

"""simple docstring"""




import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

      if is_vision_available():
            from transformers import OneFormerImageProcessor
            from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
            from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
      from PIL import Image
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase="shi-labs/oneformer_demo"   ):
 '''simple docstring'''





 with open(hf_hub_download(__UpperCamelCase				,     __UpperCamelCase				,     repo_type="""dataset"""   )				,     """r"""   ) as f:
  UpperCAmelCase__       :	Union[str, Any]        =       json.load(__UpperCamelCase   )
 UpperCAmelCase__       :	Tuple        =       {}
 UpperCAmelCase__       :	List[Any]        =       []
 UpperCAmelCase__       :	str        =       []
 for key, info in class_info.items():
  UpperCAmelCase__       :	Optional[int]        =       info["""name"""]
  class_names.append(info["""name"""]   )
  if info["isthing"]:
   thing_ids.append(int(__UpperCamelCase   )   )
 UpperCAmelCase__       :	Any        =       thing_ids
 UpperCAmelCase__       :	str        =       class_names
 return metadata







class   __lowercase      (     unittest.TestCase       ):

  def __init__(	self		:      List[Any]       ,A		:      Optional[int]       ,A		:      Optional[Any]=7       ,A		:      Any=3       ,A		:      Tuple=30       ,A		:      int=400       ,A		:      Tuple=None       ,A		:      Tuple=True       ,A		:      Tuple=True       ,A		:      Optional[int]=[0.5, 0.5, 0.5]       ,A		:      List[Any]=[0.5, 0.5, 0.5]       ,A		:      Dict=10       ,A		:      int=False       ,A		:      Optional[int]=255       ,A		:      Any="shi-labs/oneformer_demo"       ,A		:      Tuple="ade20k_panoptic.json"       ,A		:      Dict=10       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       parent
   UpperCAmelCase__       :	List[str]        =       batch_size
   UpperCAmelCase__       :	Any        =       num_channels
   UpperCAmelCase__       :	List[str]        =       min_resolution
   UpperCAmelCase__       :	List[str]        =       max_resolution
   UpperCAmelCase__       :	Optional[Any]        =       do_resize
   UpperCAmelCase__       :	int        =       {"""shortest_edge""": 32, """longest_edge""": 1_333} if size is None else size
   UpperCAmelCase__       :	Dict        =       do_normalize
   UpperCAmelCase__       :	int        =       image_mean
   UpperCAmelCase__       :	Tuple        =       image_std
   UpperCAmelCase__       :	Optional[Any]        =       class_info_file
   UpperCAmelCase__       :	List[str]        =       prepare_metadata(A       ,A     )
   UpperCAmelCase__       :	Tuple        =       num_text
   UpperCAmelCase__       :	Dict        =       repo_path

   # for the post_process_functions
   UpperCAmelCase__       :	List[str]        =       2
   UpperCAmelCase__       :	Any        =       10
   UpperCAmelCase__       :	List[Any]        =       10
   UpperCAmelCase__       :	Optional[int]        =       3
   UpperCAmelCase__       :	Optional[int]        =       4
   UpperCAmelCase__       :	str        =       num_labels
   UpperCAmelCase__       :	List[str]        =       do_reduce_labels
   UpperCAmelCase__       :	int        =       ignore_index

  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "num_labels": self.num_labels,
       "do_reduce_labels": self.do_reduce_labels,
       "ignore_index": self.ignore_index,
       "class_info_file": self.class_info_file,
       "metadata": self.metadata,
       "num_text": self.num_text,
   }

  def  __lowercase							(	self		:      Dict       ,A		:      List[str]       ,A		:      Dict=False     ):






   '''simple docstring'''
   if not batched:
    UpperCAmelCase__       :	Tuple        =       image_inputs[0]
    if isinstance(A       ,Image.Image     ):
     UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[int]        =       image.size
    else:
     UpperCAmelCase__ ,   UpperCAmelCase__       :	Union[str, Any]        =       image.shape[1], image.shape[2]
    if w < h:
     UpperCAmelCase__       :	Any        =       int(self.size["""shortest_edge"""] * h / w     )
     UpperCAmelCase__       :	int        =       self.size["""shortest_edge"""]
    elif w > h:
     UpperCAmelCase__       :	Any        =       self.size["""shortest_edge"""]
     UpperCAmelCase__       :	Any        =       int(self.size["""shortest_edge"""] * w / h     )
    else:
     UpperCAmelCase__       :	Optional[Any]        =       self.size["""shortest_edge"""]
     UpperCAmelCase__       :	str        =       self.size["""shortest_edge"""]

   else:
    UpperCAmelCase__       :	int        =       []
    for image in image_inputs:
     UpperCAmelCase__ ,   UpperCAmelCase__       :	Any        =       self.get_expected_values([image]     )
     expected_values.append((expected_height, expected_width)     )
    UpperCAmelCase__       :	str        =       max(A       ,key=lambda A     : item[0]     )[0]
    UpperCAmelCase__       :	List[Any]        =       max(A       ,key=lambda A     : item[1]     )[1]

   return expected_height, expected_width




  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   return OneFormerForUniversalSegmentationOutput(
       # +1 for null class
       class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)     )       ,masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)     )       ,)







@require_torch
@require_vision
class   __lowercase      (     __lowerCamelCase						,      unittest.TestCase       ):
  snake_case_												=							OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  snake_case_												=							image_processing_class

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       OneFormerImageProcessorTester(self     )

  @property
  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   return self.image_processing_tester.prepare_image_processor_dict()

  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       self.image_processing_class(**self.image_processor_dict     )
   self.assertTrue(hasattr(A       ,"""image_mean"""     )     )
   self.assertTrue(hasattr(A       ,"""image_std"""     )     )
   self.assertTrue(hasattr(A       ,"""do_normalize"""     )     )
   self.assertTrue(hasattr(A       ,"""do_resize"""     )     )
   self.assertTrue(hasattr(A       ,"""size"""     )     )
   self.assertTrue(hasattr(A       ,"""ignore_index"""     )     )
   self.assertTrue(hasattr(A       ,"""class_info_file"""     )     )
   self.assertTrue(hasattr(A       ,"""num_text"""     )     )
   self.assertTrue(hasattr(A       ,"""repo_path"""     )     )
   self.assertTrue(hasattr(A       ,"""metadata"""     )     )
   self.assertTrue(hasattr(A       ,"""do_reduce_labels"""     )     )

  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   pass

  def  __lowercase							(	self		:      List[str]     ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__       :	Tuple        =       self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   UpperCAmelCase__       :	List[str]        =       prepare_image_inputs(self.image_processing_tester       ,equal_resolution=A     )
   for image in image_inputs:
    self.assertIsInstance(A       ,Image.Image     )

   # Test not batched input
   UpperCAmelCase__       :	Tuple        =       image_processor(image_inputs[0]       ,["""semantic"""]       ,return_tensors="""pt"""     ).pixel_values

   UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[int]        =       self.image_processing_tester.get_expected_values(A     )

   self.assertEqual(
       encoded_images.shape       ,(1, self.image_processing_tester.num_channels, expected_height, expected_width)       ,)

   # Test batched
   UpperCAmelCase__ ,   UpperCAmelCase__       :	str        =       self.image_processing_tester.get_expected_values(A       ,batched=A     )

   UpperCAmelCase__       :	Tuple        =       image_processor(
       A       ,["""semantic"""] * len(A     )       ,return_tensors="""pt"""     ).pixel_values
   self.assertEqual(
       encoded_images.shape       ,(
           self.image_processing_tester.batch_size,
           self.image_processing_tester.num_channels,
           expected_height,
           expected_width,
       )       ,)

  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__       :	str        =       self.image_processing_class(**self.image_processor_dict     )
   # create random numpy tensors
   UpperCAmelCase__       :	Union[str, Any]        =       prepare_image_inputs(self.image_processing_tester       ,equal_resolution=A       ,numpify=A     )
   for image in image_inputs:
    self.assertIsInstance(A       ,np.ndarray     )

   # Test not batched input
   UpperCAmelCase__       :	List[str]        =       image_processor(image_inputs[0]       ,["""semantic"""]       ,return_tensors="""pt"""     ).pixel_values

   UpperCAmelCase__ ,   UpperCAmelCase__       :	Tuple        =       self.image_processing_tester.get_expected_values(A     )

   self.assertEqual(
       encoded_images.shape       ,(1, self.image_processing_tester.num_channels, expected_height, expected_width)       ,)

   # Test batched
   UpperCAmelCase__ ,   UpperCAmelCase__       :	Union[str, Any]        =       self.image_processing_tester.get_expected_values(A       ,batched=A     )

   UpperCAmelCase__       :	Tuple        =       image_processor(
       A       ,["""semantic"""] * len(A     )       ,return_tensors="""pt"""     ).pixel_values
   self.assertEqual(
       encoded_images.shape       ,(
           self.image_processing_tester.batch_size,
           self.image_processing_tester.num_channels,
           expected_height,
           expected_width,
       )       ,)

  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__       :	Union[str, Any]        =       self.image_processing_class(**self.image_processor_dict     )
   # create random PyTorch tensors
   UpperCAmelCase__       :	List[Any]        =       prepare_image_inputs(self.image_processing_tester       ,equal_resolution=A       ,torchify=A     )
   for image in image_inputs:
    self.assertIsInstance(A       ,torch.Tensor     )

   # Test not batched input
   UpperCAmelCase__       :	str        =       image_processor(image_inputs[0]       ,["""semantic"""]       ,return_tensors="""pt"""     ).pixel_values

   UpperCAmelCase__ ,   UpperCAmelCase__       :	Union[str, Any]        =       self.image_processing_tester.get_expected_values(A     )

   self.assertEqual(
       encoded_images.shape       ,(1, self.image_processing_tester.num_channels, expected_height, expected_width)       ,)

   # Test batched
   UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[Any]        =       self.image_processing_tester.get_expected_values(A       ,batched=A     )

   UpperCAmelCase__       :	Tuple        =       image_processor(
       A       ,["""semantic"""] * len(A     )       ,return_tensors="""pt"""     ).pixel_values
   self.assertEqual(
       encoded_images.shape       ,(
           self.image_processing_tester.batch_size,
           self.image_processing_tester.num_channels,
           expected_height,
           expected_width,
       )       ,)

  def  __lowercase							(	self		:      List[str]       ,A		:      Optional[Any]=False       ,A		:      List[Any]=False       ,A		:      str="np"     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       self.image_processing_class(**self.image_processor_dict     )
   # prepare image and target
   UpperCAmelCase__       :	Any        =       self.image_processing_tester.num_labels
   UpperCAmelCase__       :	Optional[int]        =       None
   UpperCAmelCase__       :	int        =       None
   UpperCAmelCase__       :	Union[str, Any]        =       prepare_image_inputs(self.image_processing_tester       ,equal_resolution=A     )
   if with_segmentation_maps:
    UpperCAmelCase__       :	Optional[int]        =       num_labels
    if is_instance_map:
     UpperCAmelCase__       :	int        =       list(range(A     )     ) * 2
     UpperCAmelCase__       :	Dict        =       dict(enumerate(A     )     )
    UpperCAmelCase__       :	Union[str, Any]        =       [
        np.random.randint(0       ,high * 2       ,(img.size[1], img.size[0])     ).astype(np.uinta     ) for img in image_inputs
    ]
    if segmentation_type == "pil":
     UpperCAmelCase__       :	Optional[Any]        =       [Image.fromarray(A     ) for annotation in annotations]

   UpperCAmelCase__       :	Optional[Any]        =       image_processor(
       A       ,["""semantic"""] * len(A     )       ,A       ,return_tensors="""pt"""       ,instance_id_to_semantic_id=A       ,pad_and_return_pixel_mask=A       ,)

   return inputs

  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   pass

  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   def common(A		:      str=False       ,A		:      Tuple=None     ):
    UpperCAmelCase__       :	Tuple        =       self.comm_get_image_processor_inputs(
        with_segmentation_maps=A       ,is_instance_map=A       ,segmentation_type=A     )

    UpperCAmelCase__       :	int        =       inputs["""mask_labels"""]
    UpperCAmelCase__       :	Optional[int]        =       inputs["""class_labels"""]
    UpperCAmelCase__       :	str        =       inputs["""pixel_values"""]
    UpperCAmelCase__       :	int        =       inputs["""text_inputs"""]

    # check the batch_size
    for mask_label, class_label, text_input in zip(A       ,A       ,A     ):
     self.assertEqual(mask_label.shape[0]       ,class_label.shape[0]     )
     # this ensure padding has happened
     self.assertEqual(mask_label.shape[1:]       ,pixel_values.shape[2:]     )
     self.assertEqual(len(A     )       ,self.image_processing_tester.num_text     )

   common()
   common(is_instance_map=A     )
   common(is_instance_map=A       ,segmentation_type="""pil"""     )
   common(is_instance_map=A       ,segmentation_type="""pil"""     )

  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       np.zeros((20, 50)     )
   UpperCAmelCase__       :	List[Any]        =       1
   UpperCAmelCase__       :	List[Any]        =       1
   UpperCAmelCase__       :	Optional[int]        =       1

   UpperCAmelCase__       :	List[Any]        =       binary_mask_to_rle(A     )
   self.assertEqual(len(A     )       ,4     )
   self.assertEqual(rle[0]       ,21     )
   self.assertEqual(rle[1]       ,45     )

  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       self.image_processing_class(
       num_labels=self.image_processing_tester.num_classes       ,max_seq_length=77       ,task_seq_length=77       ,class_info_file="""ade20k_panoptic.json"""       ,num_text=self.image_processing_tester.num_text       ,repo_path="""shi-labs/oneformer_demo"""       ,)
   UpperCAmelCase__       :	List[Any]        =       self.image_processing_tester.get_fake_oneformer_outputs()

   UpperCAmelCase__       :	str        =       fature_extractor.post_process_semantic_segmentation(A     )

   self.assertEqual(len(A     )       ,self.image_processing_tester.batch_size     )
   self.assertEqual(
       segmentation[0].shape       ,(
           self.image_processing_tester.height,
           self.image_processing_tester.width,
       )       ,)

   UpperCAmelCase__       :	Tuple        =       [(1, 4) for i in range(self.image_processing_tester.batch_size     )]
   UpperCAmelCase__       :	Any        =       fature_extractor.post_process_semantic_segmentation(A       ,target_sizes=A     )

   self.assertEqual(segmentation[0].shape       ,target_sizes[0]     )

  def  __lowercase							(	self		:      List[str]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       self.image_processing_class(
       num_labels=self.image_processing_tester.num_classes       ,max_seq_length=77       ,task_seq_length=77       ,class_info_file="""ade20k_panoptic.json"""       ,num_text=self.image_processing_tester.num_text       ,repo_path="""shi-labs/oneformer_demo"""       ,)
   UpperCAmelCase__       :	Optional[int]        =       self.image_processing_tester.get_fake_oneformer_outputs()
   UpperCAmelCase__       :	Any        =       image_processor.post_process_instance_segmentation(A       ,threshold=0     )

   self.assertTrue(len(A     ) == self.image_processing_tester.batch_size     )
   for el in segmentation:
    self.assertTrue("""segmentation""" in el     )
    self.assertTrue("""segments_info""" in el     )
    self.assertEqual(type(el["""segments_info"""]     )       ,A     )
    self.assertEqual(
        el["""segmentation"""].shape       ,(self.image_processing_tester.height, self.image_processing_tester.width)     )




  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       self.image_processing_class(
       num_labels=self.image_processing_tester.num_classes       ,max_seq_length=77       ,task_seq_length=77       ,class_info_file="""ade20k_panoptic.json"""       ,num_text=self.image_processing_tester.num_text       ,repo_path="""shi-labs/oneformer_demo"""       ,)
   UpperCAmelCase__       :	List[Any]        =       self.image_processing_tester.get_fake_oneformer_outputs()
   UpperCAmelCase__       :	str        =       image_processor.post_process_panoptic_segmentation(A       ,threshold=0     )

   self.assertTrue(len(A     ) == self.image_processing_tester.batch_size     )
   for el in segmentation:
    self.assertTrue("""segmentation""" in el     )
    self.assertTrue("""segments_info""" in el     )
    self.assertEqual(type(el["""segments_info"""]     )       ,A     )
    self.assertEqual(
        el["""segmentation"""].shape       ,(self.image_processing_tester.height, self.image_processing_tester.width)     )








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()
_lowerCAmelCase  :      List[str]			 =logging.get_logger("""transformers.models.speecht5""")

_lowerCAmelCase  :      List[Any]			 ={
    """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""",
}
_lowerCAmelCase  :      List[str]			 ={
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
_lowerCAmelCase  :      Optional[Any]			 ={
    """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""",
}
_lowerCAmelCase  :      int			 ={
    """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""",
}
_lowerCAmelCase  :      List[Any]			 ={
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
_lowerCAmelCase  :      Any			 ={
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
_lowerCAmelCase  :      Dict			 ={
    """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""",
}
_lowerCAmelCase  :      List[str]			 ={
    """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""",
}
_lowerCAmelCase  :      List[Any]			 ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_lowerCAmelCase  :      int			 ={
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCAmelCase  :      Dict			 ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCAmelCase  :      Optional[Any]			 =[]
_lowerCAmelCase  :      int			 =[
    """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""",
]
_lowerCAmelCase  :      Any			 =IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
_lowerCAmelCase  :      int			 =IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
_lowerCAmelCase  :      Union[str, Any]			 =IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	for attribute in key.split("."      ):
		UpperCAmelCase__:	List[Any]									= getattr(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

	if weight_type is not None:
		UpperCAmelCase__:	Optional[int]									= getattr(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ).shape
	else:
		UpperCAmelCase__:	int									= 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__:	Union[str, Any]									= value
	elif weight_type == "weight_g":
		UpperCAmelCase__:	str									= value
	elif weight_type == "weight_v":
		UpperCAmelCase__:	Optional[int]									= value
	elif weight_type == "bias":
		UpperCAmelCase__:	Union[str, Any]									= value
	elif weight_type == "running_mean":
		UpperCAmelCase__:	Optional[Any]									= value
	elif weight_type == "running_var":
		UpperCAmelCase__:	Union[str, Any]									= value
	elif weight_type == "num_batches_tracked":
		UpperCAmelCase__:	Optional[int]									= value
	else:
		UpperCAmelCase__:	Union[str, Any]									= value

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



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	for key in ignore_keys:
		if key.endswith(".*"      ):
			if name.startswith(key[:-1]      ):
				return True
		elif ".*." in key:
			UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= key.split(".*."      )
			if prefix in name and suffix in name:
				return True
		elif key in name:
			return True
	return False



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[Any]									= []

	if task == "s2t":
		UpperCAmelCase__:	List[Any]									= hf_model.speechta.encoder.prenet.feature_encoder
		UpperCAmelCase__:	Tuple									= MAPPING_S2T
		UpperCAmelCase__:	int									= IGNORE_KEYS_S2T
	elif task == "t2s":
		UpperCAmelCase__:	Dict									= None
		UpperCAmelCase__:	Any									= MAPPING_T2S
		UpperCAmelCase__:	List[Any]									= IGNORE_KEYS_T2S
	elif task == "s2s":
		UpperCAmelCase__:	List[Any]									= hf_model.speechta.encoder.prenet.feature_encoder
		UpperCAmelCase__:	Union[str, Any]									= MAPPING_S2S
		UpperCAmelCase__:	Tuple									= IGNORE_KEYS_S2S
	else:
		raise ValueError(f"Unsupported task: {task}"      )

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

		UpperCAmelCase__:	Any									= False
		if "conv_layers" in name:
			load_conv_layer(
			    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,hf_model.config.feat_extract_norm == "group"	,)
			UpperCAmelCase__:	Optional[Any]									= 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__:	List[Any]									= key.split(".*."      )
					if prefix in name and suffix in name:
						UpperCAmelCase__:	str									= suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
				if key in name:
					UpperCAmelCase__:	Optional[int]									= True
					if "*" in mapped_key:
						UpperCAmelCase__:	Any									= name.split(SCREAMING_SNAKE_CASE      )[0].split("."      )[-2]
						UpperCAmelCase__:	Dict									= mapped_key.replace("*"	,SCREAMING_SNAKE_CASE      )
					if "weight_g" in name:
						UpperCAmelCase__:	List[str]									= "weight_g"
					elif "weight_v" in name:
						UpperCAmelCase__:	Union[str, Any]									= "weight_v"
					elif "bias" in name:
						UpperCAmelCase__:	str									= "bias"
					elif "weight" in name:
						UpperCAmelCase__:	List[Any]									= "weight"
					elif "running_mean" in name:
						UpperCAmelCase__:	List[Any]									= "running_mean"
					elif "running_var" in name:
						UpperCAmelCase__:	Optional[Any]									= "running_var"
					elif "num_batches_tracked" in name:
						UpperCAmelCase__:	Optional[int]									= "num_batches_tracked"
					else:
						UpperCAmelCase__:	Dict									= None
					set_recursively(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
				continue
		if not is_used:
			unused_weights.append(SCREAMING_SNAKE_CASE      )

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



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



@torch.no_grad()
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=None	,SCREAMING_SNAKE_CASE=None	,SCREAMING_SNAKE_CASE=None	,):
	if config_path is not None:
		UpperCAmelCase__:	Optional[int]									= SpeechTaConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
	else:
		UpperCAmelCase__:	Tuple									= SpeechTaConfig()

	if task == "s2t":
		UpperCAmelCase__:	List[Any]									= config.max_text_positions
		UpperCAmelCase__:	List[str]									= SpeechTaForSpeechToText(SCREAMING_SNAKE_CASE      )
	elif task == "t2s":
		UpperCAmelCase__:	Dict									= 1_8_7_6
		UpperCAmelCase__:	Dict									= 6_0_0
		UpperCAmelCase__:	str									= config.max_speech_positions
		UpperCAmelCase__:	int									= SpeechTaForTextToSpeech(SCREAMING_SNAKE_CASE      )
	elif task == "s2s":
		UpperCAmelCase__:	Any									= 1_8_7_6
		UpperCAmelCase__:	List[str]									= config.max_speech_positions
		UpperCAmelCase__:	Union[str, Any]									= SpeechTaForSpeechToSpeech(SCREAMING_SNAKE_CASE      )
	else:
		raise ValueError(f"Unknown task name: {task}"      )

	if vocab_path:
		UpperCAmelCase__:	int									= SpeechTaTokenizer(SCREAMING_SNAKE_CASE	,model_max_length=config.max_text_positions      )

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

	UpperCAmelCase__:	int									= SpeechTaFeatureExtractor()
	UpperCAmelCase__:	Optional[Any]									= SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE	,feature_extractor=SCREAMING_SNAKE_CASE      )
	processor.save_pretrained(SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	int									= torch.load(SCREAMING_SNAKE_CASE      )
	recursively_load_weights(fairseq_checkpoint["model"]	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

	model.save_pretrained(SCREAMING_SNAKE_CASE      )

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


if __name__ == "__main__":
					_lowerCAmelCase  :      List[str]			 =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."""
					)

					_lowerCAmelCase  :      Optional[int]			 =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,
					)