



'''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     UpperCAmelCase_       :
    """simple docstring"""







    lowercase					=		MBartConfig
    lowercase					=		{}
    lowercase					=		"gelu"




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




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    Union[str, Any]    						=      ids_tensor([self.batch_size, self.seq_length - 1] ,     self.vocab_size						)
          snake_case__  :    int    						=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						) ,     1						)
          snake_case__  :    Any    						=      tf.concat([input_ids, eos_tensor] ,     axis=1						)

          snake_case__  :    Optional[int]    						=      ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)

          snake_case__  :    Union[str, Any]    						=      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 ,     )
          snake_case__  :    List[Any]    						=      prepare_mbart_inputs_dict(snake_case_ ,     snake_case_ ,     snake_case_						)
          return config, inputs_dict




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     Dict ,     snake_case_					:     str						):
          snake_case__  :    Union[str, Any]    						=      TFMBartModel(config=snake_case_						).get_decoder()
          snake_case__  :    Any    						=      inputs_dict["""input_ids"""]

          snake_case__  :    Union[str, Any]    						=      input_ids[:1, :]
          snake_case__  :    Any    						=      inputs_dict["""attention_mask"""][:1, :]
          snake_case__  :    Optional[Any]    						=      inputs_dict["""head_mask"""]
          snake_case__  :    Optional[int]    						=      1

          # first forward pass
          snake_case__  :    Dict    						=      model(snake_case_ ,     attention_mask=snake_case_ ,     head_mask=snake_case_ ,     use_cache=snake_case_						)

          snake_case__						,				snake_case__  :    List[Any]    						=      outputs.to_tuple()
          snake_case__  :    Tuple    						=      past_key_values[1]





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=None     ,   _lowerCAmelCase=None     ,   _lowerCAmelCase=None     ,   _lowerCAmelCase=None     ,   _lowerCAmelCase=None     ,   )		-> str:
      if attention_mask is None:
            snake_case__  :    Union[str, Any]    						=      tf.cast(tf.math.not_equal(_lowerCAmelCase     ,   config.pad_token_id		)     ,   tf.inta		)
      if decoder_attention_mask is None:
            snake_case__  :    Dict    						=      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:
            snake_case__  :    Dict    						=      tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
      if decoder_head_mask is None:
            snake_case__  :    int    						=      tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
      if cross_attn_head_mask is None:
            snake_case__  :    Union[str, Any]    						=      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     UpperCAmelCase_       (       _a		,		_a		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
    lowercase					=		(TFMBartForConditionalGeneration,) if is_tf_available() else ()
    lowercase					=		(
        {
            "conversational": TFMBartForConditionalGeneration,
            "feature-extraction": TFMBartModel,
            "summarization": TFMBartForConditionalGeneration,
            "text2text-generation": TFMBartForConditionalGeneration,
            "translation": TFMBartForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    lowercase					=		True
    lowercase					=		False
    lowercase					=		False




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     List[str] ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Tuple ,     snake_case_					:     Any ,     snake_case_					:     List[str]						):
          if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
                # Exception encountered when calling layer '...'
                return True

          return False




    def lowerCamelCase       (    self					:     str						):
          snake_case__  :    int    						=      TFMBartModelTester(self						)
          snake_case__  :    Optional[int]    						=      ConfigTester(self ,     config_class=snake_case_						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          self.config_tester.run_common_tests()




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    Optional[Any]    						=      self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*snake_case_						)







@require_sentencepiece
@require_tokenizers
@require_tf
class     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    lowercase					=		[
        " UN Chief Says There Is No Military Solution in Syria",
    ]
    lowercase					=		[
        "Şeful ONU declară că nu există o soluţie militară în Siria",
    ]
    lowercase					=		"facebook/mbart-large-en-ro"




    @cached_property
    def lowerCamelCase       (    self					:     Optional[Any]						):
          return AutoTokenizer.from_pretrained(self.model_name						)




    @cached_property
    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    Any    						=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
          return model




    def lowerCamelCase       (    self					:     Union[str, Any] ,     **snake_case_					:     int						):
          snake_case__  :    Tuple    						=      self.translate_src_text(**snake_case_						)
          self.assertListEqual(self.expected_text ,     snake_case_						)




    def lowerCamelCase       (    self					:     List[str] ,     **snake_case_					:     Any						):
          snake_case__  :    Tuple    						=      self.tokenizer(self.src_text ,     **snake_case_ ,     return_tensors="""tf"""						)
          snake_case__  :    List[str]    						=      self.model.generate(
              model_inputs.input_ids ,     attention_mask=model_inputs.attention_mask ,     num_beams=2						)
          snake_case__  :    str    						=      self.tokenizer.batch_decode(snake_case_ ,     skip_special_tokens=snake_case_						)
          return generated_words




    @slow
    def lowerCamelCase       (    self					:     int						):
          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()
__a          =		logging.get_logger(__name__)

__a          =		{
    "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",
}
__a          =		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Union[str, Any]:
      for attribute in key.split("""."""		):
            snake_case__  :    Dict    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)

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

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





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      snake_case__  :    Union[str, Any]    						=      []
      snake_case__  :    Dict    						=      fairseq_model.state_dict()

      snake_case__  :    List[Any]    						=      hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      snake_case__  :    Optional[int]    						=      None

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

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

      return proj_weight





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Dict:
      snake_case__  :    int    						=      full_name.split("""conv_layers."""		)[-1]
      snake_case__  :    Dict    						=      name.split("""."""		)
      snake_case__  :    Any    						=      int(items[0]		)
      snake_case__  :    Optional[Any]    						=      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."
                  )
                  snake_case__  :    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."
                  )
                  snake_case__  :    str    						=      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."
                  )
                  snake_case__  :    Union[str, 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."
                  )
                  snake_case__  :    int    						=      value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
      else:
            unused_weights.append(_lowerCAmelCase		)





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      snake_case__						,				snake_case__  :    str    						=      emb.weight.shape
      snake_case__  :    List[str]    						=      nn.Linear(_lowerCAmelCase     ,   _lowerCAmelCase     ,   bias=_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      emb.weight.data
      return lin_layer





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      with open(_lowerCAmelCase     ,   """r"""     ,   encoding="""utf-8"""		) as f:
            snake_case__  :    int    						=      f.readlines()
            snake_case__  :    List[Any]    						=      [line.split(""" """		)[0] for line in lines]

      snake_case__  :    Union[str, Any]    						=      len(_lowerCAmelCase		)

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

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





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   )		-> int:
      snake_case__  :    Optional[Any]    						=      WavaVecaConfig.from_pretrained(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      SpeechaTextaConfig.from_pretrained(
          _lowerCAmelCase     ,   vocab_size=_lowerCAmelCase     ,   decoder_layers=_lowerCAmelCase     ,   do_stable_layer_norm=_lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      WavaVecaFeatureExtractor(
          feature_size=1     ,   sampling_rate=16_000     ,   padding_value=0     ,   do_normalize=_lowerCAmelCase     ,   return_attention_mask=_lowerCAmelCase     ,   )

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

      # set weights for wav2vec2 encoder
      snake_case__  :    Optional[Any]    						=      WavaVecaModel(_lowerCAmelCase		)
      snake_case__  :    Dict    						=      recursively_load_weights_wavaveca(model.encoder     ,   _lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      SpeechaTextaForCausalLM(_lowerCAmelCase		)
      snake_case__						,				snake_case__  :    Tuple    						=      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,   strict=_lowerCAmelCase		)

      # set output linear layer
      unexpected_keys.remove("""embed_out"""		)
      snake_case__  :    Tuple    						=      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}"		)

      snake_case__  :    List[Any]    						=      SpeechEncoderDecoderModel(encoder=_lowerCAmelCase     ,   decoder=_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      False

      # add projection layer
      snake_case__  :    Union[str, Any]    						=      nn.Parameter(projection_layer.weight		)
      snake_case__  :    int    						=      nn.Parameter(projection_layer.bias		)

      snake_case__  :    Tuple    						=      create_vocab_dict(_lowerCAmelCase		)

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

      snake_case__  :    Tuple    						=      SpeechaTextaTokenizer(os.path.join(_lowerCAmelCase     ,   """vocab.json"""		)		)
      tokenizer.save_pretrained(_lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      hf_wavavec.config.to_dict()
      snake_case__  :    Tuple    						=      tokenizer.pad_token_id
      snake_case__  :    Optional[Any]    						=      tokenizer.bos_token_id
      snake_case__  :    int    						=      tokenizer.eos_token_id
      snake_case__  :    str    						=      """speech_to_text_2"""
      snake_case__  :    List[Any]    						=      """wav2vec2"""

      snake_case__  :    List[str]    						=      SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase		)

      hf_wavavec.save_pretrained(_lowerCAmelCase		)
      feature_extractor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		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_0224, type=int, help="Vocab size of decoder")
       parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

       __a          =		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,
       )
