


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__       :	Tuple     				=logging.get_logger(__name__)

lowerCAmelCase__       :	int     				={
    "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__       :	Optional[int]     				=[
    "lm_head",
]






def 		a__       (							A__,	A__,	A__,	A__,	A__							):
			for attribute in key.split('.'							):
						SCREAMING_SNAKE_CASE_						:			List[Any]       						= getattr(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_							)

			if weight_type is not None:
						SCREAMING_SNAKE_CASE_						:			Tuple       						= getattr(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_							).shape
			else:
						SCREAMING_SNAKE_CASE_						:			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":
						SCREAMING_SNAKE_CASE_						:			Any       						= value
			elif weight_type == "weight_g":
						SCREAMING_SNAKE_CASE_						:			List[str]       						= value
			elif weight_type == "weight_v":
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= value
			elif weight_type == "bias":
						SCREAMING_SNAKE_CASE_						:			List[str]       						= value
			else:
						SCREAMING_SNAKE_CASE_						:			Any       						= value

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






def 		a__       (							A__,	A__,	A__							):
			SCREAMING_SNAKE_CASE_						:			List[Any]       						= []
			SCREAMING_SNAKE_CASE_						:			List[str]       						= fairseq_model.state_dict()

			if not is_headless:
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= hf_model.dataavec_audio.feature_extractor
						SCREAMING_SNAKE_CASE_						:			List[str]       						= hf_model.dataavec_audio.encoder.pos_conv_embed

			else:
						SCREAMING_SNAKE_CASE_						:			Dict       						= hf_model.feature_extractor
						SCREAMING_SNAKE_CASE_						:			Tuple       						= hf_model.encoder.pos_conv_embed

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

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






def 		a__       (							A__,	A__							):
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= path.split('.'							)
			return reduce(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_							)






def 		a__       (							A__,	A__,	A__,	A__							):
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= access_by_string(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_							)
			SCREAMING_SNAKE_CASE_						:			List[str]       						= 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_						:			Tuple       						= fsq_value
			logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''							)






def 		a__       (							A__,	A__,	A__,	A__							):
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= full_name.split('conv_layers.'							)[-1]
			SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= name.split('.'							)
			SCREAMING_SNAKE_CASE_						:			Tuple       						= int(items[0]							)
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= int(items[1]							)

			SCREAMING_SNAKE_CASE_						:			Any       						= name.split('.'							)[-1]
			if type_id == 0:
						SCREAMING_SNAKE_CASE_						:			List[str]       						= 'conv'
			elif type_id == 2:
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 'layer_norm'
			else:
						unused_weights.append(SCREAMING_SNAKE_CASE_							)
						return

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






def 		a__       (							A__,	A__,	A__,	A__							):
			SCREAMING_SNAKE_CASE_						:			str       						= full_name.split('pos_conv.'							)[-1]
			SCREAMING_SNAKE_CASE_						:			int       						= name.split('.'							)
			SCREAMING_SNAKE_CASE_						:			int       						= int(items[0]							)
			SCREAMING_SNAKE_CASE_						:			Dict       						= int(items[1]							)

			SCREAMING_SNAKE_CASE_						:			Dict       						= name.split('.'							)[-1]
			if type_id != 0:
						unused_weights.append(SCREAMING_SNAKE_CASE_							)
						return
			else:
						SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 'conv'

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






@torch.no_grad()
def 		a__       (							A__,	A__,	A__=None,	A__=None,	A__=True							):
			if config_path is not None:
						SCREAMING_SNAKE_CASE_						:			str       						= DataaVecAudioConfig.from_pretrained(SCREAMING_SNAKE_CASE_							)
			else:
						SCREAMING_SNAKE_CASE_						:			Optional[int]       						= DataaVecAudioConfig()

			if not is_finetuned:
						# Modify final_proj layer name
						SCREAMING_SNAKE_CASE_						:			Dict       						= DataaVecAudioModel(SCREAMING_SNAKE_CASE_							)
						SCREAMING_SNAKE_CASE_						:			Optional[int]       						= os.path.dirname(SCREAMING_SNAKE_CASE_							)

						SCREAMING_SNAKE_CASE_						:			int       						= torch.load(SCREAMING_SNAKE_CASE_							)
						SCREAMING_SNAKE_CASE_						:			List[str]       						= state_dict['model'].pop('final_proj.0.weight'							)
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= state_dict['model'].pop('final_proj.0.bias'							)
						SCREAMING_SNAKE_CASE_						:			Dict       						= os.path.join(SCREAMING_SNAKE_CASE_,	'converted.pt'							)
						torch.save(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_							)
			else:
						SCREAMING_SNAKE_CASE_						:			Optional[int]       						= DataaVecAudioForCTC(SCREAMING_SNAKE_CASE_							)
						SCREAMING_SNAKE_CASE_						:			Dict       						= checkpoint_path

			def load_dataavec(A__							):
						SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[Any]       						= fairseq.checkpoint_utils.load_model_ensemble_and_task([path]							)
						return model[0].eval()

			SCREAMING_SNAKE_CASE_						:			int       						= load_dataavec(SCREAMING_SNAKE_CASE_							)

			recursively_load_weights(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	not is_finetuned							)

			SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'							)

			SCREAMING_SNAKE_CASE_						:			Dict       						= load_dataset('patrickvonplaten/librispeech_asr_dummy',	'clean',	split='validation'							)
			SCREAMING_SNAKE_CASE_						:			List[str]       						= [x['array'] for x in ds[:4]['audio']]

			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= processor(SCREAMING_SNAKE_CASE_,	return_tensors='pt',	padding=SCREAMING_SNAKE_CASE_							)

			SCREAMING_SNAKE_CASE_						:			List[str]       						= inputs.input_values
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 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_						:			str       						= model(source=SCREAMING_SNAKE_CASE_,	padding_mask=(1 - attention_mask),	mask=SCREAMING_SNAKE_CASE_,	features_only=SCREAMING_SNAKE_CASE_							)[
						    'encoder_out'
						].transpose(0,	1							)
						SCREAMING_SNAKE_CASE_						:			int       						= hf_wavavec(SCREAMING_SNAKE_CASE_,	attention_mask=SCREAMING_SNAKE_CASE_							)['logits']

						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= torch.argmax(SCREAMING_SNAKE_CASE_,	dim=-1							)
						SCREAMING_SNAKE_CASE_						:			Any       						= processor.batch_decode(SCREAMING_SNAKE_CASE_							)

						print(F'''Expected Output: {ds[:4]['text']}, Pred: {output_string}'''							)
			else:
						SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model(source=SCREAMING_SNAKE_CASE_,	padding_mask=(1 - attention_mask),	mask=SCREAMING_SNAKE_CASE_,	features_only=SCREAMING_SNAKE_CASE_							)[
						    'layer_results'
						][-1][0].transpose(0,	1							)
						SCREAMING_SNAKE_CASE_						:			Optional[int]       						= hf_wavavec(SCREAMING_SNAKE_CASE_,	attention_mask=SCREAMING_SNAKE_CASE_							)['last_hidden_state']

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

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


if __name__ == "__main__":
				lowerCAmelCase__       :	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'
				)
				lowerCAmelCase__       :	Optional[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
				)







'''simple docstring'''



import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
     import sqlitea

     import sqlalchemy


UpperCamelCase__					:						Tuple											=     datasets.utils.logging.get_logger(__name__)

@dataclass
class       _a (datasets.BuilderConfig):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     True
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     1_00_00
       SCREAMING_SNAKE_CASE					=     None






       def 				UpperCamelCase (							self					)			->	str:
          if self.sql is None:
             raise ValueError("""sql must be specified"""					)
          if self.con is None:
             raise ValueError("""con must be specified"""					)






       def 				UpperCamelCase (							self				,							A__				,							A__ = None				,							)			->	str:
          _SCREAMING_SNAKE_CASE          = config_kwargs.copy()
          # We need to stringify the Selectable object to make its hash deterministic

          # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
          _SCREAMING_SNAKE_CASE          = config_kwargs["""sql"""]
          if not isinstance(A__				,							A__					):
             if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                import sqlalchemy

                if isinstance(A__				,							sqlalchemy.sql.Selectable					):
                   _SCREAMING_SNAKE_CASE          = sqlalchemy.create_engine(config_kwargs["""con"""].split("""://"""					)[0] + """://"""					)
                   _SCREAMING_SNAKE_CASE          = str(sql.compile(dialect=engine.dialect					)					)
                   _SCREAMING_SNAKE_CASE          = sql_str
                else:
                   raise TypeError(
                       F"Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(A__					)}: {sql}"					)
             else:
                raise TypeError(
                    F"Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(A__					)}: {sql}"					)
          _SCREAMING_SNAKE_CASE          = config_kwargs["""con"""]
          if not isinstance(A__				,							A__					):
             _SCREAMING_SNAKE_CASE          = id(A__					)
             logger.info(
                 F"SQL connection 'con' of type {type(A__					)} couldn't be hashed properly. To enable hashing, specify 'con' as URI string instead."					)

          return super().create_config_id(A__				,							custom_features=A__					)






       @property
       def 				UpperCamelCase (							self					)			->	Any:
          _SCREAMING_SNAKE_CASE          = {
              """index_col""": self.index_col,
              """columns""": self.columns,
              """params""": self.params,
              """coerce_float""": self.coerce_float,
              """parse_dates""": self.parse_dates,
          }
          return pd_read_sql_kwargs






class       _a (datasets.ArrowBasedBuilder):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     SqlConfig






       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return datasets.DatasetInfo(features=self.config.features					)






       def 				UpperCamelCase (							self				,							A__					)			->	Dict:
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,							gen_kwargs={}					)]






       def 				UpperCamelCase (							self				,							A__					)			->	pa.Table:
          if self.config.features is not None:
             _SCREAMING_SNAKE_CASE          = self.config.features.arrow_schema
             if all(not require_storage_cast(A__					) for feature in self.config.features.values()					):
                # cheaper cast
                _SCREAMING_SNAKE_CASE          = pa.Table.from_arrays([pa_table[field.name] for field in schema]				,							schema=A__					)
             else:
                # more expensive cast; allows str <-> int/float or str to Audio for example
                _SCREAMING_SNAKE_CASE          = table_cast(A__				,							A__					)
          return pa_table






       def 				UpperCamelCase (							self					)			->	Optional[int]:
          _SCREAMING_SNAKE_CASE          = self.config.chunksize
          _SCREAMING_SNAKE_CASE          = pd.read_sql(
              self.config.sql				,							self.config.con				,							chunksize=A__				,							**self.config.pd_read_sql_kwargs					)
          _SCREAMING_SNAKE_CASE          = [sql_reader] if chunksize is None else sql_reader
          for chunk_idx, df in enumerate(A__					):
             _SCREAMING_SNAKE_CASE          = pa.Table.from_pandas(A__					)
             yield chunk_idx, self._cast_table(A__					)






