
"""simple docstring"""




import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

UpperCamelCase_									={
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
UpperCamelCase_									=[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						for attribute in key.split('''.'''       ):
												_UpperCamelCase      : Optional[Any]         =   getattr(lowerCAmelCase__	,						lowerCAmelCase__       )

						if weight_type is not None:
												_UpperCamelCase      : Any         =   getattr(lowerCAmelCase__	,						lowerCAmelCase__       ).shape
						else:
												_UpperCamelCase      : List[str]         =   hf_pointer.shape

						assert hf_shape == value.shape, (
						    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
						    F""" {value.shape} for {full_name}"""
						)

						if weight_type == "weight":
												_UpperCamelCase      : str         =   value
						elif weight_type == "weight_g":
												_UpperCamelCase      : Optional[Any]         =   value
						elif weight_type == "weight_v":
												_UpperCamelCase      : Dict         =   value
						elif weight_type == "bias":
												_UpperCamelCase      : Optional[int]         =   value
						else:
												_UpperCamelCase      : List[Any]         =   value

						logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""       )





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Tuple         =   []
						_UpperCamelCase      : Optional[Any]         =   fairseq_model.state_dict()

						_UpperCamelCase      : Tuple         =   hf_model.feature_extractor
						_UpperCamelCase      : Optional[Any]         =   hf_model.adapter

						for name, value in fairseq_dict.items():
												_UpperCamelCase      : List[str]         =   False
												if "conv_layers" in name:
																		load_conv_layer(
																		    lowerCAmelCase__	,						lowerCAmelCase__	,						lowerCAmelCase__	,						lowerCAmelCase__	,						hf_model.config.feat_extract_norm == '''group'''	,						)
																		_UpperCamelCase      : Dict         =   True
												elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']       ):
																		load_adapter(lowerCAmelCase__	,						lowerCAmelCase__	,						lowerCAmelCase__	,						lowerCAmelCase__       )
																		_UpperCamelCase      : Tuple         =   True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split('''w2v_model.'''       )[-1] == name.split('''.'''       )[0]:
																														_UpperCamelCase      : Dict         =   True
																														if "*" in mapped_key:
																																				_UpperCamelCase      : Any         =   name.split(lowerCAmelCase__       )[0].split('''.'''       )[-2]
																																				_UpperCamelCase      : Optional[Any]         =   mapped_key.replace('''*'''	,						lowerCAmelCase__       )
																														if "weight_g" in name:
																																				_UpperCamelCase      : str         =   '''weight_g'''
																														elif "weight_v" in name:
																																				_UpperCamelCase      : Tuple         =   '''weight_v'''
																														elif "bias" in name:
																																				_UpperCamelCase      : str         =   '''bias'''
																														elif "weight" in name:
																																				_UpperCamelCase      : Dict         =   '''weight'''
																														else:
																																				_UpperCamelCase      : List[Any]         =   None
																														set_recursively(lowerCAmelCase__	,						lowerCAmelCase__	,						lowerCAmelCase__	,						lowerCAmelCase__	,						lowerCAmelCase__       )
																								continue
												if not is_used:
																		unused_weights.append(lowerCAmelCase__       )

						logger.warning(F"""Unused weights: {unused_weights}"""       )





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[Any]         =   full_name.split('''conv_layers.'''       )[-1]
						_UpperCamelCase      : Any         =   name.split('''.'''       )
						_UpperCamelCase      : Optional[int]         =   int(items[0]       )
						_UpperCamelCase      : Optional[int]         =   int(items[1]       )

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																		)
																		_UpperCamelCase      : Optional[int]         =   value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																		)
																		_UpperCamelCase      : Optional[int]         =   value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																		    " found."
																		)
																		_UpperCamelCase      : Union[str, Any]         =   value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																		)
																		_UpperCamelCase      : Optional[Any]         =   value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
						else:
												unused_weights.append(lowerCAmelCase__       )





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Union[str, Any]         =   full_name.split('''adaptor.'''       )[-1]
						_UpperCamelCase      : Dict         =   name.split('''.'''       )

						if items[1].isdigit():
												_UpperCamelCase      : Union[str, Any]         =   int(items[1]       )
						else:
												_UpperCamelCase      : Tuple         =   None

						if "adaptor" not in full_name:
												if "proj_ln" in full_name:
																		# has to be layer norm
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.bias.data.shape
																								), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
																								_UpperCamelCase      : List[str]         =   value
																								logger.info(F"""Adapter proj layer norm bias was initialized from {full_name}."""       )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.weight.data.shape
																								), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
																								_UpperCamelCase      : int         =   value
												else:
																		# has to be projection layer
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj.bias.data.shape
																								), F"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
																								_UpperCamelCase      : Optional[Any]         =   value
																								logger.info(F"""Adapter proj layer bias was initialized from {full_name}."""       )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj.weight.data.shape
																								), F"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
																								_UpperCamelCase      : Optional[Any]         =   value
																								logger.info(F"""Adapter proj layer weight was initialized from {full_name}."""       )
						elif isinstance(lowerCAmelCase__	,						lowerCAmelCase__       ):
												if "bias" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																		), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
																		_UpperCamelCase      : Union[str, Any]         =   value
																		logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""       )
												elif "weight" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																		), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
																		_UpperCamelCase      : Union[str, Any]         =   value
																		logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""       )
						else:
												unused_weights.append(lowerCAmelCase__       )





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   emb.weight.shape
						_UpperCamelCase      : Optional[int]         =   nn.Linear(lowerCAmelCase__	,						lowerCAmelCase__	,						bias=lowerCAmelCase__       )
						_UpperCamelCase      : Optional[Any]         =   emb.weight.data
						return lin_layer





@torch.no_grad()
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						):
						_UpperCamelCase      : Tuple         =   WavaVecaConfig.from_pretrained(
						    lowerCAmelCase__	,						add_adapter=lowerCAmelCase__	,						adapter_stride=lowerCAmelCase__	,						adapter_kernel_size=lowerCAmelCase__	,						use_auth_token=lowerCAmelCase__	,						output_hidden_size=lowerCAmelCase__	,						)
						_UpperCamelCase      : int         =   MBartConfig.from_pretrained(lowerCAmelCase__       )

						# load model
						_UpperCamelCase      : Any         =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]	,						arg_overrides={
						        '''config_yaml''': config_yaml_path,
						        '''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       ),
						        '''w2v_path''': checkpoint_path,
						        '''load_pretrained_decoder_from''': None,
						    }	,						)
						_UpperCamelCase      : List[Any]         =   model[0].eval()

						# load feature extractor
						_UpperCamelCase      : Dict         =   WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase__	,						use_auth_token=lowerCAmelCase__       )

						# set weights for wav2vec2 encoder
						_UpperCamelCase      : str         =   WavaVecaModel(lowerCAmelCase__       )

						recursively_load_weights_wavaveca(model.encoder	,						lowerCAmelCase__       )

						# load decoder weights
						_UpperCamelCase      : List[Any]         =   MBartForCausalLM(lowerCAmelCase__       )
						_UpperCamelCase      : str         =   hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,						strict=lowerCAmelCase__       )
						logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}"""       )
						logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""       )

						_UpperCamelCase      : List[Any]         =   SpeechEncoderDecoderModel(encoder=lowerCAmelCase__	,						decoder=lowerCAmelCase__       )
						_UpperCamelCase      : List[str]         =   False

						_UpperCamelCase      : int         =   MBartaaTokenizer(lowerCAmelCase__       )
						tokenizer.save_pretrained(lowerCAmelCase__       )

						_UpperCamelCase      : Dict         =   hf_wavavec.config.to_dict()
						_UpperCamelCase      : str         =   tokenizer.pad_token_id
						_UpperCamelCase      : Union[str, Any]         =   tokenizer.bos_token_id
						_UpperCamelCase      : str         =   tokenizer.eos_token_id
						_UpperCamelCase      : Optional[int]         =   '''mbart50'''
						_UpperCamelCase      : Optional[int]         =   '''wav2vec2'''

						_UpperCamelCase      : Any         =   tokenizer.eos_token_id
						_UpperCamelCase      : Tuple         =   25_0004
						_UpperCamelCase      : str         =   tokenizer.eos_token_id

						_UpperCamelCase      : Tuple         =   SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase__       )

						hf_wavavec.save_pretrained(lowerCAmelCase__       )
						feature_extractor.save_pretrained(lowerCAmelCase__       )


if __name__ == "__main__":
						UpperCamelCase_									=argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
						parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
						parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
						parser.add_argument(
						    """--encoder_config_path""",
						    default="""facebook/wav2vec2-xls-r-1b""",
						    type=str,
						    help="""Path to hf encoder wav2vec2 checkpoint config""",
						)
						parser.add_argument(
						    """--decoder_config_path""",
						    default="""facebook/mbart-large-50-one-to-many-mmt""",
						    type=str,
						    help="""Path to hf decoder checkpoint config""",
						)
						parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
						parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
						parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
						parser.add_argument("""--encoder_output_dim""", default=1_024, type=int, help="""encoder output dim""")
						parser.add_argument("""--start_token_id""", default=250_004, type=int, help="""`decoder_start_token_id` of model config""")

						UpperCamelCase_									=parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.dict_path,
						    args.config_yaml_path,
						    encoder_config_path=args.encoder_config_path,
						    decoder_config_path=args.decoder_config_path,
						    add_adapter=args.add_adapter,
						    adapter_kernel_size=args.adapter_kernel_size,
						    adapter_stride=args.adapter_stride,
						    decoder_start_token_id=args.start_token_id,
						    encoder_output_dim=args.encoder_output_dim,
						)










"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor




@require_vision
class 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : int         =   tempfile.mkdtemp()

										_UpperCamelCase      : List[str]         =   [
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''[PAD]''',
										    '''[MASK]''',
										    '''的''',
										    '''价''',
										    '''格''',
										    '''是''',
										    '''15''',
										    '''便''',
										    '''alex''',
										    '''##andra''',
										    '''，''',
										    '''。''',
										    '''-''',
										    '''t''',
										    '''shirt''',
										]
										_UpperCamelCase      : Optional[Any]         =   os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['''vocab_file''']							)
										with open(self.vocab_file,			'''w''',			encoding='''utf-8'''							) as vocab_writer:
																vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]							)							)

										_UpperCamelCase      : Dict         =   {
										    '''do_resize''': True,
										    '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
										    '''do_center_crop''': True,
										    '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
										    '''do_normalize''': True,
										    '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
										    '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
										    '''do_convert_rgb''': True,
										}
										_UpperCamelCase      : Optional[Any]         =   os.path.join(self.tmpdirname,			lowerCAmelCase__							)
										with open(self.image_processor_file,			'''w''',			encoding='''utf-8'''							) as fp:
																json.dump(lowerCAmelCase__,			lowerCAmelCase__							)






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




										'''simple docstring'''



										return BertTokenizer.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   Union[str, Any],			**lowerCAmelCase__		:   Tuple							)     ->				str:




										'''simple docstring'''



										return BertTokenizerFast.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   Any,			**lowerCAmelCase__		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



										return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






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




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname							)






				def     snake_case     (			self		:   Any							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   [np.random.randint(2_5_5,			size=(3, 3_0, 4_0_0),			dtype=np.uinta							)]

										_UpperCamelCase      : List[Any]         =   [Image.fromarray(np.moveaxis(lowerCAmelCase__,			0,			-1							)							) for x in image_inputs]

										return image_inputs






				def     snake_case     (			self		:   str							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.get_tokenizer()
										_UpperCamelCase      : int         =   self.get_rust_tokenizer()
										_UpperCamelCase      : int         =   self.get_image_processor()

										_UpperCamelCase      : Tuple         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)
										processor_slow.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname,			use_fast=lowerCAmelCase__							)

										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)
										processor_fast.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor.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,			lowerCAmelCase__							)
										self.assertIsInstance(processor_fast.tokenizer,			lowerCAmelCase__							)

										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,			lowerCAmelCase__							)
										self.assertIsInstance(processor_fast.image_processor,			lowerCAmelCase__							)






				def     snake_case     (			self		:   int							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer(),			image_processor=self.get_image_processor()							)
										processor.save_pretrained(self.tmpdirname							)

										_UpperCamelCase      : Dict         =   self.get_tokenizer(cls_token='''(CLS)''',			sep_token='''(SEP)'''							)
										_UpperCamelCase      : List[str]         =   self.get_image_processor(do_normalize=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor.from_pretrained(
										    self.tmpdirname,			cls_token='''(CLS)''',			sep_token='''(SEP)''',			do_normalize=lowerCAmelCase__							)

										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     snake_case     (			self		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



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

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   self.prepare_image_inputs()

										_UpperCamelCase      : Any         =   image_processor(lowerCAmelCase__,			return_tensors='''np'''							)
										_UpperCamelCase      : Any         =   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     snake_case     (			self		:   Optional[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



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

										_UpperCamelCase      : Any         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Tuple         =   '''Alexandra，T-shirt的价格是15便士。'''

										_UpperCamelCase      : List[str]         =   processor(text=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   tokenizer(lowerCAmelCase__							)

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






				def     snake_case     (			self		:   Dict							)     ->				Tuple:




										'''simple docstring'''



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

										_UpperCamelCase      : Dict         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   '''Alexandra，T-shirt的价格是15便士。'''
										_UpperCamelCase      : Union[str, Any]         =   self.prepare_image_inputs()

										_UpperCamelCase      : str         =   processor(text=lowerCAmelCase__,			images=lowerCAmelCase__							)

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

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






				def     snake_case     (			self		:   Optional[Any]							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.get_image_processor()
										_UpperCamelCase      : int         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : List[Any]         =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase      : List[Any]         =   processor.batch_decode(lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   tokenizer.batch_decode(lowerCAmelCase__							)

										self.assertListEqual(lowerCAmelCase__,			lowerCAmelCase__							)






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




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.get_image_processor()
										_UpperCamelCase      : Optional[int]         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   '''Alexandra，T-shirt的价格是15便士。'''
										_UpperCamelCase      : int         =   self.prepare_image_inputs()

										_UpperCamelCase      : Dict         =   processor(text=lowerCAmelCase__,			images=lowerCAmelCase__							)

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



