

"""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     :    Optional[Any]    =		logging.get_logger(__name__)

__A     :    Tuple    =		{
    '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     :    List[str]    =		[
    'lm_head',
]
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					for attribute in key.split("."      ):
										A			=					getattr(UpperCamelCase__			,							UpperCamelCase__      )

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

					logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					[]
					A			=					fairseq_model.state_dict()

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

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

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

					logger.warning(F"""Unused weights: {unused_weights}"""      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					path.split("."      )
					return reduce(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					access_by_string(UpperCamelCase__			,							UpperCamelCase__      )
					A			=					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."""      )
					A			=					fsq_value
					logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					full_name.split("conv_layers."      )[-1]
					A			=					name.split("."      )
					A			=					int(items[0]      )
					A			=					int(items[1]      )

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

					set_weights(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__			,							F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					full_name.split("pos_conv."      )[-1]
					A			=					name.split("."      )
					A			=					int(items[0]      )
					A			=					int(items[1]      )

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

					set_weights(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__			,							F"""layers.{layer_id}.{layer_type}.{weight_type}"""      )
@torch.no_grad()
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__=None			,							lowercase__=None			,							lowercase__=True      ):
					"""simple docstring"""
					if config_path is not None:
										A			=					DataaVecAudioConfig.from_pretrained(UpperCamelCase__      )
					else:
										A			=					DataaVecAudioConfig()

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

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

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

					A			=					load_dataavec(UpperCamelCase__      )

					recursively_load_weights(UpperCamelCase__			,							UpperCamelCase__			,							not is_finetuned      )

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

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

					A			=					processor(UpperCamelCase__			,							return_tensors="pt"			,							padding=UpperCamelCase__      )

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

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

										A			=					torch.argmax(UpperCamelCase__			,							dim=-1      )
										A			=					processor.batch_decode(UpperCamelCase__      )

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

					print(our_output.shape			,							their_output.shape      )
					A			=					torch.max(torch.abs(our_output - their_output      )      ).item()
					print(F"""max_absolute_diff = {max_absolute_diff}"""      )  # ~ 1e-7
					A			=					torch.allclose(UpperCamelCase__			,							UpperCamelCase__			,							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(UpperCamelCase__      )

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


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












import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
	import torch




class      UpperCamelCase_				(   unittest.TestCase ,     UpperCamelCase__			):


							def    _snake_case     (							self			:Dict						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					load_tool("""speech-to-text"""						)
									self.tool.setup()


							def    _snake_case     (							self			:str						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.tool(torch.ones(3000						)						)
									self.assertEqual(__A							, """ you"""						)


							def    _snake_case     (							self			:Union[str, Any]						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.tool(audio=torch.ones(3000						)						)
									self.assertEqual(__A							, """ you"""						)