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    _UpperCamelCase      (						unittest.TestCase ):


			"""simple docstring"""






			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							__lowercase 			=							tempfile.mkdtemp()

							__lowercase 			=							[
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''[PAD]''',
							    '''[MASK]''',
							    '''的''',
							    '''价''',
							    '''格''',
							    '''是''',
							    '''15''',
							    '''便''',
							    '''alex''',
							    '''##andra''',
							    '''，''',
							    '''。''',
							    '''-''',
							    '''t''',
							    '''shirt''',
							]
							__lowercase 			=							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]						)						)

							__lowercase 			=							{
							    '''do_resize''': True,
							    '''size''': {'''height''': 2_24, '''width''': 2_24},
							    '''do_center_crop''': True,
							    '''crop_size''': {'''height''': 18, '''width''': 18},
							    '''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,
							}
							__lowercase 			=							os.path.join(self.tmpdirname      ,   lowerCAmelCase__						)
							with open(self.image_processor_file      ,   '''w'''      ,   encoding='''utf-8'''						) as fp:
											json.dump(lowerCAmelCase__      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					str:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							shutil.rmtree(self.tmpdirname						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							[np.random.randint(2_55      ,   size=(3, 30, 4_00)      ,   dtype=np.uinta						)]

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

							return image_inputs





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							self.get_tokenizer()
							__lowercase 			=							self.get_rust_tokenizer()
							__lowercase 			=							self.get_image_processor()

							__lowercase 			=							ChineseCLIPProcessor(tokenizer=lowerCAmelCase__      ,   image_processor=lowerCAmelCase__						)
							processor_slow.save_pretrained(self.tmpdirname						)
							__lowercase 			=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,   use_fast=lowerCAmelCase__						)

							__lowercase 			=							ChineseCLIPProcessor(tokenizer=lowerCAmelCase__      ,   image_processor=lowerCAmelCase__						)
							processor_fast.save_pretrained(self.tmpdirname						)
							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

							__lowercase 			=							self.get_tokenizer(cls_token='''(CLS)'''      ,   sep_token='''(SEP)'''						)
							__lowercase 			=							self.get_image_processor(do_normalize=lowerCAmelCase__						)

							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							self.get_image_processor()
							__lowercase 			=							self.get_tokenizer()

							__lowercase 			=							ChineseCLIPProcessor(tokenizer=lowerCAmelCase__      ,   image_processor=lowerCAmelCase__						)

							__lowercase 			=							self.prepare_image_inputs()

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

							'''simple docstring'''


							__lowercase 			=							self.get_image_processor()
							__lowercase 			=							self.get_tokenizer()

							__lowercase 			=							ChineseCLIPProcessor(tokenizer=lowerCAmelCase__      ,   image_processor=lowerCAmelCase__						)

							__lowercase 			=							'''Alexandra，T-shirt的价格是15便士。'''

							__lowercase 			=							processor(text=lowerCAmelCase__						)

							__lowercase 			=							tokenizer(lowerCAmelCase__						)

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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							self.get_image_processor()
							__lowercase 			=							self.get_tokenizer()

							__lowercase 			=							ChineseCLIPProcessor(tokenizer=lowerCAmelCase__      ,   image_processor=lowerCAmelCase__						)

							__lowercase 			=							'''Alexandra，T-shirt的价格是15便士。'''
							__lowercase 			=							self.prepare_image_inputs()

							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							__lowercase 			=							self.get_image_processor()
							__lowercase 			=							self.get_tokenizer()

							__lowercase 			=							ChineseCLIPProcessor(tokenizer=lowerCAmelCase__      ,   image_processor=lowerCAmelCase__						)

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

							__lowercase 			=							processor.batch_decode(lowerCAmelCase__						)
							__lowercase 			=							tokenizer.batch_decode(lowerCAmelCase__						)

							self.assertListEqual(lowerCAmelCase__      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


							__lowercase 			=							self.get_image_processor()
							__lowercase 			=							self.get_tokenizer()

							__lowercase 			=							ChineseCLIPProcessor(tokenizer=lowerCAmelCase__      ,   image_processor=lowerCAmelCase__						)

							__lowercase 			=							'''Alexandra，T-shirt的价格是15便士。'''
							__lowercase 			=							self.prepare_image_inputs()

							__lowercase 			=							processor(text=lowerCAmelCase__      ,   images=lowerCAmelCase__						)

							self.assertListEqual(list(inputs.keys()						)      ,   processor.model_input_names						)
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a  :     Optional[int]       =  logging.get_logger(__name__)

__a  :     Tuple       =  {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Dict			 =						'''data2vec-text'''





			def __init__(   self      ,   lowerCAmelCase__=3_05_22      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-12      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__="absolute"      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Any:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__      ,   **lowerCAmelCase__						)

							__lowercase 			=							vocab_size
							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_act
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							type_vocab_size
							__lowercase 			=							initializer_range
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							position_embedding_type
							__lowercase 			=							use_cache
							__lowercase 			=							classifier_dropout




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							if self.task == "multiple-choice":
											__lowercase 			=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
											__lowercase 			=							{0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]						)