


import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_torch_available():
       from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
       __UpperCamelCase							:							Optional[int]    							=					False

if is_vision_available():
       from PIL import Image

       from transformers import (
           AutoProcessor,
           PixaStructImageProcessor,
           PixaStructProcessor,
           PreTrainedTokenizerFast,
           TaTokenizer,
       )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,					reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."       ,					)
@require_vision
@require_torch
class    __magic_name__						(  unittest.TestCase):


     def   UpperCAmelCase__						(   self				:  Tuple      )	->   Tuple:





      '''simple docstring'''






      UpperCamelCase__       :     List[Any] 							= tempfile.mkdtemp()

      UpperCamelCase__       :     Tuple 							= PixaStructImageProcessor()
      UpperCamelCase__       :     Union[str, Any] 							= TaTokenizer.from_pretrained('''t5-small'''      )

      UpperCamelCase__       :     Dict 							= PixaStructProcessor(lowerCAmelCase_				,						lowerCAmelCase_      )

      processor.save_pretrained(self.tmpdirname      )


     def   UpperCAmelCase__						(   self				:  List[str]				,						**lowerCamelCase__				:  int      )	->   Optional[Any]:





      '''simple docstring'''






      return AutoProcessor.from_pretrained(self.tmpdirname				,						**lowerCAmelCase_      ).tokenizer


     def   UpperCAmelCase__						(   self				:  Union[str, Any]				,						**lowerCamelCase__				:  List[str]      )	->   List[Any]:





      '''simple docstring'''






      return AutoProcessor.from_pretrained(self.tmpdirname				,						**lowerCAmelCase_      ).image_processor


     def   UpperCAmelCase__						(   self				:  int      )	->   List[str]:





      '''simple docstring'''






      shutil.rmtree(self.tmpdirname      )


     def   UpperCAmelCase__						(   self				:  Any      )	->   Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[int] 							= [np.random.randint(255				,						size=(3, 30, 400)				,						dtype=np.uinta      )]

      UpperCamelCase__       :     Union[str, Any] 							= [Image.fromarray(np.moveaxis(lowerCAmelCase_				,						0				,						-1      )      ) for x in image_inputs]

      return image_inputs


     def   UpperCAmelCase__						(   self				:  Union[str, Any]      )	->   Dict:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= PixaStructProcessor(tokenizer=self.get_tokenizer()				,						image_processor=self.get_image_processor()      )
      processor.save_pretrained(self.tmpdirname      )

      UpperCamelCase__       :     Dict 							= self.get_tokenizer(bos_token='''(BOS)'''				,						eos_token='''(EOS)'''      )
      UpperCamelCase__       :     List[Any] 							= self.get_image_processor(do_normalize=lowerCAmelCase_				,						padding_value=1.0      )

      UpperCamelCase__       :     List[str] 							= PixaStructProcessor.from_pretrained(
          self.tmpdirname				,						bos_token='''(BOS)'''				,						eos_token='''(EOS)'''				,						do_normalize=lowerCAmelCase_				,						padding_value=1.0      )

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer_add_kwargs.get_vocab()      )
      self.assertIsInstance(processor.tokenizer				,						lowerCAmelCase_      )

      self.assertEqual(processor.image_processor.to_json_string()				,						image_processor_add_kwargs.to_json_string()      )
      self.assertIsInstance(processor.image_processor				,						lowerCAmelCase_      )


     def   UpperCAmelCase__						(   self				:  Dict      )	->   Optional[int]:





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= self.get_image_processor()
      UpperCamelCase__       :     List[str] 							= self.get_tokenizer()

      UpperCamelCase__       :     Tuple 							= PixaStructProcessor(tokenizer=lowerCAmelCase_				,						image_processor=lowerCAmelCase_      )

      UpperCamelCase__       :     Optional[int] 							= self.prepare_image_inputs()

      UpperCamelCase__       :     Any 							= image_processor(lowerCAmelCase_				,						return_tensors='''np'''      )
      UpperCamelCase__       :     Dict 							= processor(images=lowerCAmelCase_				,						return_tensors='''np'''      )

      for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1E-2      )


     def   UpperCAmelCase__						(   self				:  Tuple      )	->   List[str]:





      '''simple docstring'''






      UpperCamelCase__       :     Any 							= self.get_image_processor()
      UpperCamelCase__       :     str 							= self.get_tokenizer()

      UpperCamelCase__       :     Dict 							= PixaStructProcessor(tokenizer=lowerCAmelCase_				,						image_processor=lowerCAmelCase_      )

      UpperCamelCase__       :     int 							= '''lower newer'''

      UpperCamelCase__       :     Union[str, Any] 							= processor(text=lowerCAmelCase_      )

      UpperCamelCase__       :     Optional[Any] 							= tokenizer(lowerCAmelCase_				,						return_token_type_ids=lowerCAmelCase_				,						add_special_tokens=lowerCAmelCase_      )

      for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]				,						encoded_processor[key]      )


     def   UpperCAmelCase__						(   self				:  str      )	->   List[str]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= self.get_image_processor()
      UpperCamelCase__       :     List[Any] 							= self.get_tokenizer()

      UpperCamelCase__       :     Union[str, Any] 							= PixaStructProcessor(tokenizer=lowerCAmelCase_				,						image_processor=lowerCAmelCase_      )

      UpperCamelCase__       :     List[Any] 							= '''lower newer'''
      UpperCamelCase__       :     int 							= self.prepare_image_inputs()

      UpperCamelCase__       :     Optional[Any] 							= processor(text=lowerCAmelCase_				,						images=lowerCAmelCase_      )

      self.assertListEqual(
          list(inputs.keys()      )				,						['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']      )

      # test if it raises when no input is passed
      with pytest.raises(lowerCAmelCase_      ):
       processor()


     def   UpperCAmelCase__						(   self				:  Optional[Any]      )	->   Any:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= self.get_image_processor()
      UpperCamelCase__       :     Tuple 							= self.get_tokenizer()

      UpperCamelCase__       :     Any 							= PixaStructProcessor(tokenizer=lowerCAmelCase_				,						image_processor=lowerCAmelCase_      )

      UpperCamelCase__       :     Union[str, Any] 							= '''lower newer'''
      UpperCamelCase__       :     Any 							= self.prepare_image_inputs()

      UpperCamelCase__       :     Optional[int] 							= processor(text=lowerCAmelCase_				,						images=lowerCAmelCase_      )

      UpperCamelCase__       :     Union[str, Any] 							= [512, 1024, 2048, 4096]
      UpperCamelCase__       :     Tuple 							= [770, 770, 770, 770]
      # with text
      for i, max_patch in enumerate(lowerCAmelCase_      ):
       UpperCamelCase__       :     List[str] 							= processor(text=lowerCAmelCase_				,						images=lowerCAmelCase_				,						max_patches=lowerCAmelCase_      )
       self.assertEqual(inputs['''flattened_patches'''][0].shape[0]				,						lowerCAmelCase_      )
       self.assertEqual(inputs['''flattened_patches'''][0].shape[1]				,						expected_hidden_size[i]      )

      # without text input
      for i, max_patch in enumerate(lowerCAmelCase_      ):
       UpperCamelCase__       :     Optional[int] 							= processor(images=lowerCAmelCase_				,						max_patches=lowerCAmelCase_      )
       self.assertEqual(inputs['''flattened_patches'''][0].shape[0]				,						lowerCAmelCase_      )
       self.assertEqual(inputs['''flattened_patches'''][0].shape[1]				,						expected_hidden_size[i]      )


     def   UpperCAmelCase__						(   self				:  int      )	->   Any:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= self.get_image_processor()
      UpperCamelCase__       :     List[Any] 							= self.get_tokenizer()

      UpperCamelCase__       :     Optional[int] 							= PixaStructProcessor(tokenizer=lowerCAmelCase_				,						image_processor=lowerCAmelCase_      )

      UpperCamelCase__       :     List[str] 							= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      UpperCamelCase__       :     List[Any] 							= processor.batch_decode(lowerCAmelCase_      )
      UpperCamelCase__       :     Optional[int] 							= tokenizer.batch_decode(lowerCAmelCase_      )

      self.assertListEqual(lowerCAmelCase_				,						lowerCAmelCase_      )


     def   UpperCAmelCase__						(   self				:  Optional[int]      )	->   str:





      '''simple docstring'''






      UpperCamelCase__       :     str 							= self.get_image_processor()
      UpperCamelCase__       :     Optional[int] 							= self.get_tokenizer()

      UpperCamelCase__       :     List[str] 							= PixaStructProcessor(tokenizer=lowerCAmelCase_				,						image_processor=lowerCAmelCase_      )

      UpperCamelCase__       :     List[Any] 							= '''lower newer'''
      UpperCamelCase__       :     List[str] 							= self.prepare_image_inputs()

      UpperCamelCase__       :     List[str] 							= processor(text=lowerCAmelCase_				,						images=lowerCAmelCase_      )

      # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
      self.assertListEqual(
          list(inputs.keys()      )				,						['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']      )

      UpperCamelCase__       :     List[Any] 							= processor(text=lowerCAmelCase_      )

      # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
      self.assertListEqual(list(inputs.keys()      )				,						['''input_ids''', '''attention_mask''']      )




'''simple docstring'''





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				WavLMForSequenceClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["projector.weight"]
      snake_case_    							=				downstream_dict["projector.bias"]
      snake_case_    							=				downstream_dict["model.post_net.linear.weight"]
      snake_case_    							=				downstream_dict["model.post_net.linear.bias"]
      return model
def 	snake_case_							(				_a  :				List[str]			,       _a  :				Optional[Any]			,       _a  :				List[str]      ):
      snake_case_    							=				WavLMForAudioFrameClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["model.linear.weight"]
      snake_case_    							=				downstream_dict["model.linear.bias"]
      return model
def 	snake_case_							(				_a  :				Dict			,       _a  :				Tuple			,       _a  :				Any      ):
      snake_case_    							=				WavLMForXVector.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["connector.weight"]
      snake_case_    							=				downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
            snake_case_    							=				downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            snake_case_    							=				downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      snake_case_    							=				downstream_dict["objective.W"]
      return model
@torch.no_grad()
def 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				torch.load(_a			,       map_location="cpu"      )

      snake_case_    							=				checkpoint["Downstream"]

      snake_case_    							=				WavLMConfig.from_pretrained(_a      )
      snake_case_    							=				WavaVecaFeatureExtractor.from_pretrained(
          _a			,       return_attention_mask=_a			,       do_normalize=_a      )

      snake_case_    							=				hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"      ):
            snake_case_    							=				convert_classification(_a			,       _a			,       _a      )
      elif arch.endswith("ForAudioFrameClassification"      ):
            snake_case_    							=				convert_diarization(_a			,       _a			,       _a      )
      elif arch.endswith("ForXVector"      ):
            snake_case_    							=				convert_xvector(_a			,       _a			,       _a      )
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''      )

      if hf_config.use_weighted_layer_sum:
            snake_case_    							=				checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(_a      )
      hf_model.save_pretrained(_a      )


if __name__ == "__main__":
     A							     = argparse.ArgumentParser()
     parser.add_argument(
         "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
     )
     parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
     parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
     parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
     A							     = parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

