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:
   __lowerCamelCase     :					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 __snake_case							(		unittest.TestCase						):




   def       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= tempfile.mkdtemp()

     SCREAMING_SNAKE_CASE__     				= PixaStructImageProcessor()
     SCREAMING_SNAKE_CASE__     				= TaTokenizer.from_pretrained("""t5-small""" )

     SCREAMING_SNAKE_CASE__     				= PixaStructProcessor(_lowerCAmelCase			,      _lowerCAmelCase )

     processor.save_pretrained(self.tmpdirname )




   def       __a		(       self     :							List[str]			,      **_lowercase     :							List[str] ):



     """simple docstring"""






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




   def       __a		(       self     :							List[Any]			,      **_lowercase     :							Tuple ):



     """simple docstring"""






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




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     shutil.rmtree(self.tmpdirname )




   def       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= [np.random.randint(2_55			,      size=(3, 30, 4_00)			,      dtype=np.uinta )]

     SCREAMING_SNAKE_CASE__     				= [Image.fromarray(np.moveaxis(_lowerCAmelCase			,      0			,      -1 ) ) for x in image_inputs]

     return image_inputs




   def       __a		(       self     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= PixaStructProcessor(tokenizer=self.get_tokenizer()			,      image_processor=self.get_image_processor() )
     processor.save_pretrained(self.tmpdirname )

     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer(bos_token="""(BOS)"""			,      eos_token="""(EOS)""" )
     SCREAMING_SNAKE_CASE__     				= self.get_image_processor(do_normalize=_lowerCAmelCase			,      padding_value=1.0 )

     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_image_processor()
     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer()

     SCREAMING_SNAKE_CASE__     				= PixaStructProcessor(tokenizer=_lowerCAmelCase			,      image_processor=_lowerCAmelCase )

     SCREAMING_SNAKE_CASE__     				= self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE__     				= image_processor(_lowerCAmelCase			,      return_tensors="""np""" )
     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_image_processor()
     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer()

     SCREAMING_SNAKE_CASE__     				= PixaStructProcessor(tokenizer=_lowerCAmelCase			,      image_processor=_lowerCAmelCase )

     SCREAMING_SNAKE_CASE__     				= """lower newer"""

     SCREAMING_SNAKE_CASE__     				= processor(text=_lowerCAmelCase )

     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_image_processor()
     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer()

     SCREAMING_SNAKE_CASE__     				= PixaStructProcessor(tokenizer=_lowerCAmelCase			,      image_processor=_lowerCAmelCase )

     SCREAMING_SNAKE_CASE__     				= """lower newer"""
     SCREAMING_SNAKE_CASE__     				= self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_image_processor()
     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer()

     SCREAMING_SNAKE_CASE__     				= PixaStructProcessor(tokenizer=_lowerCAmelCase			,      image_processor=_lowerCAmelCase )

     SCREAMING_SNAKE_CASE__     				= """lower newer"""
     SCREAMING_SNAKE_CASE__     				= self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE__     				= processor(text=_lowerCAmelCase			,      images=_lowerCAmelCase )

     SCREAMING_SNAKE_CASE__     				= [5_12, 10_24, 20_48, 40_96]
     SCREAMING_SNAKE_CASE__     				= [7_70, 7_70, 7_70, 7_70]
     # with text
     for i, max_patch in enumerate(_lowerCAmelCase ):
       SCREAMING_SNAKE_CASE__     				= 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 ):
       SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_image_processor()
     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer()

     SCREAMING_SNAKE_CASE__     				= PixaStructProcessor(tokenizer=_lowerCAmelCase			,      image_processor=_lowerCAmelCase )

     SCREAMING_SNAKE_CASE__     				= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     SCREAMING_SNAKE_CASE__     				= processor.batch_decode(_lowerCAmelCase )
     SCREAMING_SNAKE_CASE__     				= tokenizer.batch_decode(_lowerCAmelCase )

     self.assertListEqual(_lowerCAmelCase			,      _lowerCAmelCase )




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_image_processor()
     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer()

     SCREAMING_SNAKE_CASE__     				= PixaStructProcessor(tokenizer=_lowerCAmelCase			,      image_processor=_lowerCAmelCase )

     SCREAMING_SNAKE_CASE__     				= """lower newer"""
     SCREAMING_SNAKE_CASE__     				= self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE__     				= 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"""] )

     SCREAMING_SNAKE_CASE__     				= 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 HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:						Dict					     =							logging.get_logger(__name__)

_lowerCamelCase					:						List[Any]					     =							['UtteranceLevel']




@torch.no_grad()
def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   torch.load(UpperCAmelCase							,      map_location="""cpu"""     )
	if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
		raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""     )

	A					=   checkpoint["""Downstream"""]

	A					=   HubertConfig.from_pretrained(UpperCAmelCase     )
	A					=   HubertForSequenceClassification.from_pretrained(UpperCAmelCase							,      config=UpperCAmelCase     )
	A					=   WavaVecaFeatureExtractor.from_pretrained(
	    UpperCAmelCase							,      return_attention_mask=UpperCAmelCase							,      do_normalize=UpperCAmelCase     )

	if hf_congfig.use_weighted_layer_sum:
		A					=   checkpoint["""Featurizer"""]["""weights"""]

	A					=   downstream_dict["""projector.weight"""]
	A					=   downstream_dict["""projector.bias"""]
	A					=   downstream_dict["""model.post_net.linear.weight"""]
	A					=   downstream_dict["""model.post_net.linear.bias"""]

	hf_feature_extractor.save_pretrained(UpperCAmelCase     )
	hf_model.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
				_lowerCamelCase					:						List[str]					     =							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.')
				_lowerCamelCase					:						List[Any]					     =							parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

