'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel




@require_tf
class 	__A				:
    a__	:						Optional[int]	       =  MBartConfig
    a__	:						str	       =  {}
    a__	:						Optional[Any]	       =  """gelu"""



    def __init__(self   :   Dict       ,      __a   :   Any       ,      __a   :   int=13       ,      __a   :   str=7       ,      __a   :   Any=True       ,      __a   :   int=False       ,      __a   :   str=99       ,      __a   :   Any=32       ,      __a   :   Any=2       ,      __a   :   Any=4       ,      __a   :   int=37       ,      __a   :   str=0.1       ,      __a   :   Dict=0.1       ,      __a   :   List[str]=20       ,      __a   :   Optional[int]=2       ,      __a   :   Optional[int]=1       ,      __a   :   int=0       ,      ):
         UpperCAmelCase_	   =       parent
         UpperCAmelCase_	   =       batch_size
         UpperCAmelCase_	   =       seq_length
         UpperCAmelCase_	   =       is_training
         UpperCAmelCase_	   =       use_labels
         UpperCAmelCase_	   =       vocab_size
         UpperCAmelCase_	   =       hidden_size
         UpperCAmelCase_	   =       num_hidden_layers
         UpperCAmelCase_	   =       num_attention_heads
         UpperCAmelCase_	   =       intermediate_size
         UpperCAmelCase_	   =       hidden_dropout_prob
         UpperCAmelCase_	   =       attention_probs_dropout_prob
         UpperCAmelCase_	   =       max_position_embeddings
         UpperCAmelCase_	   =       eos_token_id
         UpperCAmelCase_	   =       pad_token_id
         UpperCAmelCase_	   =       bos_token_id



    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       ids_tensor([self.batch_size, self.seq_length - 1]       ,      self.vocab_size			)
         UpperCAmelCase_	   =       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)       ,      1			)
         UpperCAmelCase_	   =       tf.concat([input_ids, eos_tensor]       ,      axis=1			)

         UpperCAmelCase_	   =       ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size			)

         UpperCAmelCase_	   =       self.config_cls(
             vocab_size=self.vocab_size       ,      d_model=self.hidden_size       ,      encoder_layers=self.num_hidden_layers       ,      decoder_layers=self.num_hidden_layers       ,      encoder_attention_heads=self.num_attention_heads       ,      decoder_attention_heads=self.num_attention_heads       ,      encoder_ffn_dim=self.intermediate_size       ,      decoder_ffn_dim=self.intermediate_size       ,      dropout=self.hidden_dropout_prob       ,      attention_dropout=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      eos_token_ids=[2]       ,      bos_token_id=self.bos_token_id       ,      pad_token_id=self.pad_token_id       ,      decoder_start_token_id=self.pad_token_id       ,      **self.config_updates       ,      )
         UpperCAmelCase_	   =       prepare_mbart_inputs_dict(__a       ,      __a       ,      __a			)
         return config, inputs_dict






    def  _lowercase						(self   :   Any       ,      __a   :   Dict       ,      __a   :   str			):
         UpperCAmelCase_	   =       TFMBartModel(config=__a			).get_decoder()
         UpperCAmelCase_	   =       inputs_dict["input_ids"]

         UpperCAmelCase_	   =       input_ids[:1, :]
         UpperCAmelCase_	   =       inputs_dict["attention_mask"][:1, :]
         UpperCAmelCase_	   =       inputs_dict["head_mask"]
         UpperCAmelCase_	   =       1

         # first forward pass
         UpperCAmelCase_	   =       model(__a       ,      attention_mask=__a       ,      head_mask=__a       ,      use_cache=__a			)

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       outputs.to_tuple()
         UpperCAmelCase_	   =       past_key_values[1]





def      lowerCAmelCase_    ( snake_case_	:			Tuple ,   snake_case_	:			Tuple ,   snake_case_	:			Tuple ,   snake_case_	:			Tuple=None ,   snake_case_	:			List[str]=None ,   snake_case_	:			List[str]=None ,   snake_case_	:			int=None ,   snake_case_	:			Optional[int]=None ,   )   ->				int:


     '''simple docstring'''




     if attention_mask is None:
          UpperCAmelCase_	   =       tf.cast(tf.math.not_equal(snake_case_ ,   config.pad_token_id      ) ,   tf.inta      )
     if decoder_attention_mask is None:
          UpperCAmelCase_	   =       tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape ,   dtype=tf.inta      ),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,   config.pad_token_id      ) ,   tf.inta      ),
              ] ,   axis=-1 ,   )
     if head_mask is None:
          UpperCAmelCase_	   =       tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
     if decoder_head_mask is None:
          UpperCAmelCase_	   =       tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
     if cross_attn_head_mask is None:
          UpperCAmelCase_	   =       tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }




@require_tf
class 	__A				(			UpperCamelCase__		,     UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						str	       =  (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
    a__	:						Any	       =  (TFMBartForConditionalGeneration,) if is_tf_available() else ()
    a__	:						Optional[int]	       =  (
        {
            """conversational""": TFMBartForConditionalGeneration,
            """feature-extraction""": TFMBartModel,
            """summarization""": TFMBartForConditionalGeneration,
            """text2text-generation""": TFMBartForConditionalGeneration,
            """translation""": TFMBartForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    a__	:						str	       =  True
    a__	:						str	       =  False
    a__	:						List[str]	       =  False



    def  _lowercase						(self   :   Optional[int]       ,      __a   :   List[Any]       ,      __a   :   List[Any]       ,      __a   :   Optional[int]       ,      __a   :   Any       ,      __a   :   Any			):
         if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
              # Exception encountered when calling layer '...'
              return True

         return False



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       TFMBartModelTester(self			)
         UpperCAmelCase_	   =       ConfigTester(self       ,      config_class=__a			)



    def  _lowercase						(self   :   Any			):
         self.config_tester.run_common_tests()






    def  _lowercase						(self   :   List[Any]			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*__a			)




@require_sentencepiece
@require_tokenizers
@require_tf
class 	__A				(			unittest.TestCase     ):
    a__	:						str	       =  [
        """ UN Chief Says There Is No Military Solution in Syria""",
    ]
    a__	:						Optional[Any]	       =  [
        """Şeful ONU declară că nu există o soluţie militară în Siria""",
    ]
    a__	:						List[Any]	       =  """facebook/mbart-large-en-ro"""



    @cached_property
    def  _lowercase						(self   :   Dict			):
         return AutoTokenizer.from_pretrained(self.model_name			)



    @cached_property
    def  _lowercase						(self   :   int			):
         UpperCAmelCase_	   =       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
         return model



    def  _lowercase						(self   :   Dict       ,      **__a   :   Optional[int]			):
         UpperCAmelCase_	   =       self.translate_src_text(**__a			)
         self.assertListEqual(self.expected_text       ,      __a			)



    def  _lowercase						(self   :   Union[str, Any]       ,      **__a   :   Optional[Any]			):
         UpperCAmelCase_	   =       self.tokenizer(self.src_text       ,      **__a       ,      return_tensors="tf"			)
         UpperCAmelCase_	   =       self.model.generate(
             model_inputs.input_ids       ,      attention_mask=model_inputs.attention_mask       ,      num_beams=2			)
         UpperCAmelCase_	   =       self.tokenizer.batch_decode(__a       ,      skip_special_tokens=__a			)
         return generated_words






    @slow
    def  _lowercase						(self   :   Union[str, Any]			):
         self._assert_generated_batch_equal_expected()





'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:					int  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Dict  ={
    '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',
}
SCREAMING_SNAKE_CASE_:					List[str]  =[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			List[Any] ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			int ,   snake_case_	:			Any      )   ->				Optional[int]:


     '''simple docstring'''




     for attribute in key.split("."      ):
          UpperCAmelCase_	   =       getattr(snake_case_ ,   snake_case_      )

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

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





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			List[str]      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       []
     UpperCAmelCase_	   =       fairseq_model.state_dict()

     UpperCAmelCase_	   =       hf_model.feature_extractor

     # if encoder has different dim to decoder -> use proj_weight
     UpperCAmelCase_	   =       None

     for name, value in fairseq_dict.items():
          UpperCAmelCase_	   =       False
          if "conv_layers" in name:
               load_conv_layer(
                   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   hf_model.config.feat_extract_norm == "group" ,   )
               UpperCAmelCase_	   =       True
          elif name.split("."      )[0] == "proj":
               UpperCAmelCase_	   =       fairseq_model.proj
               UpperCAmelCase_	   =       True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
                         UpperCAmelCase_	   =       True
                         if "*" in mapped_key:
                              UpperCAmelCase_	   =       name.split(snake_case_      )[0].split("."      )[-2]
                              UpperCAmelCase_	   =       mapped_key.replace("*" ,   snake_case_      )
                         if "weight_g" in name:
                              UpperCAmelCase_	   =       "weight_g"
                         elif "weight_v" in name:
                              UpperCAmelCase_	   =       "weight_v"
                         elif "bias" in name:
                              UpperCAmelCase_	   =       "bias"
                         elif "weight" in name:
                              UpperCAmelCase_	   =       "weight"
                         else:
                              UpperCAmelCase_	   =       None
                         set_recursively(snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_      )
                    continue
          if not is_used:
               unused_weights.append(snake_case_      )

     logger.warning(f"""Unused weights: {unused_weights}"""      )

     return proj_weight





def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			List[Any] ,   snake_case_	:			str ,   snake_case_	:			Any ,   snake_case_	:			int      )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       full_name.split("conv_layers."      )[-1]
     UpperCAmelCase_	   =       name.split("."      )
     UpperCAmelCase_	   =       int(items[0]      )
     UpperCAmelCase_	   =       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."""
               )
               UpperCAmelCase_	   =       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."""
               )
               UpperCAmelCase_	   =       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."
               )
               UpperCAmelCase_	   =       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."""
               )
               UpperCAmelCase_	   =       value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
     else:
          unused_weights.append(snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			Any      )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_   ,			UpperCAmelCase_	   =       emb.weight.shape
     UpperCAmelCase_	   =       nn.Linear(snake_case_ ,   snake_case_ ,   bias=snake_case_      )
     UpperCAmelCase_	   =       emb.weight.data
     return lin_layer





def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				List[str]:


     '''simple docstring'''




     with open(snake_case_ ,   "r" ,   encoding="utf-8"      ) as f:
          UpperCAmelCase_	   =       f.readlines()
          UpperCAmelCase_	   =       [line.split(" "      )[0] for line in lines]

     UpperCAmelCase_	   =       len(snake_case_      )

     UpperCAmelCase_	   =       {
         "<s>": 0,
         "<pad>": 1,
         "</s>": 2,
         "<unk>": 3,
     }

     vocab_dict.update(dict(zip(snake_case_ ,   range(4 ,   num_words + 4      )      )      )      )
     return vocab_dict





@torch.no_grad()
def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			Tuple ,   snake_case_	:			List[str] ,   snake_case_	:			Dict ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			Tuple ,   snake_case_	:			Any ,   )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       WavaVecaConfig.from_pretrained(snake_case_      )
     UpperCAmelCase_	   =       SpeechaTextaConfig.from_pretrained(
         snake_case_ ,   vocab_size=snake_case_ ,   decoder_layers=snake_case_ ,   do_stable_layer_norm=snake_case_      )

     UpperCAmelCase_	   =       WavaVecaFeatureExtractor(
         feature_size=1 ,   sampling_rate=1_60_00 ,   padding_value=0 ,   do_normalize=snake_case_ ,   return_attention_mask=snake_case_ ,   )

     UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path] ,   arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
     UpperCAmelCase_	   =       model[0].eval()

     # set weights for wav2vec2 encoder
     UpperCAmelCase_	   =       WavaVecaModel(snake_case_      )
     UpperCAmelCase_	   =       recursively_load_weights_wavaveca(model.encoder ,   snake_case_      )

     UpperCAmelCase_	   =       SpeechaTextaForCausalLM(snake_case_      )
     UpperCAmelCase_   ,			UpperCAmelCase_	   =       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,   strict=snake_case_      )

     # set output linear layer
     unexpected_keys.remove("embed_out"      )
     UpperCAmelCase_	   =       nn.Parameter(model.decoder.embed_out.detach()      )

     # layer norm is init to identity matrix so leaving it is fine
     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}"""      )

     UpperCAmelCase_	   =       SpeechEncoderDecoderModel(encoder=snake_case_ ,   decoder=snake_case_      )
     UpperCAmelCase_	   =       False

     # add projection layer
     UpperCAmelCase_	   =       nn.Parameter(projection_layer.weight      )
     UpperCAmelCase_	   =       nn.Parameter(projection_layer.bias      )

     UpperCAmelCase_	   =       create_vocab_dict(snake_case_      )

     with open(os.path.join(snake_case_ ,   "vocab.json"      ) ,   "w"      ) as fp:
          json.dump(snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       SpeechaTextaTokenizer(os.path.join(snake_case_ ,   "vocab.json"      )      )
     tokenizer.save_pretrained(snake_case_      )

     UpperCAmelCase_	   =       hf_wavavec.config.to_dict()
     UpperCAmelCase_	   =       tokenizer.pad_token_id
     UpperCAmelCase_	   =       tokenizer.bos_token_id
     UpperCAmelCase_	   =       tokenizer.eos_token_id
     UpperCAmelCase_	   =       "speech_to_text_2"
     UpperCAmelCase_	   =       "wav2vec2"

     UpperCAmelCase_	   =       SpeechEncoderDecoderConfig.from_dict(snake_case_      )

     hf_wavavec.save_pretrained(snake_case_      )
     feature_extractor.save_pretrained(snake_case_      )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Optional[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(
        '--encoder_config_path',
        default='facebook/wav2vec2-large-lv60',
        type=str,
        help='Path to hf encoder wav2vec2 checkpoint config',
    )
    parser.add_argument(
        '--decoder_config_path',
        default='facebook/s2t-small-mustc-en-fr-st',
        type=str,
        help='Path to hf decoder s2t checkpoint config',
    )
    parser.add_argument('--vocab_size', default=1_02_24, type=int, help='Vocab size of decoder')
    parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

    SCREAMING_SNAKE_CASE_:					Dict  =parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.dict_path,
        encoder_config_path=args.encoder_config_path,
        decoder_config_path=args.decoder_config_path,
        vocab_size=args.vocab_size,
        num_decoder_layers=args.num_decoder_layers,
    )




