

"""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 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							Dict						):
						lowerCAmelCase__      :		List[Any]    							=      tempfile.mkdtemp()

						lowerCAmelCase__      :		Union[str, Any]    							=      [
						    '''[UNK]''',
						    '''[CLS]''',
						    '''[SEP]''',
						    '''[PAD]''',
						    '''[MASK]''',
						    '''的''',
						    '''价''',
						    '''格''',
						    '''是''',
						    '''15''',
						    '''便''',
						    '''alex''',
						    '''##andra''',
						    '''，''',
						    '''。''',
						    '''-''',
						    '''t''',
						    '''shirt''',
						]
						lowerCAmelCase__      :		Dict    							=      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]						)						)

						lowerCAmelCase__      :		Optional[Any]    							=      {
						    '''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''': [0.4814_5466, 0.457_8275, 0.4082_1073],
						    '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
						    '''do_convert_rgb''': True,
						}
						lowerCAmelCase__      :		List[str]    							=      os.path.join(self.tmpdirname  ,lowercase_						)
						with open(self.image_processor_file  ,'''w'''  ,encoding='''utf-8'''						) as fp:
							json.dump(lowercase_  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							str  ,**lowercase_     :							Union[str, Any]						):
						return BertTokenizer.from_pretrained(self.tmpdirname  ,**lowercase_						)







					def 			__lowerCAmelCase					(			self     :							List[str]  ,**lowercase_     :							Union[str, Any]						):
						return BertTokenizerFast.from_pretrained(self.tmpdirname  ,**lowercase_						)







					def 			__lowerCAmelCase					(			self     :							str  ,**lowercase_     :							Union[str, Any]						):
						return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname  ,**lowercase_						)







					def 			__lowerCAmelCase					(			self     :							int						):
						shutil.rmtree(self.tmpdirname						)







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		Any    							=      [np.random.randint(2_5_5  ,size=(3, 3_0, 4_0_0)  ,dtype=np.uinta						)]

						lowerCAmelCase__      :		Optional[int]    							=      [Image.fromarray(np.moveaxis(lowercase_  ,0  ,-1						)						) for x in image_inputs]

						return image_inputs







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		List[str]    							=      self.get_tokenizer()
						lowerCAmelCase__      :		Tuple    							=      self.get_rust_tokenizer()
						lowerCAmelCase__      :		Tuple    							=      self.get_image_processor()

						lowerCAmelCase__      :		List[str]    							=      ChineseCLIPProcessor(tokenizer=lowercase_  ,image_processor=lowercase_						)
						processor_slow.save_pretrained(self.tmpdirname						)
						lowerCAmelCase__      :		Optional[int]    							=      ChineseCLIPProcessor.from_pretrained(self.tmpdirname  ,use_fast=lowercase_						)

						lowerCAmelCase__      :		Optional[Any]    							=      ChineseCLIPProcessor(tokenizer=lowercase_  ,image_processor=lowercase_						)
						processor_fast.save_pretrained(self.tmpdirname						)
						lowerCAmelCase__      :		str    							=      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  ,lowercase_						)
						self.assertIsInstance(processor_fast.tokenizer  ,lowercase_						)

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







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		Optional[Any]    							=      ChineseCLIPProcessor(tokenizer=self.get_tokenizer()  ,image_processor=self.get_image_processor()						)
						processor.save_pretrained(self.tmpdirname						)

						lowerCAmelCase__      :		int    							=      self.get_tokenizer(cls_token='''(CLS)'''  ,sep_token='''(SEP)'''						)
						lowerCAmelCase__      :		Any    							=      self.get_image_processor(do_normalize=lowercase_						)

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

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

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







					def 			__lowerCAmelCase					(			self     :							Dict						):
						lowerCAmelCase__      :		Tuple    							=      self.get_image_processor()
						lowerCAmelCase__      :		Dict    							=      self.get_tokenizer()

						lowerCAmelCase__      :		Union[str, Any]    							=      ChineseCLIPProcessor(tokenizer=lowercase_  ,image_processor=lowercase_						)

						lowerCAmelCase__      :		Tuple    							=      self.prepare_image_inputs()

						lowerCAmelCase__      :		int    							=      image_processor(lowercase_  ,return_tensors='''np'''						)
						lowerCAmelCase__      :		List[str]    							=      processor(images=lowercase_  ,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 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		Union[str, Any]    							=      self.get_image_processor()
						lowerCAmelCase__      :		str    							=      self.get_tokenizer()

						lowerCAmelCase__      :		int    							=      ChineseCLIPProcessor(tokenizer=lowercase_  ,image_processor=lowercase_						)

						lowerCAmelCase__      :		List[str]    							=      '''Alexandra，T-shirt的价格是15便士。'''

						lowerCAmelCase__      :		Tuple    							=      processor(text=lowercase_						)

						lowerCAmelCase__      :		List[Any]    							=      tokenizer(lowercase_						)

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







					def 			__lowerCAmelCase					(			self     :							Dict						):
						lowerCAmelCase__      :		Optional[Any]    							=      self.get_image_processor()
						lowerCAmelCase__      :		Any    							=      self.get_tokenizer()

						lowerCAmelCase__      :		Tuple    							=      ChineseCLIPProcessor(tokenizer=lowercase_  ,image_processor=lowercase_						)

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

						lowerCAmelCase__      :		Any    							=      processor(text=lowercase_  ,images=lowercase_						)

						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(lowercase_						):
							processor()







					def 			__lowerCAmelCase					(			self     :							Dict						):
						lowerCAmelCase__      :		int    							=      self.get_image_processor()
						lowerCAmelCase__      :		Optional[int]    							=      self.get_tokenizer()

						lowerCAmelCase__      :		Tuple    							=      ChineseCLIPProcessor(tokenizer=lowercase_  ,image_processor=lowercase_						)

						lowerCAmelCase__      :		str    							=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCAmelCase__      :		Optional[int]    							=      processor.batch_decode(lowercase_						)
						lowerCAmelCase__      :		int    							=      tokenizer.batch_decode(lowercase_						)

						self.assertListEqual(lowercase_  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		str    							=      self.get_image_processor()
						lowerCAmelCase__      :		Optional[int]    							=      self.get_tokenizer()

						lowerCAmelCase__      :		List[Any]    							=      ChineseCLIPProcessor(tokenizer=lowercase_  ,image_processor=lowercase_						)

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

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

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







"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__UpperCamelCase    : Tuple				 =						logging.get_logger(__name__)

__UpperCamelCase    : Optional[int]				 =						{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}







class 							SCREAMING_SNAKE_CASE   (						a_       ,				a_			):






					"""simple docstring"""



					lowercase__												=			"bit"
					lowercase__												=			["preactivation", "bottleneck"]
					lowercase__												=			["SAME", "VALID"]







					def __init__(			self     :							int  ,lowercase_     :							Optional[int]=3  ,lowercase_     :							int=6_4  ,lowercase_     :							Optional[int]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]  ,lowercase_     :							Union[str, Any]=[3, 4, 6, 3]  ,lowercase_     :							List[Any]="preactivation"  ,lowercase_     :							Dict="relu"  ,lowercase_     :							Dict=None  ,lowercase_     :							Optional[Any]=3_2  ,lowercase_     :							Any=0.0  ,lowercase_     :							Optional[Any]=False  ,lowercase_     :							List[Any]=3_2  ,lowercase_     :							Optional[Any]=1  ,lowercase_     :							Optional[int]=None  ,lowercase_     :							int=None  ,**lowercase_     :							List[str]  ,):
						super().__init__(**lowercase_						)
						if layer_type not in self.layer_types:
							raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
						if global_padding is not None:
							if global_padding.upper() in self.supported_padding:
								lowerCAmelCase__      :		Optional[Any]    							=      global_padding.upper()
							else:
								raise ValueError(F'Padding strategy {global_padding} not supported'						)
						lowerCAmelCase__      :		int    							=      num_channels
						lowerCAmelCase__      :		int    							=      embedding_size
						lowerCAmelCase__      :		Any    							=      hidden_sizes
						lowerCAmelCase__      :		int    							=      depths
						lowerCAmelCase__      :		Union[str, Any]    							=      layer_type
						lowerCAmelCase__      :		Optional[int]    							=      hidden_act
						lowerCAmelCase__      :		int    							=      global_padding
						lowerCAmelCase__      :		List[Any]    							=      num_groups
						lowerCAmelCase__      :		List[Any]    							=      drop_path_rate
						lowerCAmelCase__      :		Union[str, Any]    							=      embedding_dynamic_padding
						lowerCAmelCase__      :		int    							=      output_stride
						lowerCAmelCase__      :		Optional[Any]    							=      width_factor

						lowerCAmelCase__      :		List[str]    							=      ['''stem'''] + [F'stage{idx}' for idx in range(1  ,len(lowercase_						) + 1						)]
						lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      get_aligned_output_features_output_indices(
						    out_features=lowercase_  ,out_indices=lowercase_  ,stage_names=self.stage_names						)




