





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, 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, TFPegasusForConditionalGeneration, TFPegasusModel



@require_tf
class 							_UpperCamelCase					:
       _UpperCamelCase				:					Optional[Any]         =   PegasusConfig
       _UpperCamelCase				:					Any         =   {}
       _UpperCamelCase				:					int         =   '''gelu'''

       def __init__(		self: Tuple					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: Optional[int]=13					,   _SCREAMING_SNAKE_CASE: int=7					,   _SCREAMING_SNAKE_CASE: List[str]=True					,   _SCREAMING_SNAKE_CASE: Any=False					,   _SCREAMING_SNAKE_CASE: List[Any]=99					,   _SCREAMING_SNAKE_CASE: Tuple=32					,   _SCREAMING_SNAKE_CASE: Any=2					,   _SCREAMING_SNAKE_CASE: List[str]=4					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=37					,   _SCREAMING_SNAKE_CASE: Dict=0.1					,   _SCREAMING_SNAKE_CASE: List[str]=0.1					,   _SCREAMING_SNAKE_CASE: Dict=40					,   _SCREAMING_SNAKE_CASE: Any=2					,   _SCREAMING_SNAKE_CASE: Dict=1					,   _SCREAMING_SNAKE_CASE: int=0					,   )       -> List[Any]:




              """simple docstring"""


              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: str		)       -> Optional[Any]:




              """simple docstring"""


              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_pegasus_inputs_dict(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              return config, inputs_dict



       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  TFPegasusModel(config=_SCREAMING_SNAKE_CASE		).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(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   head_mask=_SCREAMING_SNAKE_CASE					,   use_cache=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_       , UpperCamelCase_          =  outputs.to_tuple()

              # create hypothetical next token and extent to next_input_ids
              UpperCamelCase_          =  ids_tensor((self.batch_size, 3)					,   config.vocab_size		)
              UpperCamelCase_          =  tf.cast(ids_tensor((self.batch_size, 3)					,   2		)					,   tf.inta		)

              # append to next input_ids and
              UpperCamelCase_          =  tf.concat([input_ids, next_tokens]					,   axis=-1		)
              UpperCamelCase_          =  tf.concat([attention_mask, next_attn_mask]					,   axis=-1		)

              UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE		)[0]
              UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   past_key_values=_SCREAMING_SNAKE_CASE		)[0]

              self.parent.assertEqual(next_tokens.shape[1]					,   output_from_past.shape[1]		)

              # select random slice
              UpperCamelCase_          =  int(ids_tensor((1,)					,   output_from_past.shape[-1]		)		)
              UpperCamelCase_          =  output_from_no_past[:, -3:, random_slice_idx]
              UpperCamelCase_          =  output_from_past[:, :, random_slice_idx]

              # test that outputs are equal for slice
              tf.debugging.assert_near(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   rtol=1e-3		)




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		)     ->		List[str]:
       if attention_mask is None:
              UpperCamelCase_          =  tf.cast(tf.math.not_equal(UpperCamelCase_					,		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 							_UpperCamelCase					(	lowerCAmelCase_  ,						lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Optional[Any]         =   (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
       _UpperCamelCase				:					Optional[int]         =   (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
       _UpperCamelCase				:					List[Any]         =   (
           {
               '''conversational''': TFPegasusForConditionalGeneration,
               '''feature-extraction''': TFPegasusModel,
               '''summarization''': TFPegasusForConditionalGeneration,
               '''text2text-generation''': TFPegasusForConditionalGeneration,
               '''translation''': TFPegasusForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       _UpperCamelCase				:					int         =   True
       _UpperCamelCase				:					str         =   False
       _UpperCamelCase				:					Dict         =   False

       def lowercase (		self: List[Any]		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  TFPegasusModelTester(self		)
              UpperCamelCase_          =  ConfigTester(self					,   config_class=_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Any		)       -> List[str]:




              """simple docstring"""


              self.config_tester.run_common_tests()



       def lowercase (		self: Optional[int]		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()
              self.model_tester.check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE		)



@require_sentencepiece
@require_tokenizers
@require_tf
class 							_UpperCamelCase					(	unittest.TestCase     ):
       _UpperCamelCase				:					Any         =   [
           ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
           ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
       ]
       _UpperCamelCase				:					List[str]         =   [
           '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
           ''' reduce the risk of wildfires.''',
           '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
       ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
       _UpperCamelCase				:					List[Any]         =   '''google/pegasus-xsum'''

       @cached_property
       def lowercase (		self: Union[str, Any]		)       -> int:




              """simple docstring"""


              return AutoTokenizer.from_pretrained(self.model_name		)

       @cached_property
       def lowercase (		self: int		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
              return model

       def lowercase (		self: Tuple					,   **_SCREAMING_SNAKE_CASE: Optional[int]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  self.translate_src_text(**_SCREAMING_SNAKE_CASE		)
              assert self.expected_text == generated_words

       def lowercase (		self: str					,   **_SCREAMING_SNAKE_CASE: Optional[int]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.tokenizer(self.src_text					,   **_SCREAMING_SNAKE_CASE					,   padding=_SCREAMING_SNAKE_CASE					,   return_tensors="tf"		)
              UpperCamelCase_          =  self.model.generate(
                  model_inputs.input_ids					,   attention_mask=model_inputs.attention_mask					,   num_beams=2					,   use_cache=_SCREAMING_SNAKE_CASE					,   )
              UpperCamelCase_          =  self.tokenizer.batch_decode(generated_ids.numpy()					,   skip_special_tokens=_SCREAMING_SNAKE_CASE		)
              return generated_words



       @slow
       def lowercase (		self: Dict		)       -> str:




              """simple docstring"""


              self._assert_generated_batch_equal_expected()








import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_UpperCAmelCase        =				logging.get_logger('transformers.models.speecht5')

_UpperCAmelCase        =				{
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
_UpperCAmelCase        =				{
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
_UpperCAmelCase        =				{
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
_UpperCAmelCase        =				{
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
_UpperCAmelCase        =				{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
_UpperCAmelCase        =				{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
_UpperCAmelCase        =				{
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
_UpperCAmelCase        =				{
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
_UpperCAmelCase        =				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_UpperCAmelCase        =				{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase        =				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase        =				[]
_UpperCAmelCase        =				[
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
_UpperCAmelCase        =				IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
_UpperCAmelCase        =				IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
_UpperCAmelCase        =				IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Any:
       for attribute in key.split("."  ):
              UpperCamelCase_          =  getattr(UpperCamelCase_					,		UpperCamelCase_  )

       if weight_type is not None:
              UpperCamelCase_          =  getattr(UpperCamelCase_					,		UpperCamelCase_  ).shape
       else:
              UpperCamelCase_          =  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":
              UpperCamelCase_          =  value
       elif weight_type == "weight_g":
              UpperCamelCase_          =  value
       elif weight_type == "weight_v":
              UpperCamelCase_          =  value
       elif weight_type == "bias":
              UpperCamelCase_          =  value
       elif weight_type == "running_mean":
              UpperCamelCase_          =  value
       elif weight_type == "running_var":
              UpperCamelCase_          =  value
       elif weight_type == "num_batches_tracked":
              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_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple:
       for key in ignore_keys:
              if key.endswith(".*"  ):
                     if name.startswith(key[:-1]  ):
                            return True
              elif ".*." in key:
                     UpperCamelCase_       , UpperCamelCase_          =  key.split(".*."  )
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[Any]:
       UpperCamelCase_          =  []

       if task == "s2t":
              UpperCamelCase_          =  hf_model.speechta.encoder.prenet.feature_encoder
              UpperCamelCase_          =  MAPPING_S2T
              UpperCamelCase_          =  IGNORE_KEYS_S2T
       elif task == "t2s":
              UpperCamelCase_          =  None
              UpperCamelCase_          =  MAPPING_T2S
              UpperCamelCase_          =  IGNORE_KEYS_T2S
       elif task == "s2s":
              UpperCamelCase_          =  hf_model.speechta.encoder.prenet.feature_encoder
              UpperCamelCase_          =  MAPPING_S2S
              UpperCamelCase_          =  IGNORE_KEYS_S2S
       else:
              raise ValueError(F'''Unsupported task: {task}'''  )

       for name, value in fairseq_dict.items():
              if should_ignore(UpperCamelCase_					,		UpperCamelCase_  ):
                     logger.info(F'''{name} was ignored'''  )
                     continue

              UpperCamelCase_          =  False
              if "conv_layers" in name:
                     load_conv_layer(
                         UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		hf_model.config.feat_extract_norm == "group"					,		)
                     UpperCamelCase_          =  True
              else:
                     for key, mapped_key in MAPPING.items():
                            # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                            if "*" in key:
                                   UpperCamelCase_       , UpperCamelCase_          =  key.split(".*."  )
                                   if prefix in name and suffix in name:
                                          UpperCamelCase_          =  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                            if key in name:
                                   UpperCamelCase_          =  True
                                   if "*" in mapped_key:
                                          UpperCamelCase_          =  name.split(UpperCamelCase_  )[0].split("."  )[-2]
                                          UpperCamelCase_          =  mapped_key.replace("*"					,		UpperCamelCase_  )
                                   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"
                                   elif "running_mean" in name:
                                          UpperCamelCase_          =  "running_mean"
                                   elif "running_var" in name:
                                          UpperCamelCase_          =  "running_var"
                                   elif "num_batches_tracked" in name:
                                          UpperCamelCase_          =  "num_batches_tracked"
                                   else:
                                          UpperCamelCase_          =  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_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[str]:
       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:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                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:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                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:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[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:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[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(UpperCamelCase_  )




@torch.no_grad()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		)     ->		str:
       if config_path is not None:
              UpperCamelCase_          =  SpeechTaConfig.from_pretrained(UpperCamelCase_  )
       else:
              UpperCamelCase_          =  SpeechTaConfig()

       if task == "s2t":
              UpperCamelCase_          =  config.max_text_positions
              UpperCamelCase_          =  SpeechTaForSpeechToText(UpperCamelCase_  )
       elif task == "t2s":
              UpperCamelCase_          =  1876
              UpperCamelCase_          =  600
              UpperCamelCase_          =  config.max_speech_positions
              UpperCamelCase_          =  SpeechTaForTextToSpeech(UpperCamelCase_  )
       elif task == "s2s":
              UpperCamelCase_          =  1876
              UpperCamelCase_          =  config.max_speech_positions
              UpperCamelCase_          =  SpeechTaForSpeechToSpeech(UpperCamelCase_  )
       else:
              raise ValueError(F'''Unknown task name: {task}'''  )

       if vocab_path:
              UpperCamelCase_          =  SpeechTaTokenizer(UpperCamelCase_					,		model_max_length=config.max_text_positions  )

              # Mask token behaves like a normal word, i.e. include the space before it
              UpperCamelCase_          =  AddedToken("<mask>"					,		lstrip=UpperCamelCase_					,		rstrip=UpperCamelCase_  )
              UpperCamelCase_          =  mask_token
              tokenizer.add_special_tokens({"mask_token": mask_token}  )
              tokenizer.add_tokens(["<ctc_blank>"]  )

       UpperCamelCase_          =  SpeechTaFeatureExtractor()
       UpperCamelCase_          =  SpeechTaProcessor(tokenizer=UpperCamelCase_					,		feature_extractor=UpperCamelCase_  )
       processor.save_pretrained(UpperCamelCase_  )

       UpperCamelCase_          =  torch.load(UpperCamelCase_  )
       recursively_load_weights(fairseq_checkpoint["model"]					,		UpperCamelCase_					,		UpperCamelCase_  )

       model.save_pretrained(UpperCamelCase_  )

       if repo_id:
              print("Pushing to the hub..."  )
              processor.push_to_hub(UpperCamelCase_  )
              model.push_to_hub(UpperCamelCase_  )


if __name__ == "__main__":
     _UpperCAmelCase        =				argparse.ArgumentParser()
     parser.add_argument(
         '--task',
         default='s2t',
         type=str,
         help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
     )
     parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
     parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument(
         '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
     )
     parser.add_argument(
         '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
     )

     _UpperCAmelCase        =				parser.parse_args()
     convert_speechta_checkpoint(
         args.task,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.vocab_path,
         args.push_to_hub,
     )

