from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCamelCase    :      Optional[Any]	 =						{
    '''configuration_align''': [
        '''ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AlignConfig''',
        '''AlignTextConfig''',
        '''AlignVisionConfig''',
    ],
    '''processing_align''': ['''AlignProcessor'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase    :      str	 =						[
							    '''ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''AlignModel''',
							    '''AlignPreTrainedModel''',
							    '''AlignTextModel''',
							    '''AlignVisionModel''',
							]

if TYPE_CHECKING:
							from .configuration_align import (
							    ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    AlignConfig,
							    AlignTextConfig,
							    AlignVisionConfig,
							)
							from .processing_align import AlignProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_align import (
														    ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AlignModel,
														    AlignPreTrainedModel,
														    AlignTextModel,
														    AlignVisionModel,
														)

else:
							import sys

							_lowerCamelCase    :      List[Any]	 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





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()
_lowerCamelCase    :      List[Any]	 =						logging.get_logger(__name__)

_lowerCamelCase    :      Any	 =						{
    '''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''',
}
_lowerCamelCase    :      int	 =						[
    '''lm_head''',
]





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[int]					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							Optional[int]					,       UpperCAmelCase__   :							Tuple					,       UpperCAmelCase__   :							Tuple     ):
					for attribute in key.split("."     ):
										SCREAMING_SNAKE_CASE				=				getattr(UpperCAmelCase__					,       UpperCAmelCase__     )

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

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





def 		__lowerCamelCase							(UpperCAmelCase__   :							Union[str, Any]					,       UpperCAmelCase__   :							Optional[Any]					,       UpperCAmelCase__   :							int     ):
					SCREAMING_SNAKE_CASE				=				[]
					SCREAMING_SNAKE_CASE				=				fairseq_model.state_dict()

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

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

					for name, value in fairseq_dict.items():
										SCREAMING_SNAKE_CASE				=				False
										if "conv_layers" in name:
															load_conv_layer(
															    UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__					,       )
															SCREAMING_SNAKE_CASE				=				True
										elif "pos_conv" in name:
															load_pos_conv_layer(
															    UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__					,       )
															SCREAMING_SNAKE_CASE				=				True
										else:
															for key, mapped_key in MAPPING.items():
																				if not is_headless:
																									SCREAMING_SNAKE_CASE				=				"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]:
																									SCREAMING_SNAKE_CASE				=				True
																									if "*" in mapped_key:
																														SCREAMING_SNAKE_CASE				=				name.split(UpperCAmelCase__     )[0].split("."     )[-2]
																														SCREAMING_SNAKE_CASE				=				mapped_key.replace("*"					,       UpperCAmelCase__     )
																									if "weight_g" in name:
																														SCREAMING_SNAKE_CASE				=				"weight_g"
																									elif "weight_v" in name:
																														SCREAMING_SNAKE_CASE				=				"weight_v"
																									elif "bias" in name:
																														SCREAMING_SNAKE_CASE				=				"bias"
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														SCREAMING_SNAKE_CASE				=				"weight"
																									else:
																														SCREAMING_SNAKE_CASE				=				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 		__lowerCamelCase							(UpperCAmelCase__   :							Any					,       UpperCAmelCase__   :							List[str]     ):
					SCREAMING_SNAKE_CASE				=				path.split("."     )
					return reduce(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[int]					,       UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							List[str]     ):
					SCREAMING_SNAKE_CASE				=				access_by_string(UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				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."     )
					SCREAMING_SNAKE_CASE				=				fsq_value
					logger.info(F"{full_name} was correctly initialized from {hf_weight_path}."     )





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[Any]					,       UpperCAmelCase__   :							Optional[int]					,       UpperCAmelCase__   :							Dict					,       UpperCAmelCase__   :							int     ):
					SCREAMING_SNAKE_CASE				=				full_name.split("conv_layers."     )[-1]
					SCREAMING_SNAKE_CASE				=				name.split("."     )
					SCREAMING_SNAKE_CASE				=				int(items[0]     )
					SCREAMING_SNAKE_CASE				=				int(items[1]     )

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

					set_weights(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__					,       F"conv_layers.{layer_id}.{layer_type}.{weight_type}"     )





def 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							Tuple					,       UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Optional[int]     ):
					SCREAMING_SNAKE_CASE				=				full_name.split("pos_conv."     )[-1]
					SCREAMING_SNAKE_CASE				=				name.split("."     )
					SCREAMING_SNAKE_CASE				=				int(items[0]     )
					SCREAMING_SNAKE_CASE				=				int(items[1]     )

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

					set_weights(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__					,       F"layers.{layer_id}.{layer_type}.{weight_type}"     )





@torch.no_grad()
def 		__lowerCamelCase							(UpperCAmelCase__   :							Any					,       UpperCAmelCase__   :							Optional[Any]					,       UpperCAmelCase__   :							Optional[Any]=None					,       UpperCAmelCase__   :							List[Any]=None					,       UpperCAmelCase__   :							Optional[int]=True     ):
					if config_path is not None:
										SCREAMING_SNAKE_CASE				=				DataaVecAudioConfig.from_pretrained(UpperCAmelCase__     )
					else:
										SCREAMING_SNAKE_CASE				=				DataaVecAudioConfig()

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

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

					def load_dataavec(UpperCAmelCase__   :							Optional[Any]     ):
										SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				fairseq.checkpoint_utils.load_model_ensemble_and_task([path]     )
										return model[0].eval()

					SCREAMING_SNAKE_CASE				=				load_dataavec(UpperCAmelCase__     )

					recursively_load_weights(UpperCAmelCase__					,       UpperCAmelCase__					,       not is_finetuned     )

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

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

					SCREAMING_SNAKE_CASE				=				processor(UpperCAmelCase__					,       return_tensors="pt"					,       padding=UpperCAmelCase__     )

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

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

										SCREAMING_SNAKE_CASE				=				torch.argmax(UpperCAmelCase__					,       dim=-1     )
										SCREAMING_SNAKE_CASE				=				processor.batch_decode(UpperCAmelCase__     )

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

					print(our_output.shape					,       their_output.shape     )
					SCREAMING_SNAKE_CASE				=				torch.max(torch.abs(our_output - their_output     )     ).item()
					print(F"max_absolute_diff = {max_absolute_diff}"     )  # ~ 1e-7
					SCREAMING_SNAKE_CASE				=				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__":
							_lowerCamelCase    :      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'''
							)
							_lowerCamelCase    :      Union[str, Any]	 =						parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)




