import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_lowercase							:      Union[str, Any]      							=logging.get_logger(__name__)

_lowercase							:      Tuple      							={
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''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''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
_lowercase							:      List[str]      							=[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def A__  (			lowercase:     Tuple,		lowercase:     Optional[int],		lowercase:     List[Any],		lowercase:     Union[str, Any],		lowercase:     Optional[int]					)				->		str:
       for attribute in key.split('.'					):
              A						:     List[Any]         =getattr(lowercase,		lowercase					)

       if weight_type is not None:
              A						:     Optional[int]         =getattr(lowercase,		lowercase					).shape
       else:
              A						:     List[Any]         =hf_pointer.shape

       assert hf_shape == value.shape, (
           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						:     List[str]         =value
       elif weight_type == "weight_g":
              A						:     Optional[Any]         =value
       elif weight_type == "weight_v":
              A						:     List[str]         =value
       elif weight_type == "bias":
              A						:     List[str]         =value
       else:
              A						:     str         =value

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



def A__  (			lowercase:     int,		lowercase:     List[str]					)				->		Dict:
       A						:     str         =[]
       A						:     List[Any]         =fairseq_model.state_dict()

       A						:     Optional[int]         =hf_model.feature_extractor
       A						:     Optional[int]         =hf_model.adapter

       for name, value in fairseq_dict.items():
              A						:     str         =False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowercase,		lowercase,		lowercase,		lowercase,		hf_model.config.feat_extract_norm == 'group',		)
                     A						:     List[str]         =True
              elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']					):
                     load_adapter(lowercase,		lowercase,		lowercase,		lowercase					)
                     A						:     List[str]         =True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split('w2v_model.'					)[-1] == name.split('.'					)[0]:
                                   A						:     str         =True
                                   if "*" in mapped_key:
                                          A						:     int         =name.split(lowercase					)[0].split('.'					)[-2]
                                          A						:     List[str]         =mapped_key.replace('*',		lowercase					)
                                   if "weight_g" in name:
                                          A						:     List[Any]         ='weight_g'
                                   elif "weight_v" in name:
                                          A						:     Optional[int]         ='weight_v'
                                   elif "bias" in name:
                                          A						:     List[str]         ='bias'
                                   elif "weight" in name:
                                          A						:     Any         ='weight'
                                   else:
                                          A						:     Optional[int]         =None
                                   set_recursively(lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)
                            continue
              if not is_used:
                     unused_weights.append(lowercase					)

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



def A__  (			lowercase:     List[str],		lowercase:     List[str],		lowercase:     Tuple,		lowercase:     Any,		lowercase:     Dict					)				->		List[Any]:
       A						:     str         =full_name.split('conv_layers.'					)[-1]
       A						:     List[Any]         =name.split('.'					)
       A						:     Optional[Any]         =int(items[0]					)
       A						:     int         =int(items[1]					)

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         F'{full_name} has size {value.shape}, but'
                         F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
                     )
                     A						:     Optional[int]         =value
                     logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         F'{full_name} has size {value.shape}, but'
                         F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
                     )
                     A						:     Union[str, Any]         =value
                     logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                         " found."
                     )
                     A						:     Any         =value
                     logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         F'{full_name} has size {value.shape}, but'
                         F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
                     )
                     A						:     Any         =value
                     logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
       else:
              unused_weights.append(lowercase					)



def A__  (			lowercase:     Tuple,		lowercase:     int,		lowercase:     List[str],		lowercase:     Optional[Any]					)				->		str:
       A						:     Union[str, Any]         =full_name.split('adaptor.'					)[-1]
       A						:     Tuple         =name.split('.'					)

       if items[1].isdigit():
              A						:     Any         =int(items[1]					)
       else:
              A						:     List[str]         =None

       if "adaptor" not in full_name:
              if "proj_ln" in full_name:
                     # has to be layer norm
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.bias.data.shape
                            ), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
                            A						:     Optional[int]         =value
                            logger.info(F'Adapter proj layer norm bias was initialized from {full_name}.'					)
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.weight.data.shape
                            ), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
                            A						:     Optional[Any]         =value
              else:
                     # has to be projection layer
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj.bias.data.shape
                            ), F'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
                            A						:     int         =value
                            logger.info(F'Adapter proj layer bias was initialized from {full_name}.'					)
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj.weight.data.shape
                            ), F'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
                            A						:     Optional[int]         =value
                            logger.info(F'Adapter proj layer weight was initialized from {full_name}.'					)
       elif isinstance(lowercase,		lowercase					):
              if "bias" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.bias.data.shape
                     ), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
                     A						:     List[str]         =value
                     logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'					)
              elif "weight" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.weight.data.shape
                     ), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
                     A						:     Optional[int]         =value
                     logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'					)
       else:
              unused_weights.append(lowercase					)



def A__  (			lowercase:     Optional[Any]					)				->		Optional[Any]:
       A  ,  A						:     str         =emb.weight.shape
       A						:     List[Any]         =nn.Linear(lowercase,		lowercase,		bias=lowercase					)
       A						:     int         =emb.weight.data
       return lin_layer



@torch.no_grad()
def A__  (			lowercase:     Any,		lowercase:     str,		lowercase:     List[str],		lowercase:     int,		lowercase:     Optional[Any],		lowercase:     List[Any],		lowercase:     Optional[int],		lowercase:     int,		lowercase:     str,		lowercase:     List[Any],		lowercase:     str,		)				->		Optional[int]:
       A						:     Optional[int]         =WavaVecaConfig.from_pretrained(
           lowercase,		add_adapter=lowercase,		adapter_stride=lowercase,		adapter_kernel_size=lowercase,		use_auth_token=lowercase,		output_hidden_size=lowercase,		)
       A						:     int         =MBartConfig.from_pretrained(lowercase					)

       # load model
       A  ,  A  ,  A						:     List[Any]         =fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path],		arg_overrides={
               'config_yaml': config_yaml_path,
               'data': '/'.join(dict_path.split('/'					)[:-1]					),
               'w2v_path': checkpoint_path,
               'load_pretrained_decoder_from': None,
           },		)
       A						:     Union[str, Any]         =model[0].eval()

       # load feature extractor
       A						:     Tuple         =WavaVecaFeatureExtractor.from_pretrained(lowercase,		use_auth_token=lowercase					)

       # set weights for wav2vec2 encoder
       A						:     str         =WavaVecaModel(lowercase					)

       recursively_load_weights_wavaveca(model.encoder,		lowercase					)

       # load decoder weights
       A						:     List[str]         =MBartForCausalLM(lowercase					)
       A  ,  A						:     Optional[Any]         =hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),		strict=lowercase					)
       logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}'					)
       logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'					)

       A						:     Optional[Any]         =SpeechEncoderDecoderModel(encoder=lowercase,		decoder=lowercase					)
       A						:     Any         =False

       A						:     Optional[int]         =MBartaaTokenizer(lowercase					)
       tokenizer.save_pretrained(lowercase					)

       A						:     int         =hf_wavavec.config.to_dict()
       A						:     Dict         =tokenizer.pad_token_id
       A						:     str         =tokenizer.bos_token_id
       A						:     Tuple         =tokenizer.eos_token_id
       A						:     Tuple         ='mbart50'
       A						:     Any         ='wav2vec2'

       A						:     Tuple         =tokenizer.eos_token_id
       A						:     List[Any]         =250_004
       A						:     Optional[int]         =tokenizer.eos_token_id

       A						:     Any         =SpeechEncoderDecoderConfig.from_dict(lowercase					)

       hf_wavavec.save_pretrained(lowercase					)
       feature_extractor.save_pretrained(lowercase					)


if __name__ == "__main__":
    _lowercase							:      int      							=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_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
    parser.add_argument(
        '''--encoder_config_path''',
        default='''facebook/wav2vec2-xls-r-1b''',
        type=str,
        help='''Path to hf encoder wav2vec2 checkpoint config''',
    )
    parser.add_argument(
        '''--decoder_config_path''',
        default='''facebook/mbart-large-50-one-to-many-mmt''',
        type=str,
        help='''Path to hf decoder checkpoint config''',
    )
    parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
    parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
    parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
    parser.add_argument('''--encoder_output_dim''', default=1_0_2_4, type=int, help='''encoder output dim''')
    parser.add_argument('''--start_token_id''', default=2_5_0_0_0_4, type=int, help='''`decoder_start_token_id` of model config''')

    _lowercase							:      List[str]      							=parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.dict_path,
        args.config_yaml_path,
        encoder_config_path=args.encoder_config_path,
        decoder_config_path=args.decoder_config_path,
        add_adapter=args.add_adapter,
        adapter_kernel_size=args.adapter_kernel_size,
        adapter_stride=args.adapter_stride,
        decoder_start_token_id=args.start_token_id,
        encoder_output_dim=args.encoder_output_dim,
    )







import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
    raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
_lowercase							:      str      							=False
_lowercase							:      Optional[Any]      							=False



def A__  (			lowercase:     Namespace					)				->		Optional[int]:
       return TrainCommand(lowercase					)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				SCREAMING_SNAKE_CASE__    :					ArgumentParser     )    ->				Dict:
         A						:     Optional[Any]         =parser.add_parser('train'      ,	help='CLI tool to train a model on a task.'     )

         train_parser.add_argument(
             '--train_data'      ,	type=SCREAMING_SNAKE_CASE__      ,	required=SCREAMING_SNAKE_CASE__      ,	help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'      ,	)
         train_parser.add_argument(
             '--column_label'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=0      ,	help='Column of the dataset csv file with example labels.'     )
         train_parser.add_argument(
             '--column_text'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=1      ,	help='Column of the dataset csv file with example texts.'     )
         train_parser.add_argument(
             '--column_id'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=2      ,	help='Column of the dataset csv file with example ids.'     )
         train_parser.add_argument(
             '--skip_first_row'      ,	action='store_true'      ,	help='Skip the first row of the csv file (headers).'     )

         train_parser.add_argument('--validation_data'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=''      ,	help='path to validation dataset.'     )
         train_parser.add_argument(
             '--validation_split'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=0.1      ,	help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'      ,	)

         train_parser.add_argument('--output'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='./'      ,	help='path to saved the trained model.'     )

         train_parser.add_argument(
             '--task'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='text_classification'      ,	help='Task to train the model on.'     )
         train_parser.add_argument(
             '--model'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='bert-base-uncased'      ,	help='Model\'s name or path to stored model.'     )
         train_parser.add_argument('--train_batch_size'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=32      ,	help='Batch size for training.'     )
         train_parser.add_argument('--valid_batch_size'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=64      ,	help='Batch size for validation.'     )
         train_parser.add_argument('--learning_rate'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=3e-5      ,	help='Learning rate.'     )
         train_parser.add_argument('--adam_epsilon'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=1e-08      ,	help='Epsilon for Adam optimizer.'     )
         train_parser.set_defaults(func=SCREAMING_SNAKE_CASE__     )







  def __init__(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Namespace     )    ->				List[Any]:
         A						:     Optional[int]         =logging.get_logger('transformers-cli/training'     )

         A						:     Dict         ='tf' if is_tf_available() else 'torch'

         os.makedirs(args.output      ,	exist_ok=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =args.output

         A						:     List[str]         =args.column_label
         A						:     int         =args.column_text
         A						:     Union[str, Any]         =args.column_id

         self.logger.info(f'Loading {args.task} pipeline for {args.model}'     )
         if args.task == "text_classification":
                A						:     Optional[Any]         =TextClassificationPipeline.from_pretrained(args.model     )
         elif args.task == "token_classification":
                raise NotImplementedError
         elif args.task == "question_answering":
                raise NotImplementedError

         self.logger.info(f'Loading dataset from {args.train_data}'     )
         A						:     Tuple         =Processor.create_from_csv(
             args.train_data      ,	column_label=args.column_label      ,	column_text=args.column_text      ,	column_id=args.column_id      ,	skip_first_row=args.skip_first_row      ,	)
         A						:     Dict         =None
         if args.validation_data:
                self.logger.info(f'Loading validation dataset from {args.validation_data}'     )
                A						:     List[Any]         =Processor.create_from_csv(
                    args.validation_data      ,	column_label=args.column_label      ,	column_text=args.column_text      ,	column_id=args.column_id      ,	skip_first_row=args.skip_first_row      ,	)

         A						:     Optional[Any]         =args.validation_split
         A						:     str         =args.train_batch_size
         A						:     Any         =args.valid_batch_size
         A						:     Dict         =args.learning_rate
         A						:     List[str]         =args.adam_epsilon







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Union[str, Any]:
         if self.framework == "tf":
                return self.run_tf()
         return self.run_torch()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				List[str]:
         raise NotImplementedError







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				str:
         self.pipeline.fit(
             self.train_dataset      ,	validation_data=self.valid_dataset      ,	validation_split=self.validation_split      ,	learning_rate=self.learning_rate      ,	adam_epsilon=self.adam_epsilon      ,	train_batch_size=self.train_batch_size      ,	valid_batch_size=self.valid_batch_size      ,	)

         # Save trained pipeline
         self.pipeline.save_pretrained(self.output     )






