
"""simple docstring"""



from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Union[str, Any]=None ,					__lowerCamelCase   :       Optional[Any]=None		)     ->					Tuple:
  if attention_mask is None:
    _snake_case					     =       tf.cast(tf.math.not_equal(__lowerCamelCase ,					config.pad_token_id		) ,					tf.inta		)
  return {"input_ids": input_ids, "attention_mask": attention_mask}

@require_tf
class       lowerCAmelCase__				:
     __a						=					OPTConfig
     __a						=					{}
     __a						=					"""gelu"""







     def __init__(							self			:			Tuple    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Union[str, Any]=13    ,      _lowerCamelCase			:			List[Any]=7    ,      _lowerCamelCase			:			Optional[Any]=True    ,      _lowerCamelCase			:			Union[str, Any]=False    ,      _lowerCamelCase			:			Optional[int]=99    ,      _lowerCamelCase			:			Any=16    ,      _lowerCamelCase			:			Tuple=2    ,      _lowerCamelCase			:			Tuple=4    ,      _lowerCamelCase			:			int=4    ,      _lowerCamelCase			:			str="gelu"    ,      _lowerCamelCase			:			str=0.1    ,      _lowerCamelCase			:			List[str]=0.1    ,      _lowerCamelCase			:			List[str]=20    ,      _lowerCamelCase			:			Dict=2    ,      _lowerCamelCase			:			List[str]=1    ,      _lowerCamelCase			:			Tuple=0    ,      _lowerCamelCase			:			Optional[int]=16    ,      _lowerCamelCase			:			List[Any]=16    ,      ):
       _snake_case					     =       parent
       _snake_case					     =       batch_size
       _snake_case					     =       seq_length
       _snake_case					     =       is_training
       _snake_case					     =       use_labels
       _snake_case					     =       vocab_size
       _snake_case					     =       hidden_size
       _snake_case					     =       num_hidden_layers
       _snake_case					     =       num_attention_heads
       _snake_case					     =       intermediate_size
       _snake_case					     =       hidden_act
       _snake_case					     =       hidden_dropout_prob
       _snake_case					     =       attention_probs_dropout_prob
       _snake_case					     =       max_position_embeddings
       _snake_case					     =       eos_token_id
       _snake_case					     =       pad_token_id
       _snake_case					     =       bos_token_id
       _snake_case					     =       embed_dim
       _snake_case					     =       word_embed_proj_dim
       _snake_case					     =       False







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

       _snake_case					     =       self.config_cls(
           vocab_size=self.vocab_size    ,      hidden_size=self.hidden_size    ,      num_hidden_layers=self.num_hidden_layers    ,      num_attention_heads=self.num_attention_heads    ,      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_id=self.eos_token_id    ,      bos_token_id=self.bos_token_id    ,      pad_token_id=self.pad_token_id    ,      embed_dim=self.embed_dim    ,      word_embed_proj_dim=self.word_embed_proj_dim    ,      is_encoder_decoder=_lowerCamelCase    ,      **self.config_updates    ,      )
       _snake_case					     =       prepare_opt_inputs_dict(_lowerCamelCase    ,      _lowerCamelCase			)
       return config, inputs_dict







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			Any			):
       _snake_case					     =       TFOPTModel(config=_lowerCamelCase			)
       _snake_case					     =       inputs_dict['''input_ids''']

       _snake_case					     =       input_ids[:1, :]
       _snake_case					     =       inputs_dict['''attention_mask'''][:1, :]
       _snake_case					     =       1

       # first forward pass
       _snake_case					     =       model(_lowerCamelCase    ,      attention_mask=_lowerCamelCase    ,      use_cache=_lowerCamelCase			)

       _snake_case     ,					_snake_case					     =       outputs.to_tuple()

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

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

       _snake_case					     =       model(_lowerCamelCase    ,      attention_mask=_lowerCamelCase			)[0]
       _snake_case					     =       model(_lowerCamelCase    ,      attention_mask=_lowerCamelCase    ,      past_key_values=_lowerCamelCase			)[0]

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

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

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






@require_tf
class       lowerCAmelCase__				(						A_ ,	A_ ,	unittest.TestCase	):
     __a						=					(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
     __a						=					(TFOPTForCausalLM,) if is_tf_available() else ()
     __a						=					(
         {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
     )
     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					10







     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       TFOPTModelTester(self			)
       _snake_case					     =       ConfigTester(self    ,      config_class=_lowerCamelCase			)







     def      lowercase	(							self			:			List[str]			):
       self.config_tester.run_common_tests()







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

       def _get_word_embedding_weight(_lowerCamelCase			:			Optional[int]    ,      _lowerCamelCase			:			Dict			):
         if hasattr(_lowerCamelCase    ,      '''weight'''			):
           return embedding_layer.weight
         else:
           # Here we build the word embeddings weights if not exists.
           # And then we retry to get the attribute once built.
           model.build()
           if hasattr(_lowerCamelCase    ,      '''weight'''			):
             return embedding_layer.weight
           else:
             return None

       for model_class in self.all_model_classes:
         for size in [config.vocab_size - 10, config.vocab_size + 10]:
           # build the embeddings
           _snake_case					     =       model_class(config=_lowerCamelCase			)
           _snake_case					     =       _get_word_embedding_weight(_lowerCamelCase    ,      model.get_input_embeddings()			)
           _snake_case					     =       _get_word_embedding_weight(_lowerCamelCase    ,      model.get_output_embeddings()			)

           # reshape the embeddings
           model.resize_token_embeddings(_lowerCamelCase			)
           _snake_case					     =       _get_word_embedding_weight(_lowerCamelCase    ,      model.get_input_embeddings()			)
           _snake_case					     =       _get_word_embedding_weight(_lowerCamelCase    ,      model.get_output_embeddings()			)

           # check that the resized embeddings size matches the desired size.
           _snake_case					     =       size if size is not None else config.vocab_size

           self.assertEqual(new_input_embeddings.shape[0]    ,      _lowerCamelCase			)

           # check that weights remain the same after resizing
           _snake_case					     =       True
           for pa, pa in zip(old_input_embeddings.value()    ,      new_input_embeddings.value()			):
             if tf.math.reduce_sum(tf.math.abs(pa - pa			)			) > 0:
               _snake_case					     =       False
           self.assertTrue(_lowerCamelCase			)

           if old_output_embeddings is not None and new_output_embeddings is not None:
             self.assertEqual(new_output_embeddings.shape[0]    ,      _lowerCamelCase			)

             _snake_case					     =       True
             for pa, pa in zip(old_output_embeddings.value()    ,      new_output_embeddings.value()			):
               if tf.math.reduce_sum(tf.math.abs(pa - pa			)			) > 0:
                 _snake_case					     =       False
             self.assertTrue(_lowerCamelCase			)






def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any]		)     ->					List[str]:
  return tf.constant(__lowerCamelCase ,					dtype=tf.intaa		)

@require_tf
class       lowerCAmelCase__				(						unittest.TestCase	):
     __a						=					99







     def      lowercase	(							self			:			Tuple			):
       _snake_case					     =       tf.ones((4, 1)    ,      dtype=tf.intaa			) * 2
       _snake_case					     =       tf.concat([ids_tensor((4, 6)    ,      self.vocab_size - 3			) + 3, eos_column_vector]    ,      axis=1			)
       _snake_case					     =       input_ids.shape[0]
       _snake_case					     =       OPTConfig(
           vocab_size=self.vocab_size    ,      hidden_size=24    ,      num_hidden_layers=2    ,      num_attention_heads=2    ,      ffn_dim=32    ,      max_position_embeddings=48    ,      eos_token_id=2    ,      pad_token_id=1    ,      bos_token_id=0    ,      )
       return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class       lowerCAmelCase__				(						unittest.TestCase	):







     @slow
     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       TFOPTModel.from_pretrained('''facebook/opt-350m'''			)
       _snake_case					     =       _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]			)
       _snake_case					     =       tf.not_equal(_lowerCamelCase    ,      model.config.pad_token_id			)
       with tf.GradientTape():
         _snake_case					     =       model(input_ids=_lowerCamelCase    ,      attention_mask=_lowerCamelCase			).last_hidden_state
       _snake_case					     =       (1, 11, 512)
       self.assertEqual(output.shape    ,      _lowerCamelCase			)
       _snake_case					     =       tf.constant(
           [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]]			)
       self.assertTrue(np.allclose(output[:, :3, :3]    ,      _lowerCamelCase    ,      atol=4e-3			)			)

       _snake_case					     =       tf.function(_lowerCamelCase    ,      jit_compile=_lowerCamelCase			)
       _snake_case					     =       xla_generate(_lowerCamelCase    ,      _lowerCamelCase			)[0]
       self.assertTrue(np.allclose(output[:, :3, :3]    ,      _lowerCamelCase    ,      atol=4e-2			)			)






@require_tf
@slow
class       lowerCAmelCase__				(						unittest.TestCase	):







     def      lowercase	(							self			:			Optional[Any]			):
       super().setUp()
       _snake_case					     =       '''facebook/opt-350m'''







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       TFOPTForCausalLM.from_pretrained(self.path_model			)
       _snake_case					     =       GPTaTokenizer.from_pretrained(self.path_model			)

       _snake_case					     =       [
           '''Today is a beautiful day and I want to''',
           '''In the city of''',
           '''Paris is the capital of France and''',
           '''Computers and mobile phones have taken''',
       ]
       # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
       _snake_case					     =       tokenizer(_lowerCamelCase    ,      return_tensors='''tf'''    ,      padding=_lowerCamelCase    ,      add_special_tokens=_lowerCamelCase			)
       _snake_case					     =       tf.math.reduce_mean(model(inputs.input_ids    ,      attention_mask=inputs.attention_mask			)[0]    ,      axis=-1			)
       _snake_case					     =       tf.constant(
           [
               [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
               [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
               [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
               [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
           ]			)
       self.assertTrue(np.allclose(_lowerCamelCase    ,      _lowerCamelCase    ,      atol=1e-4			)			)

       _snake_case					     =       tf.function(_lowerCamelCase    ,      jit_compile=_lowerCamelCase			)
       _snake_case					     =       tf.math.reduce_mean(xla_generate(inputs.input_ids    ,      attention_mask=inputs.attention_mask			)[0]    ,      axis=-1			)
       self.assertTrue(np.allclose(_lowerCamelCase    ,      _lowerCamelCase    ,      atol=1e-4			)			)






@require_tf
@slow
class       lowerCAmelCase__				(						unittest.TestCase	):







     @property
     def      lowercase	(							self			:			List[str]			):
       return [
           "Today is a beautiful day and I want",
           "In the city of",
           "Paris is the capital of France and",
           "Computers and mobile phones have taken",
       ]







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       '''facebook/opt-125m'''

       _snake_case					     =       [
           '''Today is a beautiful day and I want to''',
           '''In the city of New York, the city''',
           '''Paris is the capital of France and the capital''',
           '''Computers and mobile phones have taken over the''',
       ]

       _snake_case					     =       []
       _snake_case					     =       GPTaTokenizer.from_pretrained(_lowerCamelCase			)
       _snake_case					     =       TFOPTForCausalLM.from_pretrained(_lowerCamelCase			)

       for prompt in self.prompts:
         _snake_case					     =       tokenizer(_lowerCamelCase    ,      return_tensors='''tf'''			).input_ids

         _snake_case					     =       model.generate(_lowerCamelCase    ,      max_length=10			)

         _snake_case					     =       tokenizer.batch_decode(_lowerCamelCase    ,      skip_special_tokens=_lowerCamelCase			)
         predicted_outputs += generated_string

       self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			str			):
       _snake_case					     =       '''facebook/opt-350m'''

       _snake_case					     =       GPTaTokenizer.from_pretrained(_lowerCamelCase			)
       _snake_case					     =       TFOPTForCausalLM.from_pretrained(_lowerCamelCase			)

       _snake_case					     =       '''left'''

       # use different length sentences to test batching
       _snake_case					     =       [
           '''Hello, my dog is a little''',
           '''Today, I''',
       ]

       _snake_case					     =       tokenizer(_lowerCamelCase    ,      return_tensors='''tf'''    ,      padding=_lowerCamelCase			)
       _snake_case					     =       inputs['''input_ids''']

       _snake_case					     =       model.generate(input_ids=_lowerCamelCase    ,      attention_mask=inputs['''attention_mask''']			)

       _snake_case					     =       tokenizer(sentences[0]    ,      return_tensors='''tf'''			).input_ids
       _snake_case					     =       model.generate(input_ids=_lowerCamelCase			)

       _snake_case					     =       inputs_non_padded.shape[-1] - tf.math.reduce_sum(
           tf.cast(inputs['''attention_mask'''][-1]    ,      tf.intaa			)			)
       _snake_case					     =       tokenizer(sentences[1]    ,      return_tensors='''tf'''			).input_ids
       _snake_case					     =       model.generate(input_ids=_lowerCamelCase    ,      max_length=model.config.max_length - num_paddings			)

       _snake_case					     =       tokenizer.batch_decode(_lowerCamelCase    ,      skip_special_tokens=_lowerCamelCase			)
       _snake_case					     =       tokenizer.decode(output_non_padded[0]    ,      skip_special_tokens=_lowerCamelCase			)
       _snake_case					     =       tokenizer.decode(output_padded[0]    ,      skip_special_tokens=_lowerCamelCase			)

       _snake_case					     =       [
           '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
           '''Today, I was in the middle of a conversation with a friend about the''',
       ]
       self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)
       self.assertListEqual(_lowerCamelCase    ,      [non_padded_sentence, padded_sentence]			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       '''facebook/opt-350m'''

       _snake_case					     =       [
           '''Today is a beautiful day and I want to''',
           '''In the city of San Francisco, the city''',
           '''Paris is the capital of France and the capital''',
           '''Computers and mobile phones have taken over the''',
       ]

       _snake_case					     =       []
       _snake_case					     =       GPTaTokenizer.from_pretrained(_lowerCamelCase			)
       _snake_case					     =       TFOPTForCausalLM.from_pretrained(_lowerCamelCase			)

       for prompt in self.prompts:
         _snake_case					     =       tokenizer(_lowerCamelCase    ,      return_tensors='''tf'''			).input_ids

         _snake_case					     =       model.generate(_lowerCamelCase    ,      max_length=10			)

         _snake_case					     =       tokenizer.batch_decode(_lowerCamelCase    ,      skip_special_tokens=_lowerCamelCase			)
         predicted_outputs += generated_string

       self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)





"""simple docstring"""



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 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       Dict		)     ->					List[Any]:
  for attribute in key.split('''.'''		):
    _snake_case					     =       getattr(__lowerCamelCase ,					__lowerCamelCase		)

  if weight_type is not None:
    _snake_case					     =       getattr(__lowerCamelCase ,					__lowerCamelCase		).shape
  else:
    _snake_case					     =       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":
    _snake_case					     =       value
  elif weight_type == "weight_g":
    _snake_case					     =       value
  elif weight_type == "weight_v":
    _snake_case					     =       value
  elif weight_type == "bias":
    _snake_case					     =       value
  elif weight_type == "running_mean":
    _snake_case					     =       value
  elif weight_type == "running_var":
    _snake_case					     =       value
  elif weight_type == "num_batches_tracked":
    _snake_case					     =       value
  else:
    _snake_case					     =       value

  logger.info(f'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Optional[int]		)     ->					List[str]:
  for key in ignore_keys:
    if key.endswith('''.*'''		):
      if name.startswith(key[:-1]		):
        return True
    elif ".*." in key:
      _snake_case     ,					_snake_case					     =       key.split('''.*.'''		)
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Tuple		)     ->					Optional[Any]:
  _snake_case					     =       []

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

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

    _snake_case					     =       False
    if "conv_layers" in name:
      load_conv_layer(
          __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					hf_model.config.feat_extract_norm == '''group''' ,					)
      _snake_case					     =       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:
          _snake_case     ,					_snake_case					     =       key.split('''.*.'''		)
          if prefix in name and suffix in name:
            _snake_case					     =       suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          _snake_case					     =       True
          if "*" in mapped_key:
            _snake_case					     =       name.split(__lowerCamelCase		)[0].split('''.'''		)[-2]
            _snake_case					     =       mapped_key.replace('''*''' ,					__lowerCamelCase		)
          if "weight_g" in name:
            _snake_case					     =       '''weight_g'''
          elif "weight_v" in name:
            _snake_case					     =       '''weight_v'''
          elif "bias" in name:
            _snake_case					     =       '''bias'''
          elif "weight" in name:
            _snake_case					     =       '''weight'''
          elif "running_mean" in name:
            _snake_case					     =       '''running_mean'''
          elif "running_var" in name:
            _snake_case					     =       '''running_var'''
          elif "num_batches_tracked" in name:
            _snake_case					     =       '''num_batches_tracked'''
          else:
            _snake_case					     =       None
          set_recursively(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
        continue
    if not is_used:
      unused_weights.append(__lowerCamelCase		)

  logger.warning(f'''Unused weights: {unused_weights}'''		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Tuple		)     ->					List[Any]:
  _snake_case					     =       full_name.split('''conv_layers.'''		)[-1]
  _snake_case					     =       name.split('''.'''		)
  _snake_case					     =       int(items[0]		)
  _snake_case					     =       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.'''		)
      _snake_case					     =       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.'''		)
      _snake_case					     =       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.'''		)
      _snake_case					     =       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.'''		)
      _snake_case					     =       value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
  else:
    unused_weights.append(__lowerCamelCase		)
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Optional[int]=None ,					__lowerCamelCase   :       int=None ,					__lowerCamelCase   :       Union[str, Any]=None ,					)     ->					Dict:
  if config_path is not None:
    _snake_case					     =       SpeechTaConfig.from_pretrained(__lowerCamelCase		)
  else:
    _snake_case					     =       SpeechTaConfig()

  if task == "s2t":
    _snake_case					     =       config.max_text_positions
    _snake_case					     =       SpeechTaForSpeechToText(__lowerCamelCase		)
  elif task == "t2s":
    _snake_case					     =       18_76
    _snake_case					     =       6_00
    _snake_case					     =       config.max_speech_positions
    _snake_case					     =       SpeechTaForTextToSpeech(__lowerCamelCase		)
  elif task == "s2s":
    _snake_case					     =       18_76
    _snake_case					     =       config.max_speech_positions
    _snake_case					     =       SpeechTaForSpeechToSpeech(__lowerCamelCase		)
  else:
    raise ValueError(f'''Unknown task name: {task}'''		)

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

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

  _snake_case					     =       SpeechTaFeatureExtractor()
  _snake_case					     =       SpeechTaProcessor(tokenizer=__lowerCamelCase ,					feature_extractor=__lowerCamelCase		)
  processor.save_pretrained(__lowerCamelCase		)

  _snake_case					     =       torch.load(__lowerCamelCase		)
  recursively_load_weights(fairseq_checkpoint['''model'''] ,					__lowerCamelCase ,					__lowerCamelCase		)

  model.save_pretrained(__lowerCamelCase		)

  if repo_id:
    print('''Pushing to the hub...'''		)
    processor.push_to_hub(__lowerCamelCase		)
    model.push_to_hub(__lowerCamelCase		)


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,
 )



