



'''simple docstring'''



import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.roberta.modeling_flax_roberta import (
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaModel,
       )
class 	lowerCamelCase_							(					unittest.TestCase			):







      def __init__(  self   :      Tuple				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Dict=13				,    lowerCAmelCase__   :      int=7				,    lowerCAmelCase__   :      List[Any]=True				,    lowerCAmelCase__   :      Tuple=True				,    lowerCAmelCase__   :      Optional[int]=True				,    lowerCAmelCase__   :      str=True				,    lowerCAmelCase__   :      Union[str, Any]=99				,    lowerCAmelCase__   :      Dict=32				,    lowerCAmelCase__   :      str=5				,    lowerCAmelCase__   :      int=4				,    lowerCAmelCase__   :      str=37				,    lowerCAmelCase__   :      Dict="gelu"				,    lowerCAmelCase__   :      int=0.1				,    lowerCAmelCase__   :      str=0.1				,    lowerCAmelCase__   :      Any=5_12				,    lowerCAmelCase__   :      List[Any]=16				,    lowerCAmelCase__   :      Tuple=2				,    lowerCAmelCase__   :      Optional[Any]=0.02				,    lowerCAmelCase__   :      Optional[Any]=4				,    ):

             """simple docstring"""
             SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			parent
             SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			batch_size
             SCREAMING_SNAKE_CASE					:      int     					=			seq_length
             SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			is_training
             SCREAMING_SNAKE_CASE					:      Any     					=			use_attention_mask
             SCREAMING_SNAKE_CASE					:      Optional[int]     					=			use_token_type_ids
             SCREAMING_SNAKE_CASE					:      str     					=			use_labels
             SCREAMING_SNAKE_CASE					:      Optional[int]     					=			vocab_size
             SCREAMING_SNAKE_CASE					:      Any     					=			hidden_size
             SCREAMING_SNAKE_CASE					:      Any     					=			num_hidden_layers
             SCREAMING_SNAKE_CASE					:      Any     					=			num_attention_heads
             SCREAMING_SNAKE_CASE					:      str     					=			intermediate_size
             SCREAMING_SNAKE_CASE					:      Any     					=			hidden_act
             SCREAMING_SNAKE_CASE					:      List[Any]     					=			hidden_dropout_prob
             SCREAMING_SNAKE_CASE					:      int     					=			attention_probs_dropout_prob
             SCREAMING_SNAKE_CASE					:      Dict     					=			max_position_embeddings
             SCREAMING_SNAKE_CASE					:      Dict     					=			type_vocab_size
             SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			type_sequence_label_size
             SCREAMING_SNAKE_CASE					:      str     					=			initializer_range
             SCREAMING_SNAKE_CASE					:      Any     					=			num_choices







      def 					__lowercase    (  self   :      Optional[Any]				):

             """simple docstring"""
             SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)

             SCREAMING_SNAKE_CASE					:      List[Any]     					=			None
             if self.use_attention_mask:
                    SCREAMING_SNAKE_CASE					:      Dict     					=			random_attention_mask([self.batch_size, self.seq_length]				)

             SCREAMING_SNAKE_CASE					:      List[Any]     					=			None
             if self.use_token_type_ids:
                    SCREAMING_SNAKE_CASE					:      Tuple     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.type_vocab_size				)

             SCREAMING_SNAKE_CASE					:      List[str]     					=			RobertaConfig(
                 vocab_size=self.vocab_size				,    hidden_size=self.hidden_size				,    num_hidden_layers=self.num_hidden_layers				,    num_attention_heads=self.num_attention_heads				,    intermediate_size=self.intermediate_size				,    hidden_act=self.hidden_act				,    hidden_dropout_prob=self.hidden_dropout_prob				,    attention_probs_dropout_prob=self.attention_probs_dropout_prob				,    max_position_embeddings=self.max_position_embeddings				,    type_vocab_size=self.type_vocab_size				,    is_decoder=lowerCAmelCase__				,    initializer_range=self.initializer_range				,    )

             return config, input_ids, token_type_ids, attention_mask







      def 					__lowercase    (  self   :      List[str]				):

             """simple docstring"""
             SCREAMING_SNAKE_CASE					:      str     					=			self.prepare_config_and_inputs()
             SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			config_and_inputs
             SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
             return config, inputs_dict







      def 					__lowercase    (  self   :      str				):

             """simple docstring"""
             SCREAMING_SNAKE_CASE					:      List[str]     					=			self.prepare_config_and_inputs()
             SCREAMING_SNAKE_CASE					:      int     					=			config_and_inputs

             SCREAMING_SNAKE_CASE					:      str     					=			True
             SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
             SCREAMING_SNAKE_CASE					:      int     					=			ids_tensor([self.batch_size, self.seq_length]				,    vocab_size=2				)

             return (
                 config,
                 input_ids,
                 token_type_ids,
                 encoder_hidden_states,
                 encoder_attention_mask,
             )


@require_flax
class 	lowerCamelCase_							(					snake_case_    ,				unittest.TestCase			):
      _lowerCAmelCase     :   Optional[int]	     =				True

      _lowerCAmelCase     :   Dict	     =				(
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )







      def 					__lowercase    (  self   :      int				):

             """simple docstring"""
             SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			FlaxRobertaModelTester(self				)







      @slow
      def 					__lowercase    (  self   :      List[str]				):

             """simple docstring"""
             for model_class_name in self.all_model_classes:
                    SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model_class_name.from_pretrained('''roberta-base'''				,    from_pt=lowerCAmelCase__				)
                    SCREAMING_SNAKE_CASE					:      Any     					=			model(np.ones((1, 1)				)				)
                    self.assertIsNotNone(lowerCAmelCase__				)









'''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 	lowerCamelCase_							(					unittest.TestCase			):







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tempfile.mkdtemp()

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

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			{
													    '''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,
													}
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			os.path.join(self.tmpdirname				,    lowerCAmelCase__				)
													with open(self.image_processor_file				,    '''w'''				,    encoding='''utf-8'''				) as fp:
																				json.dump(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				,    **lowerCAmelCase__   :      int				):

													"""simple docstring"""
													return BertTokenizer.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return BertTokenizerFast.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													shutil.rmtree(self.tmpdirname				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			[np.random.randint(2_55				,    size=(3, 30, 4_00)				,    dtype=np.uinta				)]

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

													return image_inputs







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_image_processor()

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

													SCREAMING_SNAKE_CASE					:      int     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)
													processor_fast.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      Union[str, 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 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ChineseCLIPProcessor(tokenizer=self.get_tokenizer()				,    image_processor=self.get_image_processor()				)
													processor.save_pretrained(self.tmpdirname				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_tokenizer(cls_token='''(CLS)'''				,    sep_token='''(SEP)'''				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_image_processor(do_normalize=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, 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 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_tokenizer()

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

													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_processor(lowerCAmelCase__				,    return_tensors='''np'''				)
													SCREAMING_SNAKE_CASE					:      List[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 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_tokenizer()

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

													SCREAMING_SNAKE_CASE					:      int     					=			'''Alexandra，T-shirt的价格是15便士。'''

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			processor(text=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer(lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer()

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

													SCREAMING_SNAKE_CASE					:      str     					=			'''Alexandra，T-shirt的价格是15便士。'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      List[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 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer()

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

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

													SCREAMING_SNAKE_CASE					:      Any     					=			processor.batch_decode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			tokenizer.batch_decode(lowerCAmelCase__				)

													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()

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

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

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

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




