
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast


@require_vision
class 							lowercase   (						unittest.TestCase  ):



		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	tempfile.mkdtemp()

				UpperCamelCase            =	CLIPImageProcessor()
				UpperCamelCase            =	BertTokenizer.from_pretrained(
				    'hf-internal-testing/tiny-random-BertModel'							, model_input_names=['input_ids', 'attention_mask']			)

				UpperCamelCase            =	GitProcessor(__UpperCAmelCase							, __UpperCAmelCase			)

				processor.save_pretrained(self.tmpdirname			)



		def __UpperCamelCase					(					self							, **A_			)    ->   Any:
				"""simple docstring"""
				return AutoProcessor.from_pretrained(self.tmpdirname							, **__UpperCAmelCase			).tokenizer



		def __UpperCamelCase					(					self							, **A_			)    ->   int:
				"""simple docstring"""
				return AutoProcessor.from_pretrained(self.tmpdirname							, **__UpperCAmelCase			).image_processor



		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				shutil.rmtree(self.tmpdirname			)



		def __UpperCamelCase					(					self			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	[np.random.randint(255							, size=(3, 30, 400)							, dtype=np.uinta			)]

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

				return image_inputs



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	GitProcessor(tokenizer=self.get_tokenizer()							, image_processor=self.get_image_processor()			)
				processor.save_pretrained(self.tmpdirname			)

				UpperCamelCase            =	self.get_tokenizer(bos_token='(BOS)'							, eos_token='(EOS)'			)
				UpperCamelCase            =	self.get_image_processor(do_normalize=__UpperCAmelCase							, padding_value=1.0			)

				UpperCamelCase            =	GitProcessor.from_pretrained(
				    self.tmpdirname							, bos_token='(BOS)'							, eos_token='(EOS)'							, do_normalize=__UpperCAmelCase							, padding_value=1.0			)

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

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



		def __UpperCamelCase					(					self			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	GitProcessor(tokenizer=__UpperCAmelCase							, image_processor=__UpperCAmelCase			)

				UpperCamelCase            =	self.prepare_image_inputs()

				UpperCamelCase            =	image_processor(__UpperCAmelCase							, return_tensors='np'			)
				UpperCamelCase            =	processor(images=__UpperCAmelCase							, 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			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	GitProcessor(tokenizer=__UpperCAmelCase							, image_processor=__UpperCAmelCase			)

				UpperCamelCase            =	'''lower newer'''

				UpperCamelCase            =	processor(text=__UpperCAmelCase			)

				UpperCamelCase            =	tokenizer(__UpperCAmelCase							, return_token_type_ids=__UpperCAmelCase			)

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



		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	GitProcessor(tokenizer=__UpperCAmelCase							, image_processor=__UpperCAmelCase			)

				UpperCamelCase            =	'''lower newer'''
				UpperCamelCase            =	self.prepare_image_inputs()

				UpperCamelCase            =	processor(text=__UpperCAmelCase							, images=__UpperCAmelCase			)

				self.assertListEqual(list(inputs.keys()			)							, ['input_ids', 'attention_mask', 'pixel_values']			)

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



		def __UpperCamelCase					(					self			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	GitProcessor(tokenizer=__UpperCAmelCase							, image_processor=__UpperCAmelCase			)

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

				UpperCamelCase            =	processor.batch_decode(__UpperCAmelCase			)
				UpperCamelCase            =	tokenizer.batch_decode(__UpperCAmelCase			)

				self.assertListEqual(__UpperCAmelCase							, __UpperCAmelCase			)



		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	GitProcessor(tokenizer=__UpperCAmelCase							, image_processor=__UpperCAmelCase			)

				UpperCamelCase            =	'''lower newer'''
				UpperCamelCase            =	self.prepare_image_inputs()

				UpperCamelCase            =	processor(text=__UpperCAmelCase							, images=__UpperCAmelCase			)

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

"""simple docstring"""




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase       :    int									=			logging.get_logger(__name__)

UpperCAmelCase       :    Optional[int]									=			["UtteranceLevel"]



@torch.no_grad()
def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





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

					lowerCamelCase__		:   List[Any]  				=					checkpoint['''Downstream''']

					lowerCamelCase__		:   int  				=					HubertConfig.from_pretrained(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					HubertForSequenceClassification.from_pretrained(_lowercase							, config=_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					WavaVecaFeatureExtractor.from_pretrained(
					    _lowercase							, return_attention_mask=_lowercase							, do_normalize=_lowercase						)

					if hf_congfig.use_weighted_layer_sum:
										lowerCamelCase__		:   Dict  				=					checkpoint['''Featurizer''']['''weights''']

					lowerCamelCase__		:   Any  				=					downstream_dict['''projector.weight''']
					lowerCamelCase__		:   Optional[int]  				=					downstream_dict['''projector.bias''']
					lowerCamelCase__		:   Dict  				=					downstream_dict['''model.post_net.linear.weight''']
					lowerCamelCase__		:   str  				=					downstream_dict['''model.post_net.linear.bias''']

					hf_feature_extractor.save_pretrained(_lowercase						)
					hf_model.save_pretrained(_lowercase						)


if __name__ == "__main__":
					UpperCAmelCase       :    int									=			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.")
					UpperCAmelCase       :    Dict									=			parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






