																lowerCamelCase_:       int					     = self.vqvae.decode(A__							)["""sample"""]

										lowerCamelCase_:       Optional[Any]					     = (images / 2 + 0.5).clamp(0  ,							1							)
										lowerCamelCase_:       List[str]					     = images.cpu().permute(0  ,							2  ,							3  ,							1							).numpy()
										lowerCamelCase_:       List[str]					     = (images * 2_55).round().astype("""uint8"""							)
										lowerCamelCase_:       Tuple					     = list(
										    (Image.fromarray(_[:, :, 0]							) for _ in images)
										    if images.shape[3] == 1
										    else (Image.fromarray(A__  ,							mode="""RGB"""							).convert("""L"""							) for _ in images)							)

										lowerCamelCase_:       List[str]					     = [self.mel.image_to_audio(A__							) for _ in images]
										if not return_dict:
																return images, (self.mel.get_sample_rate(), audios)

										return BaseOutput(**AudioPipelineOutput(np.array(A__							)[:, np.newaxis, :]							)  ,							**ImagePipelineOutput(A__							)							)






				@torch.no_grad()
				def   lowerCAmelCase (    self       :						Optional[int]  ,							A_       :						Optional[int]  ,							A_       :						Any = 50							)       ->      Tuple:

										"""simple docstring"""
										assert isinstance(self.scheduler  ,							A__							)
										self.scheduler.set_timesteps(A__							)
										lowerCamelCase_:       Any					     = np.array(
										    [np.frombuffer(image.tobytes()  ,							dtype="""uint8"""							).reshape((1, image.height, image.width)							) for image in images]							)
										lowerCamelCase_:       Optional[int]					     = (sample / 2_55) * 2 - 1
										lowerCamelCase_:       int					     = torch.Tensor(A__							).to(self.device							)

										for t in self.progress_bar(torch.flip(self.scheduler.timesteps  ,							(0,)							)							):
																lowerCamelCase_:       List[Any]					     = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
																lowerCamelCase_:       List[Any]					     = self.scheduler.alphas_cumprod[t]
																lowerCamelCase_:       Optional[int]					     = (
																    self.scheduler.alphas_cumprod[prev_timestep]
																    if prev_timestep >= 0
																    else self.scheduler.final_alpha_cumprod
																)
																lowerCamelCase_:       Tuple					     = 1 - alpha_prod_t
																lowerCamelCase_:       List[str]					     = self.unet(A__  ,							A__							)["""sample"""]
																lowerCamelCase_:       Tuple					     = (1 - alpha_prod_t_prev) ** 0.5 * model_output
																lowerCamelCase_:       Any					     = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
																lowerCamelCase_:       str					     = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

										return sample






				@staticmethod
				def   lowerCAmelCase (    A_       :						Optional[int]  ,							A_       :						Dict  ,							A_       :						Dict							)       ->      Any:

										"""simple docstring"""
										lowerCamelCase_:       str					     = acos(torch.dot(torch.flatten(A__							)  ,							torch.flatten(A__							)							) / torch.norm(A__							) / torch.norm(A__							)							)
										return sin((1 - alpha) * theta							) * xa / sin(A__							) + sin(alpha * theta							) * xa / sin(A__							)







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
A_			:					List[str]    			=     logging.get_logger(__name__)

A_			:					List[Any]    			=     {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
A_			:					str    			=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]

def 		UpperCamelCase   (lowercase_:					List[Any]   )	-> List[Any]:
	A__				:       Optional[Any]	  =						{}
	with open(lowercase_       , """r"""   ) as file:
		for line_number, line in enumerate(lowercase_   ):
			A__				:       Any	  =						line.strip()
			if line:
				A__				:       Optional[int]	  =						line.split()
				A__				:       Any	  =						line_number
				A__				:       Tuple	  =						words[0]
				A__				:       Union[str, Any]	  =						value
	return result

def 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					Tuple       , lowercase_:					int       , lowercase_:					Optional[int]       , lowercase_:					Optional[int]   )	-> Dict:
	for attribute in key.split("""."""   ):
		A__				:       Optional[int]	  =						getattr(lowercase_       , lowercase_   )

	A__				:       Optional[int]	  =						None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowercase_   ):
			A__				:       Dict	  =						PARAM_MAPPING[full_name.split("""."""   )[-1]]
			A__				:       str	  =						"""param"""

	if weight_type is not None and weight_type != "param":
		A__				:       List[Any]	  =						getattr(lowercase_       , lowercase_   ).shape
	elif weight_type is not None and weight_type == "param":
		A__				:       Optional[int]	  =						hf_pointer
		for attribute in hf_param_name.split("""."""   ):
			A__				:       Optional[Any]	  =						getattr(lowercase_       , lowercase_   )
		A__				:       List[Any]	  =						shape_pointer.shape

		# let's reduce dimension
		A__				:       str	  =						value[0]
	else:
		A__				:       Dict	  =						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":
		A__				:       Any	  =						value
	elif weight_type == "weight_g":
		A__				:       List[str]	  =						value
	elif weight_type == "weight_v":
		A__				:       Union[str, Any]	  =						value
	elif weight_type == "bias":
		A__				:       Optional[Any]	  =						value
	elif weight_type == "param":
		for attribute in hf_param_name.split("""."""   ):
			A__				:       Dict	  =						getattr(lowercase_       , lowercase_   )
		A__				:       Tuple	  =						value
	else:
		A__				:       List[str]	  =						value

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

def 		UpperCamelCase   (lowercase_:					Any       , lowercase_:					Optional[Any]       , lowercase_:					Tuple       , lowercase_:					Any       , lowercase_:					Union[str, Any]   )	-> Tuple:
	A__				:       Any	  =						None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowercase_   ):
			A__				:       List[Any]	  =						PARAM_MAPPING[full_name.split("""."""   )[-1]]
			A__				:       List[str]	  =						"""param"""

	if weight_type is not None and weight_type != "param":
		A__				:       int	  =						""".""".join([key, weight_type]   )
	elif weight_type is not None and weight_type == "param":
		A__				:       Union[str, Any]	  =						""".""".join([key, hf_param_name]   )
	else:
		A__				:       Optional[Any]	  =						key

	A__				:       Tuple	  =						value if """lm_head""" in full_key else value[0]


A_			:					List[Any]    			=     {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}

def 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					Tuple       , lowercase_:					Any=None       , lowercase_:					Optional[int]=None   )	-> List[Any]:
	A__				:       int	  =						False
	for key, mapped_key in MAPPING.items():
		A__				:       int	  =						"""wav2vec2.""" + 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]:
			A__				:       Optional[Any]	  =						True
			if "*" in mapped_key:
				A__				:       List[Any]	  =						name.split(lowercase_   )[0].split("""."""   )[-2]
				A__				:       int	  =						mapped_key.replace("""*"""       , lowercase_   )
			if "weight_g" in name:
				A__				:       List[Any]	  =						"""weight_g"""
			elif "weight_v" in name:
				A__				:       int	  =						"""weight_v"""
			elif "bias" in name:
				A__				:       Any	  =						"""bias"""
			elif "weight" in name:
				# TODO: don't match quantizer.weight_proj
				A__				:       str	  =						"""weight"""
			else:
				A__				:       str	  =						None
			if hf_dict is not None:
				rename_dict(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )
			else:
				set_recursively(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )
			return is_used
	return is_used

def 		UpperCamelCase   (lowercase_:					Union[str, Any]       , lowercase_:					Optional[int]       , lowercase_:					Any   )	-> Tuple:
	A__				:       List[Any]	  =						[]
	A__				:       Tuple	  =						fairseq_model.state_dict()

	A__				:       Any	  =						hf_model.wavaveca.feature_extractor

	for name, value in fairseq_dict.items():
		A__				:       Any	  =						False
		if "conv_layers" in name:
			load_conv_layer(
			    lowercase_       , lowercase_       , lowercase_       , lowercase_       , hf_model.config.feat_extract_norm == """group"""       , )
			A__				:       Any	  =						True
		else:
			A__				:       Union[str, Any]	  =						load_wavaveca_layer(lowercase_       , lowercase_       , lowercase_   )
		if not is_used:
			unused_weights.append(lowercase_   )

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

def 		UpperCamelCase   (lowercase_:					Optional[Any]       , lowercase_:					List[str]       , lowercase_:					Any       , lowercase_:					Union[str, Any]       , lowercase_:					Any   )	-> Dict:
	A__				:       str	  =						full_name.split("""conv_layers."""   )[-1]
	A__				:       Optional[Any]	  =						name.split("""."""   )
	A__				:       Optional[int]	  =						int(items[0]   )
	A__				:       List[str]	  =						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."""   )
			A__				:       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."""   )
			A__				:       Any	  =						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."""   )
			A__				:       Union[str, Any]	  =						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."""   )
			A__				:       Dict	  =						value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
	else:
		unused_weights.append(lowercase_   )

@torch.no_grad()
def 		UpperCamelCase   (lowercase_:					List[Any]       , lowercase_:					int       , lowercase_:					str=None       , lowercase_:					Tuple=None       , lowercase_:					List[str]=True       , lowercase_:					Any=False   )	-> str:
	if config_path is not None:
		A__				:       Any	  =						WavaVecaConfig.from_pretrained(lowercase_   )
	else:
		A__				:       int	  =						WavaVecaConfig()

	if is_seq_class:
		A__				:       Optional[Any]	  =						read_txt_into_dict(lowercase_   )
		A__				:       List[str]	  =						idalabel
		A__				:       Any	  =						WavaVecaForSequenceClassification(lowercase_   )
		A__				:       str	  =						WavaVecaFeatureExtractor(
		    feature_size=1       , sampling_rate=16000       , padding_value=0       , do_normalize=lowercase_       , return_attention_mask=lowercase_       , )
		feature_extractor.save_pretrained(lowercase_   )

	elif is_finetuned:
		if dict_path:
			A__				:       List[Any]	  =						Dictionary.load(lowercase_   )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			A__				:       Tuple	  =						target_dict.pad_index
			A__				:       Union[str, Any]	  =						target_dict.bos_index
			A__				:       List[Any]	  =						target_dict.eos_index
			A__				:       List[str]	  =						len(target_dict.symbols   )
			A__				:       str	  =						os.path.join(lowercase_       , """vocab.json"""   )
			if not os.path.isdir(lowercase_   ):
				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase_   )   )
				return
			os.makedirs(lowercase_       , exist_ok=lowercase_   )
			A__				:       Dict	  =						target_dict.indices

			# fairseq has the <pad> and <s> switched
			A__				:       List[str]	  =						0
			A__				:       Optional[Any]	  =						1
			with open(lowercase_       , """w"""       , encoding="""utf-8"""   ) as vocab_handle:
				json.dump(lowercase_       , lowercase_   )
			A__				:       List[str]	  =						WavaVecaCTCTokenizer(
			    lowercase_       , 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=lowercase_       , )
			A__				:       List[Any]	  =						True if config.feat_extract_norm == """layer""" else False
			A__				:       int	  =						WavaVecaFeatureExtractor(
			    feature_size=1       , sampling_rate=16000       , padding_value=0       , do_normalize=lowercase_       , return_attention_mask=lowercase_       , )
			A__				:       str	  =						WavaVecaProcessor(feature_extractor=lowercase_       , tokenizer=lowercase_   )
			processor.save_pretrained(lowercase_   )

		A__				:       Optional[Any]	  =						WavaVecaForCTC(lowercase_   )
	else:
		A__				:       str	  =						WavaVecaForPreTraining(lowercase_   )

	if is_finetuned or is_seq_class:
		A__  ,  A__  ,  A__				:       List[Any]	  =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]       , arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
	else:
		A__				:       List[Any]	  =						argparse.Namespace(task="""audio_pretraining"""   )
		A__				:       Any	  =						fairseq.tasks.setup_task(lowercase_   )

		A__  ,  A__  ,  A__				:       List[str]	  =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       , task=lowercase_   )

	A__				:       Tuple	  =						model[0].eval()

	recursively_load_weights(lowercase_       , lowercase_       , not is_finetuned   )

	hf_wavavec.save_pretrained(lowercase_   )


if __name__ == "__main__":
	A_			:					Optional[Any]    			=     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'
	)
	parser.add_argument(
	    '--is_seq_class',
	    action='store_true',
	    help='Whether the model to convert is a fine-tuned sequence classification model or not',
	)
	A_			:					Any    			=     parser.parse_args()

	A_			:					List[Any]    			=     not args.not_finetuned and not args.is_seq_class
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.dict_path,
	    is_finetuned,
	    args.is_seq_class,
	)



