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

_lowercase							 =							{
    '''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''',
}
_lowercase							 =							[
    '''lm_head''',
]



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)	->List[Any]:
			for attribute in key.split("."			):
						__lowercase						      =					getattr(__magic_name__ ,      __magic_name__			)

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

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



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__			)	->List[Any]:
			__lowercase						      =					[]
			__lowercase						      =					fairseq_model.state_dict()

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

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

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

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



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->List[Any]:
			__lowercase						      =					path.split("."			)
			return reduce(__magic_name__ ,      __magic_name__ ,      __magic_name__			)



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)	->int:
			__lowercase						      =					access_by_string(__magic_name__ ,      __magic_name__			)
			__lowercase						      =					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.'''			)
			__lowercase						      =					fsq_value
			logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''			)



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)	->Dict:
			__lowercase						      =					full_name.split("conv_layers."			)[-1]
			__lowercase						      =					name.split("."			)
			__lowercase						      =					int(items[0]			)
			__lowercase						      =					int(items[1]			)

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

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



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__			)	->Tuple:
			__lowercase						      =					full_name.split("pos_conv."			)[-1]
			__lowercase						      =					name.split("."			)
			__lowercase						      =					int(items[0]			)
			__lowercase						      =					int(items[1]			)

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

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



@torch.no_grad()
def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__=None ,      __magic_name__=None ,      __magic_name__=True			)	->Optional[Any]:
			if config_path is not None:
						__lowercase						      =					DataaVecAudioConfig.from_pretrained(__magic_name__			)
			else:
						__lowercase						      =					DataaVecAudioConfig()

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

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

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

			__lowercase						      =					load_dataavec(__magic_name__			)

			recursively_load_weights(__magic_name__ ,      __magic_name__ ,      not is_finetuned			)

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

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

			__lowercase						      =					processor(__magic_name__ ,      return_tensors="pt" ,      padding=__magic_name__			)

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

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

						__lowercase						      =					torch.argmax(__magic_name__ ,      dim=-1			)
						__lowercase						      =					processor.batch_decode(__magic_name__			)

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

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

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


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_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)







"""simple docstring"""






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
					from .testing_utils import (
					    floats_tensor,
					    load_hf_numpy,
					    load_image,
					    load_numpy,
					    load_pt,
					    nightly,
					    parse_flag_from_env,
					    print_tensor_test,
					    require_torch_a,
					    require_torch_gpu,
					    skip_mps,
					    slow,
					    torch_all_close,
					    torch_device,
					)
					from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_lowercase							 =							get_logger(__name__)



def     lowerCAmelCase__ (		__magic_name__			)	->Optional[int]:
			if version.parse(__magic_name__			) < version.parse(__magic_name__			):
						if "dev" in min_version:
									__lowercase						      =					(
									    "This example requires a source install from HuggingFace diffusers (see "
									    "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
									)
						else:
									__lowercase						      =					F'''This example requires a minimum version of {min_version},'''
						error_message += F''' but the version found is {__version__}.\n'''
						raise ImportError(__magic_name__			)





