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 			a__  (					self       ):
            _A=	tempfile.mkdtemp()

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

            _A=	{
                'do_resize': True,
                'size': {'height': 224, 'width': 224},
                'do_center_crop': True,
                'crop_size': {'height': 18, 'width': 18},
                'do_normalize': True,
                'image_mean': [0.48145466, 0.4578275, 0.40821073],
                'image_std': [0.26862954, 0.26130258, 0.27577711],
                'do_convert_rgb': True,
            }
            _A=	os.path.join(self.tmpdirname				,      lowerCAmelCase__       )
            with open(self.image_processor_file				,      'w'				,      encoding='utf-8'       ) as fp:
                   json.dump(lowerCAmelCase__				,      lowerCAmelCase__       )



     def 			a__  (					self				,      **lowerCAmelCase__       ):
            return BertTokenizer.from_pretrained(self.tmpdirname				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      **lowerCAmelCase__       ):
            return BertTokenizerFast.from_pretrained(self.tmpdirname				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      **lowerCAmelCase__       ):
            return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname				,      **lowerCAmelCase__       )



     def 			a__  (					self       ):
            shutil.rmtree(self.tmpdirname       )



     def 			a__  (					self       ):
            _A=	[np.random.randint(255				,      size=(3, 30, 400)				,      dtype=np.uinta       )]

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

            return image_inputs



     def 			a__  (					self       ):
            _A=	self.get_tokenizer()
            _A=	self.get_rust_tokenizer()
            _A=	self.get_image_processor()

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

            _A=	ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,      image_processor=lowerCAmelCase__       )
            processor_fast.save_pretrained(self.tmpdirname       )
            _A=	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 			a__  (					self       ):
            _A=	ChineseCLIPProcessor(tokenizer=self.get_tokenizer()				,      image_processor=self.get_image_processor()       )
            processor.save_pretrained(self.tmpdirname       )

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

            _A=	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 			a__  (					self       ):
            _A=	self.get_image_processor()
            _A=	self.get_tokenizer()

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

            _A=	self.prepare_image_inputs()

            _A=	image_processor(lowerCAmelCase__				,      return_tensors='np'       )
            _A=	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 			a__  (					self       ):
            _A=	self.get_image_processor()
            _A=	self.get_tokenizer()

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

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

            _A=	processor(text=lowerCAmelCase__       )

            _A=	tokenizer(lowerCAmelCase__       )

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



     def 			a__  (					self       ):
            _A=	self.get_image_processor()
            _A=	self.get_tokenizer()

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

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

            _A=	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 			a__  (					self       ):
            _A=	self.get_image_processor()
            _A=	self.get_tokenizer()

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

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

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

            self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )





     def 			a__  (					self       ):
            _A=	self.get_image_processor()
            _A=	self.get_tokenizer()

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

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

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

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

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
      import jax

      from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
      from transformers.models.bert.modeling_flax_bert import FlaxBertModel
      from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel





@require_flax
class      lowerCAmelCase						(					unittest.TestCase			):



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxBertModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxRobertaModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'bert-base is not a local folder and is not a valid model identifier'       ):
                   _A=	FlaxAutoModel.from_pretrained('bert-base'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
                   _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__				,      revision='aaaaaa'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'				,      ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'       )





     def 			a__  (					self       ):
            with self.assertRaisesRegex(lowerCAmelCase__				,      'Use `from_pt=True` to load this model'       ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'       )