




'''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     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				tempfile.mkdtemp()

          _snake_case							=				[
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "的",
              "价",
              "格",
              "是",
              "15",
              "便",
              "alex",
              "##andra",
              "，",
              "。",
              "-",
              "t",
              "shirt",
          ]
          _snake_case							=				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]   )   )

          _snake_case							=				{
              "do_resize": True,
              "size": {"height": 224, "width": 224},
              "do_center_crop": True,
              "crop_size": {"height": 18, "width": 18},
              "do_normalize": True,
              "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
              "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
              "do_convert_rgb": True,
          }
          _snake_case							=				os.path.join(self.tmpdirname ,_SCREAMING_SNAKE_CASE   )
          with open(self.image_processor_file ,"w" ,encoding="utf-8"   ) as fp:
               json.dump(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,**_SCREAMING_SNAKE_CASE   )							->      List[Any]:
          return BertTokenizer.from_pretrained(self.tmpdirname ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,**_SCREAMING_SNAKE_CASE   )							->      str:
          return BertTokenizerFast.from_pretrained(self.tmpdirname ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Any:
          shutil.rmtree(self.tmpdirname   )






     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				[np.random.randint(255 ,size=(3, 30, 400) ,dtype=np.uinta   )]

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

          return image_inputs






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				self.get_tokenizer()
          _snake_case							=				self.get_rust_tokenizer()
          _snake_case							=				self.get_image_processor()

          _snake_case							=				ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE   )
          processor_slow.save_pretrained(self.tmpdirname   )
          _snake_case							=				ChineseCLIPProcessor.from_pretrained(self.tmpdirname ,use_fast=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE   )
          processor_fast.save_pretrained(self.tmpdirname   )
          _snake_case							=				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 ,_SCREAMING_SNAKE_CASE   )
          self.assertIsInstance(processor_fast.tokenizer ,_SCREAMING_SNAKE_CASE   )

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






     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				ChineseCLIPProcessor(tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor()   )
          processor.save_pretrained(self.tmpdirname   )

          _snake_case							=				self.get_tokenizer(cls_token="(CLS)" ,sep_token="(SEP)"   )
          _snake_case							=				self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				ChineseCLIPProcessor.from_pretrained(
              self.tmpdirname ,cls_token="(CLS)" ,sep_token="(SEP)" ,do_normalize=_SCREAMING_SNAKE_CASE   )

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

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






     def     _lowercase     (			self   )							->      int:
          _snake_case							=				self.get_image_processor()
          _snake_case							=				self.get_tokenizer()

          _snake_case							=				ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				self.prepare_image_inputs()

          _snake_case							=				image_processor(_SCREAMING_SNAKE_CASE ,return_tensors="np"   )
          _snake_case							=				processor(images=_SCREAMING_SNAKE_CASE ,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:
          _snake_case							=				self.get_image_processor()
          _snake_case							=				self.get_tokenizer()

          _snake_case							=				ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				"Alexandra，T-shirt的价格是15便士。"

          _snake_case							=				processor(text=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				tokenizer(_SCREAMING_SNAKE_CASE   )

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






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				self.get_image_processor()
          _snake_case							=				self.get_tokenizer()

          _snake_case							=				ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				"Alexandra，T-shirt的价格是15便士。"
          _snake_case							=				self.prepare_image_inputs()

          _snake_case							=				processor(text=_SCREAMING_SNAKE_CASE ,images=_SCREAMING_SNAKE_CASE   )

          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(_SCREAMING_SNAKE_CASE   ):
               processor()






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				self.get_image_processor()
          _snake_case							=				self.get_tokenizer()

          _snake_case							=				ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE   )

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

          _snake_case							=				processor.batch_decode(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   )

          self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				self.get_image_processor()
          _snake_case							=				self.get_tokenizer()

          _snake_case							=				ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,image_processor=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				"Alexandra，T-shirt的价格是15便士。"
          _snake_case							=				self.prepare_image_inputs()

          _snake_case							=				processor(text=_SCREAMING_SNAKE_CASE ,images=_SCREAMING_SNAKE_CASE   )

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








'''simple docstring'''





# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


UpperCamelCase_						:      int       =						'''pytorch_model.bin'''
UpperCamelCase_						:      str       =						'''pytorch_model.bin.index.json'''
UpperCamelCase_						:      int       =						'''adapter_config.json'''
UpperCamelCase_						:      str       =						'''adapter_model.bin'''
UpperCamelCase_						:      str       =						'''adapter_model.safetensors'''
UpperCamelCase_						:      List[Any]       =						'''tf_model.h5'''
UpperCamelCase_						:      Union[str, Any]       =						'''tf_model.h5.index.json'''
UpperCamelCase_						:      Tuple       =						'''model.ckpt'''
UpperCamelCase_						:      Union[str, Any]       =						'''flax_model.msgpack'''
UpperCamelCase_						:      Union[str, Any]       =						'''flax_model.msgpack.index.json'''
UpperCamelCase_						:      Dict       =						'''model.safetensors'''
UpperCamelCase_						:      List[Any]       =						'''model.safetensors.index.json'''
UpperCamelCase_						:      Tuple       =						'''config.json'''
UpperCamelCase_						:      List[str]       =						'''preprocessor_config.json'''
UpperCamelCase_						:      List[Any]       =						FEATURE_EXTRACTOR_NAME
UpperCamelCase_						:      Union[str, Any]       =						'''generation_config.json'''
UpperCamelCase_						:      str       =						'''modelcard.json'''

UpperCamelCase_						:      List[Any]       =						'''▁'''
UpperCamelCase_						:      Tuple       =						SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

UpperCamelCase_						:      Any       =						[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
UpperCamelCase_						:      Tuple       =						[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
UpperCamelCase_						:      str       =						[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]





def      __a     (       _UpperCamelCase:   Optional[Any]   )						->			int:

     """simple docstring"""




     if version.parse(_UpperCamelCase   ) < version.parse(_UpperCamelCase   ):
          if "dev" in min_version:
               _snake_case							=				(
                   "This example requires a source install from HuggingFace Transformers (see "
                   "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
               )
          else:
               _snake_case							=				F"""This example requires a minimum version of {min_version},"""
          error_message += F""" but the version found is {__version__}.\n"""
          raise ImportError(
              error_message
              + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
              "versions of HuggingFace Transformers."   )


