



import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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

def 			a		( _UpperCAmelCase				:   Union[str, Any]					,  _UpperCAmelCase				:   str=False					,  _UpperCAmelCase				:   Union[str, Any]=False		):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      '''backbone.''' if is_semantic else ''''''

		__UpperCAmelCase       :	List[str]						=      []
		for i in range(config.num_hidden_layers		):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight')		)
				rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias')		)
				rename_keys.append(
				    (f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight')		)
				rename_keys.append(
				    (f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias')		)
				rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight')		)
				rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias')		)
				rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight')		)
				rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias')		)
				rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight')		)
				rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias')		)

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        (f'{prefix}cls_token', '''beit.embeddings.cls_token'''),
		        (f'{prefix}patch_embed.proj.weight', '''beit.embeddings.patch_embeddings.projection.weight'''),
		        (f'{prefix}patch_embed.proj.bias', '''beit.embeddings.patch_embeddings.projection.bias'''),
		    ]		)

		if has_lm_head:
				# mask token + shared relative position bias + layernorm
				rename_keys.extend(
				    [
				        ('''mask_token''', '''beit.embeddings.mask_token'''),
				        (
				            '''rel_pos_bias.relative_position_bias_table''',
				            '''beit.encoder.relative_position_bias.relative_position_bias_table''',
				        ),
				        (
				            '''rel_pos_bias.relative_position_index''',
				            '''beit.encoder.relative_position_bias.relative_position_index''',
				        ),
				        ('''norm.weight''', '''layernorm.weight'''),
				        ('''norm.bias''', '''layernorm.bias'''),
				    ]		)
		elif is_semantic:
				# semantic segmentation classification heads
				rename_keys.extend(
				    [
				        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
				        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
				        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
				        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
				    ]		)
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
				        ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
				        ('''head.weight''', '''classifier.weight'''),
				        ('''head.bias''', '''classifier.bias'''),
				    ]		)

		return rename_keys

def 			a		( _UpperCAmelCase				:   Optional[int]					,  _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   List[str]=False					,  _UpperCAmelCase				:   Union[str, Any]=False		):







		'''simple docstring'''





		for i in range(config.num_hidden_layers		):
				__UpperCAmelCase       :	Dict						=      '''backbone.''' if is_semantic else ''''''
				# queries, keys and values
				__UpperCAmelCase       :	int						=      state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'		)
				__UpperCAmelCase       :	Union[str, Any]						=      state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'		)
				__UpperCAmelCase       :	Optional[Any]						=      state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'		)

				__UpperCAmelCase       :	Union[str, Any]						=      in_proj_weight[
				    : config.hidden_size, :
				]
				__UpperCAmelCase       :	int						=      q_bias
				__UpperCAmelCase       :	List[str]						=      in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				__UpperCAmelCase       :	Optional[Any]						=      in_proj_weight[
				    -config.hidden_size :, :
				]
				__UpperCAmelCase       :	List[Any]						=      v_bias

				# gamma_1 and gamma_2
				# we call them lambda because otherwise they are renamed when using .from_pretrained
				__UpperCAmelCase       :	List[str]						=      state_dict.pop(f'{prefix}blocks.{i}.gamma_1'		)
				__UpperCAmelCase       :	Any						=      state_dict.pop(f'{prefix}blocks.{i}.gamma_2'		)

				__UpperCAmelCase       :	Any						=      gamma_a
				__UpperCAmelCase       :	List[Any]						=      gamma_a

				# relative_position bias table + index
				if not has_lm_head:
						# each layer has its own relative position bias
						__UpperCAmelCase       :	List[str]						=      state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'		)
						__UpperCAmelCase       :	str						=      state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'		)

						__UpperCAmelCase       :	Optional[int]						=      table
						__UpperCAmelCase       :	Optional[int]						=      index

def 			a		( _UpperCAmelCase				:   int					,  _UpperCAmelCase				:   Union[str, Any]					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





		__UpperCAmelCase       :	Dict						=      dct.pop(_UpperCAmelCase		)
		__UpperCAmelCase       :	Union[str, Any]						=      val

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		__UpperCAmelCase       :	Optional[Any]						=      Image.open(requests.get(_UpperCAmelCase					,  stream=_UpperCAmelCase		).raw		)
		return im

@torch.no_grad()
def 			a		( _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Optional[Any]		):







		'''simple docstring'''





		__UpperCAmelCase       :	Optional[int]						=      BeitConfig()
		__UpperCAmelCase       :	List[Any]						=      False
		__UpperCAmelCase       :	List[str]						=      False
		__UpperCAmelCase       :	List[str]						=      '''huggingface/label-files'''
		# set config parameters based on URL
		if checkpoint_url[-9:-4] == "pt22k":
				# masked image modeling
				__UpperCAmelCase       :	Optional[int]						=      True
				__UpperCAmelCase       :	Optional[Any]						=      True
				__UpperCAmelCase       :	Tuple						=      True
		elif checkpoint_url[-9:-4] == "ft22k":
				# intermediate fine-tuning on ImageNet-22k
				__UpperCAmelCase       :	Optional[Any]						=      True
				__UpperCAmelCase       :	Optional[int]						=      2_18_41
				__UpperCAmelCase       :	List[Any]						=      '''imagenet-22k-id2label.json'''
				__UpperCAmelCase       :	Union[str, Any]						=      json.load(open(hf_hub_download(_UpperCAmelCase					,  _UpperCAmelCase					,  repo_type='''dataset'''		)					,  '''r'''		)		)
				__UpperCAmelCase       :	Tuple						=      {int(_UpperCAmelCase		): v for k, v in idalabel.items()}
				# this dataset contains 21843 labels but the model only has 21841
				# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
				del idalabel[92_05]
				del idalabel[1_50_27]
				__UpperCAmelCase       :	Optional[int]						=      idalabel
				__UpperCAmelCase       :	List[Any]						=      {v: k for k, v in idalabel.items()}
		elif checkpoint_url[-8:-4] == "to1k":
				# fine-tuning on ImageNet-1k
				__UpperCAmelCase       :	Optional[Any]						=      True
				__UpperCAmelCase       :	Tuple						=      10_00
				__UpperCAmelCase       :	str						=      '''imagenet-1k-id2label.json'''
				__UpperCAmelCase       :	List[Any]						=      json.load(open(hf_hub_download(_UpperCAmelCase					,  _UpperCAmelCase					,  repo_type='''dataset'''		)					,  '''r'''		)		)
				__UpperCAmelCase       :	str						=      {int(_UpperCAmelCase		): v for k, v in idalabel.items()}
				__UpperCAmelCase       :	Union[str, Any]						=      idalabel
				__UpperCAmelCase       :	str						=      {v: k for k, v in idalabel.items()}
				if "384" in checkpoint_url:
						__UpperCAmelCase       :	Optional[Any]						=      3_84
				if "512" in checkpoint_url:
						__UpperCAmelCase       :	Dict						=      5_12
		elif "ade20k" in checkpoint_url:
				# fine-tuning
				__UpperCAmelCase       :	List[str]						=      True
				__UpperCAmelCase       :	Optional[int]						=      1_50
				__UpperCAmelCase       :	Tuple						=      '''ade20k-id2label.json'''
				__UpperCAmelCase       :	int						=      json.load(open(hf_hub_download(_UpperCAmelCase					,  _UpperCAmelCase					,  repo_type='''dataset'''		)					,  '''r'''		)		)
				__UpperCAmelCase       :	Optional[Any]						=      {int(_UpperCAmelCase		): v for k, v in idalabel.items()}
				__UpperCAmelCase       :	List[Any]						=      idalabel
				__UpperCAmelCase       :	int						=      {v: k for k, v in idalabel.items()}
				__UpperCAmelCase       :	List[str]						=      6_40
				__UpperCAmelCase       :	Any						=      True
		else:
				raise ValueError('''Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''''		)

		# size of the architecture
		if "base" in checkpoint_url:
				pass
		elif "large" in checkpoint_url:
				__UpperCAmelCase       :	Optional[Any]						=      10_24
				__UpperCAmelCase       :	Tuple						=      40_96
				__UpperCAmelCase       :	Optional[Any]						=      24
				__UpperCAmelCase       :	List[Any]						=      16
				if "ade20k" in checkpoint_url:
						__UpperCAmelCase       :	Tuple						=      6_40
						__UpperCAmelCase       :	List[str]						=      [7, 11, 15, 23]
		else:
				raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''		)

		# load state_dict of original model, remove and rename some keys
		__UpperCAmelCase       :	int						=      torch.hub.load_state_dict_from_url(_UpperCAmelCase					,  map_location='''cpu'''					,  check_hash=_UpperCAmelCase		)
		__UpperCAmelCase       :	List[Any]						=      state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

		__UpperCAmelCase       :	Optional[int]						=      create_rename_keys(_UpperCAmelCase					,  has_lm_head=_UpperCAmelCase					,  is_semantic=_UpperCAmelCase		)
		for src, dest in rename_keys:
				rename_key(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase		)
		read_in_q_k_v(_UpperCAmelCase					,  _UpperCAmelCase					,  has_lm_head=_UpperCAmelCase					,  is_semantic=_UpperCAmelCase		)
		if is_semantic:
				# add prefix to decoder keys
				for key, val in state_dict.copy().items():
						__UpperCAmelCase       :	Union[str, Any]						=      state_dict.pop(_UpperCAmelCase		)
						if key.startswith('''backbone.fpn'''		):
								__UpperCAmelCase       :	List[Any]						=      key.replace('''backbone.fpn'''					,  '''fpn'''		)
						__UpperCAmelCase       :	Optional[int]						=      val

    # load HuggingFace model
		if checkpoint_url[-9:-4] == "pt22k":
				__UpperCAmelCase       :	Any						=      BeitForMaskedImageModeling(_UpperCAmelCase		)
		elif "ade20k" in checkpoint_url:
				__UpperCAmelCase       :	List[Any]						=      BeitForSemanticSegmentation(_UpperCAmelCase		)
		else:
				__UpperCAmelCase       :	int						=      BeitForImageClassification(_UpperCAmelCase		)
		model.eval()
		model.load_state_dict(_UpperCAmelCase		)

		# Check outputs on an image
		if is_semantic:
				__UpperCAmelCase       :	Union[str, Any]						=      BeitImageProcessor(size=config.image_size					,  do_center_crop=_UpperCAmelCase		)
				__UpperCAmelCase       :	str						=      load_dataset('''hf-internal-testing/fixtures_ade20k'''					,  split='''test'''		)
				__UpperCAmelCase       :	Optional[int]						=      Image.open(ds[0]['''file''']		)
		else:
				__UpperCAmelCase       :	List[Any]						=      BeitImageProcessor(
				    size=config.image_size					,  resample=PILImageResampling.BILINEAR					,  do_center_crop=_UpperCAmelCase		)
				__UpperCAmelCase       :	Optional[int]						=      prepare_img()

		__UpperCAmelCase       :	Optional[int]						=      image_processor(images=_UpperCAmelCase					,  return_tensors='''pt'''		)
		__UpperCAmelCase       :	Dict						=      encoding['''pixel_values''']

		__UpperCAmelCase       :	str						=      model(_UpperCAmelCase		)
		__UpperCAmelCase       :	Union[str, Any]						=      outputs.logits

		# verify logits
		__UpperCAmelCase       :	Union[str, Any]						=      torch.Size([1, 10_00]		)
		if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''		):
				__UpperCAmelCase       :	Optional[Any]						=      torch.Size([1, 1_96, 81_92]		)
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''		):
				__UpperCAmelCase       :	List[Any]						=      torch.Size([1, 1_96, 81_92]		)
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''		):
				__UpperCAmelCase       :	str						=      torch.Size([1, 2_18_41]		)
				__UpperCAmelCase       :	Any						=      torch.tensor([2.22_88, 2.46_71, 0.73_95]		)
				__UpperCAmelCase       :	Dict						=      23_97
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''		):
				__UpperCAmelCase       :	Optional[Any]						=      torch.Size([1, 2_18_41]		)
				__UpperCAmelCase       :	Any						=      torch.tensor([1.68_81, -0.27_87, 0.59_01]		)
				__UpperCAmelCase       :	str						=      23_96
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''		):
				__UpperCAmelCase       :	Dict						=      torch.tensor([0.12_41, 0.07_98, -0.65_69]		)
				__UpperCAmelCase       :	Dict						=      2_85
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''		):
				__UpperCAmelCase       :	str						=      torch.tensor([-1.23_85, -1.09_87, -1.01_08]		)
				__UpperCAmelCase       :	Optional[int]						=      2_81
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''		):
				__UpperCAmelCase       :	Optional[int]						=      torch.tensor([-1.53_03, -0.94_84, -0.31_47]		)
				__UpperCAmelCase       :	List[Any]						=      7_61
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''		):
				__UpperCAmelCase       :	str						=      torch.tensor([0.46_10, -0.09_28, 0.20_86]		)
				__UpperCAmelCase       :	Any						=      7_61
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''		):
				__UpperCAmelCase       :	int						=      torch.tensor([-0.48_04, 0.62_57, -0.18_37]		)
				__UpperCAmelCase       :	Dict						=      7_61
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''		):
				__UpperCAmelCase       :	Optional[int]						=      torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]		)
				__UpperCAmelCase       :	int						=      7_61
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''		):
				__UpperCAmelCase       :	Tuple						=      torch.tensor([-0.30_62, 0.72_61, 0.48_52]		)
				__UpperCAmelCase       :	Union[str, Any]						=      7_61
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''		):
				__UpperCAmelCase       :	Any						=      (1, 1_50, 1_60, 1_60)
				__UpperCAmelCase       :	List[str]						=      torch.tensor(
				    [
				        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
				        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
				        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
				    ]		)
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''		):
				__UpperCAmelCase       :	Optional[Any]						=      (1, 1_50, 1_60, 1_60)
				__UpperCAmelCase       :	List[str]						=      torch.tensor(
				    [
				        [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
				        [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
				        [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
				    ]		)
		else:
				raise ValueError('''Can\'t verify logits as model is not supported'''		)

		assert logits.shape == expected_shape, "Shape of logits not as expected"
		if not has_lm_head:
				if is_semantic:
						assert torch.allclose(
						    logits[0, :3, :3, :3]					,  _UpperCAmelCase					,  atol=1e-3		), "First elements of logits not as expected"
				else:
						print('''Predicted class idx:'''					,  logits.argmax(-1		).item()		)
						assert torch.allclose(
						    logits[0, :3]					,  _UpperCAmelCase					,  atol=1e-3		), "First elements of logits not as expected"
						assert logits.argmax(-1		).item() == expected_class_idx, "Predicted class index not as expected"

		Path(_UpperCAmelCase		).mkdir(exist_ok=_UpperCAmelCase		)
		print(f'Saving model to {pytorch_dump_folder_path}'		)
		model.save_pretrained(_UpperCAmelCase		)
		print(f'Saving image processor to {pytorch_dump_folder_path}'		)
		image_processor.save_pretrained(_UpperCAmelCase		)


if __name__ == "__main__":
				__A       =argparse.ArgumentParser()

				parser.add_argument(
				    "--checkpoint_url",
				    default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
				    type=str,
				    help="URL to the original PyTorch checkpoint (.pth file).",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
				)
				__A       =parser.parse_args()
				convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
				from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

				from .test_feature_extraction_speechta import floats_list


__A       =get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")

@require_torch
class 						UpperCAmelCase__     (     unittest.TestCase  ):




			'''simple docstring'''






			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      tempfile.mkdtemp()

					__UpperCAmelCase       :	Optional[int]						=      SpeechTaTokenizer(a_					)
					tokenizer.save_pretrained(self.tmpdirname					)

					__UpperCAmelCase       :	Union[str, Any]						=      {
					    '''feature_size''': 1,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_60_00,
					    '''do_normalize''': False,
					    '''num_mel_bins''': 80,
					    '''hop_length''': 16,
					    '''win_length''': 64,
					    '''win_function''': '''hann_window''',
					    '''fmin''': 80,
					    '''fmax''': 76_00,
					    '''mel_floor''': 1e-10,
					    '''reduction_factor''': 2,
					    '''return_attention_mask''': True,
					}

					__UpperCAmelCase       :	Tuple						=      os.path.join(self.tmpdirname			,			a_					)
					with open(self.feature_extraction_file			,			'''w'''			,			encoding='''utf-8'''					) as fp:
							fp.write(json.dumps(a_					) + '''\n'''					)




			def 					snake_case__	(  self				:					Any			,			**a_				:					Tuple					):
					'''simple docstring'''





					return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,			**a_					)




			def 					snake_case__	(  self				:					Dict			,			**a_				:					str					):
					'''simple docstring'''





					return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,			**a_					)




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					shutil.rmtree(self.tmpdirname					)




			def 					snake_case__	(  self				:					Dict					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      self.get_tokenizer()
					__UpperCAmelCase       :	str						=      self.get_feature_extractor()

					__UpperCAmelCase       :	List[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					processor.save_pretrained(self.tmpdirname					)
					__UpperCAmelCase       :	List[str]						=      SpeechTaProcessor.from_pretrained(self.tmpdirname					)

					self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer.get_vocab()					)
					self.assertIsInstance(processor.tokenizer			,			a_					)

					self.assertEqual(processor.feature_extractor.to_json_string()			,			feature_extractor.to_json_string()					)
					self.assertIsInstance(processor.feature_extractor			,			a_					)




			def 					snake_case__	(  self				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      SpeechTaProcessor(tokenizer=self.get_tokenizer()			,			feature_extractor=self.get_feature_extractor()					)
					processor.save_pretrained(self.tmpdirname					)

					__UpperCAmelCase       :	Optional[Any]						=      self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''					)
					__UpperCAmelCase       :	List[Any]						=      self.get_feature_extractor(do_normalize=a_			,			padding_value=1.0					)

					__UpperCAmelCase       :	List[str]						=      SpeechTaProcessor.from_pretrained(
					    self.tmpdirname			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			do_normalize=a_			,			padding_value=1.0					)

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

					self.assertEqual(processor.feature_extractor.to_json_string()			,			feature_extractor_add_kwargs.to_json_string()					)
					self.assertIsInstance(processor.feature_extractor			,			a_					)




			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Union[str, Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	List[Any]						=      floats_list((3, 10_00)					)

					__UpperCAmelCase       :	Tuple						=      feature_extractor(audio=a_			,			return_tensors='''np'''					)
					__UpperCAmelCase       :	List[str]						=      processor(audio=a_			,			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 					snake_case__	(  self				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Dict						=      self.get_tokenizer()

					__UpperCAmelCase       :	Optional[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	Union[str, Any]						=      floats_list((3, 10_00)					)

					__UpperCAmelCase       :	Dict						=      feature_extractor(audio_target=a_			,			return_tensors='''np'''					)
					__UpperCAmelCase       :	str						=      processor(audio_target=a_			,			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 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Union[str, Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Optional[int]						=      self.get_tokenizer()

					__UpperCAmelCase       :	Optional[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''This is a test string'''

					__UpperCAmelCase       :	List[Any]						=      processor(text=a_					)
					__UpperCAmelCase       :	Optional[int]						=      tokenizer(a_					)

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




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Optional[Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''This is a test string'''

					__UpperCAmelCase       :	Optional[Any]						=      processor(text_target=a_					)
					__UpperCAmelCase       :	Optional[int]						=      tokenizer(a_					)

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




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      self.get_feature_extractor()
					__UpperCAmelCase       :	Union[str, Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	int						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

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

					__UpperCAmelCase       :	Optional[Any]						=      processor.batch_decode(a_					)
					__UpperCAmelCase       :	Any						=      tokenizer.batch_decode(a_					)

					self.assertListEqual(a_			,			a_					)




			def 					snake_case__	(  self				:					str					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      self.get_feature_extractor()
					__UpperCAmelCase       :	str						=      self.get_tokenizer()

					__UpperCAmelCase       :	Optional[Any]						=      SpeechTaProcessor(tokenizer=a_			,			feature_extractor=a_					)

					self.assertListEqual(
					    processor.model_input_names			,			feature_extractor.model_input_names			,			msg='''`processor` and `feature_extractor` model input names do not match'''			,			)





