"""simple docstring"""


import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

a							={
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
a							=[
    'lm_head',
]





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						for attribute in key.split("."				):
												lowerCamelCase__					=getattr(__lowerCAmelCase   ,    __lowerCAmelCase				)

						if weight_type is not None:
												lowerCamelCase__					=getattr(__lowerCAmelCase   ,    __lowerCAmelCase				).shape
						else:
												lowerCamelCase__					=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":
												lowerCamelCase__					=value
						elif weight_type == "weight_g":
												lowerCamelCase__					=value
						elif weight_type == "weight_v":
												lowerCamelCase__					=value
						elif weight_type == "bias":
												lowerCamelCase__					=value
						else:
												lowerCamelCase__					=value

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





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[Any]:




						'''simple docstring'''



						lowerCamelCase__					=[]
						lowerCamelCase__					=fairseq_model.state_dict()

						if not is_headless:
												lowerCamelCase__					=hf_model.dataavec_audio.feature_extractor
												lowerCamelCase__					=hf_model.dataavec_audio.encoder.pos_conv_embed

						else:
												lowerCamelCase__					=hf_model.feature_extractor
												lowerCamelCase__					=hf_model.encoder.pos_conv_embed

						for name, value in fairseq_dict.items():
												lowerCamelCase__					=False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    )
																		lowerCamelCase__					=True
												elif "pos_conv" in name:
																		load_pos_conv_layer(
																		    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    )
																		lowerCamelCase__					=True
												else:
																		for key, mapped_key in MAPPING.items():
																								if not is_headless:
																														lowerCamelCase__					="data2vec_audio." + 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]:
																														lowerCamelCase__					=True
																														if "*" in mapped_key:
																																				lowerCamelCase__					=name.split(__lowerCAmelCase				)[0].split("."				)[-2]
																																				lowerCamelCase__					=mapped_key.replace("*"   ,    __lowerCAmelCase				)
																														if "weight_g" in name:
																																				lowerCamelCase__					="weight_g"
																														elif "weight_v" in name:
																																				lowerCamelCase__					="weight_v"
																														elif "bias" in name:
																																				lowerCamelCase__					="bias"
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				lowerCamelCase__					="weight"
																														else:
																																				lowerCamelCase__					=None
																														set_recursively(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)
																								continue
												if not is_used:
																		unused_weights.append(__lowerCAmelCase				)

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





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[Any]:




						'''simple docstring'''



						lowerCamelCase__					=path.split("."				)
						return reduce(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						lowerCamelCase__					=access_by_string(__lowerCAmelCase   ,    __lowerCAmelCase				)
						lowerCamelCase__					=hf_weight.data

						if fsq_value.shape != hf_value.shape:
												raise ValueError(F'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''				)
						lowerCamelCase__					=fsq_value
						logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''				)





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Dict:




						'''simple docstring'''



						lowerCamelCase__					=full_name.split("conv_layers."				)[-1]
						lowerCamelCase__					=name.split("."				)
						lowerCamelCase__					=int(items[0]				)
						lowerCamelCase__					=int(items[1]				)

						lowerCamelCase__					=name.split("."				)[-1]
						if type_id == 0:
												lowerCamelCase__					="conv"
						elif type_id == 2:
												lowerCamelCase__					="layer_norm"
						else:
												unused_weights.append(__lowerCAmelCase				)
												return

						set_weights(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    F'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''				)





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__					=full_name.split("pos_conv."				)[-1]
						lowerCamelCase__					=name.split("."				)
						lowerCamelCase__					=int(items[0]				)
						lowerCamelCase__					=int(items[1]				)

						lowerCamelCase__					=name.split("."				)[-1]
						if type_id != 0:
												unused_weights.append(__lowerCAmelCase				)
												return
						else:
												lowerCamelCase__					="conv"

						set_weights(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    F'''layers.{layer_id}.{layer_type}.{weight_type}'''				)





@torch.no_grad()
def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase=None   ,    __lowerCAmelCase=None   ,    __lowerCAmelCase=True				)							->					Tuple:




						'''simple docstring'''



						if config_path is not None:
												lowerCamelCase__					=DataaVecAudioConfig.from_pretrained(__lowerCAmelCase				)
						else:
												lowerCamelCase__					=DataaVecAudioConfig()

						if not is_finetuned:
												# Modify final_proj layer name
												lowerCamelCase__					=DataaVecAudioModel(__lowerCAmelCase				)
												lowerCamelCase__					=os.path.dirname(__lowerCAmelCase				)

												lowerCamelCase__					=torch.load(__lowerCAmelCase				)
												lowerCamelCase__					=state_dict["model"].pop("final_proj.0.weight"				)
												lowerCamelCase__					=state_dict["model"].pop("final_proj.0.bias"				)
												lowerCamelCase__					=os.path.join(__lowerCAmelCase   ,    "converted.pt"				)
												torch.save(__lowerCAmelCase   ,    __lowerCAmelCase				)
						else:
												lowerCamelCase__					=DataaVecAudioForCTC(__lowerCAmelCase				)
												lowerCamelCase__					=checkpoint_path

						def load_dataavec(__lowerCAmelCase				):
												lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=fairseq.checkpoint_utils.load_model_ensemble_and_task([path]				)
												return model[0].eval()

						lowerCamelCase__					=load_dataavec(__lowerCAmelCase				)

						recursively_load_weights(__lowerCAmelCase   ,    __lowerCAmelCase   ,    not is_finetuned				)

						lowerCamelCase__					=WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"				)

						lowerCamelCase__					=load_dataset("patrickvonplaten/librispeech_asr_dummy"   ,    "clean"   ,    split="validation"				)
						lowerCamelCase__					=[x["array"] for x in ds[:4]["audio"]]

						lowerCamelCase__					=processor(__lowerCAmelCase   ,    return_tensors="pt"   ,    padding=__lowerCAmelCase				)

						lowerCamelCase__					=inputs.input_values
						lowerCamelCase__					=inputs.attention_mask
						#    input_values = inputs.input_values[:, :-1]
						#    attention_mask = inputs.attention_mask[:, :-1]

						hf_wavavec.eval()
						model.eval()
						if is_finetuned:
												lowerCamelCase__					=model(source=__lowerCAmelCase   ,    padding_mask=(1 - attention_mask)   ,    mask=__lowerCAmelCase   ,    features_only=__lowerCAmelCase				)[
												    "encoder_out"
												].transpose(0   ,    1				)
												lowerCamelCase__					=hf_wavavec(__lowerCAmelCase   ,    attention_mask=__lowerCAmelCase				)["logits"]

												lowerCamelCase__					=torch.argmax(__lowerCAmelCase   ,    dim=-1				)
												lowerCamelCase__					=processor.batch_decode(__lowerCAmelCase				)

												print(F'''Expected Output: {ds[:4]['text']}, Pred: {output_string}'''				)
						else:
												lowerCamelCase__					=model(source=__lowerCAmelCase   ,    padding_mask=(1 - attention_mask)   ,    mask=__lowerCAmelCase   ,    features_only=__lowerCAmelCase				)[
												    "layer_results"
												][-1][0].transpose(0   ,    1				)
												lowerCamelCase__					=hf_wavavec(__lowerCAmelCase   ,    attention_mask=__lowerCAmelCase				)["last_hidden_state"]

						print(our_output.shape   ,    their_output.shape				)
						lowerCamelCase__					=torch.max(torch.abs(our_output - their_output				)				).item()
						print(F'''max_absolute_diff = {max_absolute_diff}'''				)  # ~ 1e-7
						lowerCamelCase__					=torch.allclose(__lowerCAmelCase   ,    __lowerCAmelCase   ,    atol=1e-3				)
						print("Do both models output the same tensors?"   ,    "🔥" if success else "💩"				)
						if not success:
												raise Exception("Something went wRoNg"				)

						hf_wavavec.save_pretrained(__lowerCAmelCase				)

						if is_finetuned:
												processor.save_pretrained(__lowerCAmelCase				)
						else:
												processor.feature_extractor.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
	a							=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'
	)
	a							=parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)



"""simple docstring"""


import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


a							=logging.get_logger(__name__)






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Any  							=       '''hybrid-clip'''
				A__      :   Optional[Any]  							=       True

				def __init__(      self	, _lowerCamelCase=512	, **_lowerCamelCase      ):
										super().__init__(**_lowerCamelCase      )

										if "text_config" not in kwargs:
																raise ValueError("`text_config` can not be `None`."      )

										if "vision_config" not in kwargs:
																raise ValueError("`vision_config` can not be `None`."      )

										lowerCamelCase__					=kwargs.pop("text_config"      )
										lowerCamelCase__					=kwargs.pop("vision_config"      )

										lowerCamelCase__					=text_config.pop("model_type"      )
										lowerCamelCase__					=vision_config.pop("model_type"      )

										from transformers import AutoConfig

										lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      )

										if vision_model_type == "clip":
																lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      ).vision_config
										elif vision_model_type == "clip_vision_model":
																from transformers import CLIPVisionConfig

																lowerCamelCase__					=CLIPVisionConfig(**_lowerCamelCase      )
										else:
																lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      )

										lowerCamelCase__					=projection_dim
										lowerCamelCase__					=1.0

				@classmethod
				def 		_a	(      cls	, _lowerCamelCase	, _lowerCamelCase	, **_lowerCamelCase      ):
										return cls(text_config=text_config.to_dict()	, vision_config=vision_config.to_dict()	, **_lowerCamelCase      )






				def 		_a	(      self      ):
										lowerCamelCase__					=copy.deepcopy(self.__dict__      )
										lowerCamelCase__					=self.text_config.to_dict()
										lowerCamelCase__					=self.vision_config.to_dict()
										lowerCamelCase__					=self.__class__.model_type
										return output


