






'''simple docstring'''




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase__          =						logging.get_logger(__name__)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     List[Any]:
		UpperCamelCase__				:					List[str]					=					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_     ,      config=lowerCamelCase_)
		UpperCamelCase__				:					Tuple					=					downstream_dict['projector.weight']
		UpperCamelCase__				:					Any					=					downstream_dict['projector.bias']
		UpperCamelCase__				:					Dict					=					downstream_dict['model.post_net.linear.weight']
		UpperCamelCase__				:					int					=					downstream_dict['model.post_net.linear.bias']
		return model







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     int:
		UpperCamelCase__				:					Dict					=					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_     ,      config=lowerCamelCase_)
		UpperCamelCase__				:					List[str]					=					downstream_dict['model.linear.weight']
		UpperCamelCase__				:					Optional[int]					=					downstream_dict['model.linear.bias']
		return model







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Tuple:
		UpperCamelCase__				:					Tuple					=					UniSpeechSatForXVector.from_pretrained(lowerCamelCase_     ,      config=lowerCamelCase_)
		UpperCamelCase__				:					Tuple					=					downstream_dict['connector.weight']
		UpperCamelCase__				:					str					=					downstream_dict['connector.bias']
		for i, kernel_size in enumerate(hf_config.tdnn_kernel):
				UpperCamelCase__				:					Tuple					=					downstream_dict[
				    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
				]
				UpperCamelCase__				:					str					=					downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

		UpperCamelCase__				:					List[Any]					=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
		UpperCamelCase__				:					Optional[Any]					=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
		UpperCamelCase__				:					Union[str, Any]					=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
		UpperCamelCase__				:					Union[str, Any]					=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
		UpperCamelCase__				:					int					=					downstream_dict['objective.W']
		return model







@torch.no_grad()
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Dict:
		UpperCamelCase__				:					List[Any]					=					torch.load(lowerCamelCase_     ,      map_location='cpu')

		UpperCamelCase__				:					int					=					checkpoint['Downstream']

		UpperCamelCase__				:					Union[str, Any]					=					UniSpeechSatConfig.from_pretrained(lowerCamelCase_)
		UpperCamelCase__				:					List[str]					=					WavaVecaFeatureExtractor.from_pretrained(
		    lowerCamelCase_     ,      return_attention_mask=lowerCamelCase_     ,      do_normalize=lowerCamelCase_)

		UpperCamelCase__				:					Optional[Any]					=					hf_config.architectures[0]
		if arch.endswith('ForSequenceClassification'):
				UpperCamelCase__				:					List[str]					=					convert_classification(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
		elif arch.endswith('ForAudioFrameClassification'):
				UpperCamelCase__				:					List[str]					=					convert_diarization(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
		elif arch.endswith('ForXVector'):
				UpperCamelCase__				:					Optional[int]					=					convert_xvector(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
		else:
				raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}')

		if hf_config.use_weighted_layer_sum:
				UpperCamelCase__				:					Optional[int]					=					checkpoint['Featurizer']['weights']

		hf_feature_extractor.save_pretrained(lowerCamelCase_)
		hf_model.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
		lowerCAmelCase__          =						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__          =						parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)












'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPImageProcessor, CLIPProcessor







@require_vision
class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					int					=					tempfile.mkdtemp()

			# fmt: off
			UpperCamelCase__				:					Union[str, Any]					=					['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
			# fmt: on
			UpperCamelCase__				:					Dict					=					dict(zip(UpperCAmelCase_  ,					range(len(UpperCAmelCase_))))
			UpperCamelCase__				:					Optional[Any]					=					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
			UpperCamelCase__				:					Union[str, Any]					=					{'unk_token': '<unk>'}

			UpperCamelCase__				:					Union[str, Any]					=					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file'])
			UpperCamelCase__				:					Optional[Any]					=					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['merges_file'])
			with open(self.vocab_file  ,					'w'  ,					encoding='utf-8') as fp:
					fp.write(json.dumps(UpperCAmelCase_) + '\n')
			with open(self.merges_file  ,					'w'  ,					encoding='utf-8') as fp:
					fp.write('\n'.join(UpperCAmelCase_))

			UpperCamelCase__				:					Dict					=					{
			    'do_resize': True,
			    'size': 20,
			    'do_center_crop': True,
			    'crop_size': 18,
			    'do_normalize': True,
			    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
			    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
			}
			UpperCamelCase__				:					Any					=					os.path.join(self.tmpdirname  ,					UpperCAmelCase_)
			with open(self.image_processor_file  ,					'w'  ,					encoding='utf-8') as fp:
					json.dump(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict  ,					**UpperCAmelCase_   :						Union[str, Any]):
			return CLIPTokenizer.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					**UpperCAmelCase_   :						str):
			return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					**UpperCAmelCase_   :						Union[str, Any]):
			return CLIPImageProcessor.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str):
			shutil.rmtree(self.tmpdirname)

	def      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__				:					List[str]					=					[np.random.randint(255  ,					size=(3, 30, 400)  ,					dtype=np.uinta)]

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

			return image_inputs

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Union[str, Any]					=					self.get_tokenizer()
			UpperCamelCase__				:					Optional[Any]					=					self.get_rust_tokenizer()
			UpperCamelCase__				:					Any					=					self.get_image_processor()

			UpperCamelCase__				:					str					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)
			processor_slow.save_pretrained(self.tmpdirname)
			UpperCamelCase__				:					Any					=					CLIPProcessor.from_pretrained(self.tmpdirname  ,					use_fast=UpperCAmelCase_)

			UpperCamelCase__				:					str					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)
			processor_fast.save_pretrained(self.tmpdirname)
			UpperCamelCase__				:					Optional[int]					=					CLIPProcessor.from_pretrained(self.tmpdirname)

			self.assertEqual(processor_slow.tokenizer.get_vocab()  ,					tokenizer_slow.get_vocab())
			self.assertEqual(processor_fast.tokenizer.get_vocab()  ,					tokenizer_fast.get_vocab())
			self.assertEqual(tokenizer_slow.get_vocab()  ,					tokenizer_fast.get_vocab())
			self.assertIsInstance(processor_slow.tokenizer  ,					UpperCAmelCase_)
			self.assertIsInstance(processor_fast.tokenizer  ,					UpperCAmelCase_)

			self.assertEqual(processor_slow.image_processor.to_json_string()  ,					image_processor.to_json_string())
			self.assertEqual(processor_fast.image_processor.to_json_string()  ,					image_processor.to_json_string())
			self.assertIsInstance(processor_slow.image_processor  ,					UpperCAmelCase_)
			self.assertIsInstance(processor_fast.image_processor  ,					UpperCAmelCase_)

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

			UpperCamelCase__				:					List[str]					=					self.get_tokenizer(bos_token='(BOS)'  ,					eos_token='(EOS)')
			UpperCamelCase__				:					Tuple					=					self.get_image_processor(do_normalize=UpperCAmelCase_  ,					padding_value=1.0)

			UpperCamelCase__				:					Dict					=					CLIPProcessor.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):
			UpperCamelCase__				:					Optional[Any]					=					self.get_image_processor()
			UpperCamelCase__				:					int					=					self.get_tokenizer()

			UpperCamelCase__				:					List[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					int					=					self.prepare_image_inputs()

			UpperCamelCase__				:					int					=					image_processor(UpperCAmelCase_  ,					return_tensors='np')
			UpperCamelCase__				:					Optional[int]					=					processor(images=UpperCAmelCase_  ,					return_tensors='np')

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

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Optional[Any]					=					self.get_image_processor()
			UpperCamelCase__				:					Dict					=					self.get_tokenizer()

			UpperCamelCase__				:					List[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					'lower newer'

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

			UpperCamelCase__				:					Optional[Any]					=					tokenizer(UpperCAmelCase_)

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

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

			UpperCamelCase__				:					List[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

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

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

			UpperCamelCase__				:					Optional[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

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

			self.assertListEqual(UpperCAmelCase_  ,					UpperCAmelCase_)





	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Union[str, Any]					=					self.get_image_processor()
			UpperCamelCase__				:					List[str]					=					self.get_tokenizer()

			UpperCamelCase__				:					Optional[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

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

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

			self.assertListEqual(list(inputs.keys())  ,					processor.model_input_names)




