
"""simple docstring"""




from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
						import tensorflow as tf

						from transformers import TFBlipTextModel
						from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class 			_a						:






				def __init__(			self		:   str,			lowerCAmelCase__		:   Union[str, Any],			lowerCAmelCase__		:   str=1_2,			lowerCAmelCase__		:   Optional[Any]=7,			lowerCAmelCase__		:   Dict=True,			lowerCAmelCase__		:   Optional[Any]=True,			lowerCAmelCase__		:   int=True,			lowerCAmelCase__		:   int=9_9,			lowerCAmelCase__		:   List[Any]=3_2,			lowerCAmelCase__		:   Any=3_2,			lowerCAmelCase__		:   List[str]=2,			lowerCAmelCase__		:   Dict=4,			lowerCAmelCase__		:   Optional[int]=3_7,			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   List[str]=0.1,			lowerCAmelCase__		:   Any=5_1_2,			lowerCAmelCase__		:   List[Any]=0.02,			lowerCAmelCase__		:   str=0,			lowerCAmelCase__		:   Optional[Any]=None,			)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   parent
										_UpperCamelCase      : str         =   batch_size
										_UpperCamelCase      : Optional[Any]         =   seq_length
										_UpperCamelCase      : Optional[Any]         =   is_training
										_UpperCamelCase      : int         =   use_input_mask
										_UpperCamelCase      : int         =   use_labels
										_UpperCamelCase      : Optional[int]         =   vocab_size
										_UpperCamelCase      : Union[str, Any]         =   hidden_size
										_UpperCamelCase      : Optional[int]         =   projection_dim
										_UpperCamelCase      : List[Any]         =   num_hidden_layers
										_UpperCamelCase      : Optional[int]         =   num_attention_heads
										_UpperCamelCase      : Any         =   intermediate_size
										_UpperCamelCase      : Optional[Any]         =   dropout
										_UpperCamelCase      : Optional[Any]         =   attention_dropout
										_UpperCamelCase      : Optional[Any]         =   max_position_embeddings
										_UpperCamelCase      : List[str]         =   initializer_range
										_UpperCamelCase      : Union[str, Any]         =   scope
										_UpperCamelCase      : int         =   bos_token_id






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




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   ids_tensor([self.batch_size, self.seq_length],			self.vocab_size							)

										_UpperCamelCase      : Optional[Any]         =   None
										if self.use_input_mask:
																_UpperCamelCase      : Optional[Any]         =   random_attention_mask([self.batch_size, self.seq_length]							)

										if input_mask is not None:
																_UpperCamelCase      : Union[str, Any]         =   input_mask.numpy()
																_UpperCamelCase      : Optional[int]         =   input_mask.shape
																_UpperCamelCase      : Optional[int]         =   np.random.randint(1,			seq_length - 1,			size=(batch_size,)							)
																for batch_idx, start_index in enumerate(a_							):
																						_UpperCamelCase      : Any         =   1
																						_UpperCamelCase      : str         =   0

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

										return config, input_ids, tf.convert_to_tensor(a_							)






				def     snake_case     (			self		:   Dict							)     ->				str:




										'''simple docstring'''



										return BlipTextConfig(
										    vocab_size=self.vocab_size,			hidden_size=self.hidden_size,			projection_dim=self.projection_dim,			num_hidden_layers=self.num_hidden_layers,			num_attention_heads=self.num_attention_heads,			intermediate_size=self.intermediate_size,			dropout=self.dropout,			attention_dropout=self.attention_dropout,			max_position_embeddings=self.max_position_embeddings,			initializer_range=self.initializer_range,			bos_token_id=self.bos_token_id,			)






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




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   TFBlipTextModel(config=a_							)
										_UpperCamelCase      : int         =   model(a_,			attention_mask=a_,			training=a_							)
										_UpperCamelCase      : Optional[int]         =   model(a_,			training=a_							)
										self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)							)
										self.parent.assertEqual(result.pooler_output.shape,			(self.batch_size, self.hidden_size)							)






				def     snake_case     (			self		:   Tuple							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   self.prepare_config_and_inputs()
										_UpperCamelCase      : List[Any]         =   config_and_inputs
										_UpperCamelCase      : List[Any]         =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict




@require_tf
class 			_a						(				__snake_case     ,    unittest.TestCase    ):
				UpperCamelCase								=   (TFBlipTextModel,) if is_tf_available() else ()
				UpperCamelCase								=   False
				UpperCamelCase								=   False
				UpperCamelCase								=   False






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




										'''simple docstring'''



										_UpperCamelCase      : str         =   BlipTextModelTester(self							)
										_UpperCamelCase      : Dict         =   ConfigTester(self,			config_class=a_,			hidden_size=3_7							)






				def     snake_case     (			self		:   int							)     ->				Dict:




										'''simple docstring'''



										self.config_tester.run_common_tests()






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




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*a_							)






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




										'''simple docstring'''



										pass






				def     snake_case     (			self		:   List[Any]							)     ->				str:




										'''simple docstring'''



										pass






				@unittest.skip(reason='''Blip does not use inputs_embeds'''							)
				def     snake_case     (			self		:   Tuple							)     ->				str:




										'''simple docstring'''



										pass






				@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''							)
				def     snake_case     (			self		:   List[str]							)     ->				Dict:




										'''simple docstring'''



										pass






				@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''							)
				def     snake_case     (			self		:   Dict							)     ->				Any:




										'''simple docstring'''



										pass






				@slow
				def     snake_case     (			self		:   Any							)     ->				int:




										'''simple docstring'''



										for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase      : Tuple         =   TFBlipTextModel.from_pretrained(a_							)
																self.assertIsNotNone(a_							)






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




										'''simple docstring'''



										super().test_pt_tf_model_equivalence(allow_missing_keys=a_							)










"""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							)



