
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPSegProcessor, ViTImageProcessor




@require_vision
class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:str		)	->				int:
								__UpperCamelCase			:     List[Any]         =      tempfile.mkdtemp()

								# fmt: off
								__UpperCamelCase			:     str         =      ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
								# fmt: on
								__UpperCamelCase			:     List[str]         =      dict(zip(a  ,     range(len(a		)		)		)		)
								__UpperCamelCase			:     Dict         =      ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
								__UpperCamelCase			:     Optional[Any]         =      {"unk_token": "<unk>"}

								__UpperCamelCase			:     Dict         =      os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES["vocab_file"]		)
								__UpperCamelCase			:     Any         =      os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES["merges_file"]		)
								with open(self.vocab_file  ,     "w"  ,     encoding="utf-8"		) as fp:
															fp.write(json.dumps(a		) + "\n"		)
								with open(self.merges_file  ,     "w"  ,     encoding="utf-8"		) as fp:
															fp.write("\n".join(a		)		)

								__UpperCamelCase			:     Dict         =      {
								    "do_resize": True,
								    "size": 2_0,
								    "do_center_crop": True,
								    "crop_size": 1_8,
								    "do_normalize": True,
								    "image_mean": [0.48145466, 0.4578275, 0.40821073],
								    "image_std": [0.26862954, 0.26130258, 0.27577711],
								}
								__UpperCamelCase			:     List[str]         =      os.path.join(self.tmpdirname  ,     a		)
								with open(self.image_processor_file  ,     "w"  ,     encoding="utf-8"		) as fp:
															json.dump(a  ,     a		)







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     **a	:Union[str, Any]		)	->				Dict:
								return CLIPTokenizer.from_pretrained(self.tmpdirname  ,     **a		)







	def      _lowerCamelCase       (      self	:Dict  ,     **a	:Optional[int]		)	->				int:
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,     **a		)







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     **a	:int		)	->				List[Any]:
								return ViTImageProcessor.from_pretrained(self.tmpdirname  ,     **a		)







	def      _lowerCamelCase       (      self	:Tuple		)	->				List[Any]:
								shutil.rmtree(self.tmpdirname		)







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				List[str]:
								__UpperCamelCase			:     Optional[Any]         =      [np.random.randint(2_5_5  ,     size=(3, 3_0, 4_0_0)  ,     dtype=np.uinta		)]

								__UpperCamelCase			:     int         =      [Image.fromarray(np.moveaxis(a  ,     0  ,     -1		)		) for x in image_inputs]

								return image_inputs







	def      _lowerCamelCase       (      self	:Dict		)	->				List[str]:
								__UpperCamelCase			:     List[Any]         =      self.get_tokenizer()
								__UpperCamelCase			:     Tuple         =      self.get_rust_tokenizer()
								__UpperCamelCase			:     List[Any]         =      self.get_image_processor()

								__UpperCamelCase			:     List[Any]         =      CLIPSegProcessor(tokenizer=a  ,     image_processor=a		)
								processor_slow.save_pretrained(self.tmpdirname		)
								__UpperCamelCase			:     Optional[Any]         =      CLIPSegProcessor.from_pretrained(self.tmpdirname  ,     use_fast=a		)

								__UpperCamelCase			:     Any         =      CLIPSegProcessor(tokenizer=a  ,     image_processor=a		)
								processor_fast.save_pretrained(self.tmpdirname		)
								__UpperCamelCase			:     Tuple         =      CLIPSegProcessor.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  ,     a		)
								self.assertIsInstance(processor_fast.tokenizer  ,     a		)

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







	def      _lowerCamelCase       (      self	:List[str]		)	->				List[Any]:
								__UpperCamelCase			:     Any         =      CLIPSegProcessor(tokenizer=self.get_tokenizer()  ,     image_processor=self.get_image_processor()		)
								processor.save_pretrained(self.tmpdirname		)

								__UpperCamelCase			:     int         =      self.get_tokenizer(bos_token="(BOS)"  ,     eos_token="(EOS)"		)
								__UpperCamelCase			:     Dict         =      self.get_image_processor(do_normalize=a  ,     padding_value=1.0		)

								__UpperCamelCase			:     str         =      CLIPSegProcessor.from_pretrained(
								    self.tmpdirname  ,     bos_token="(BOS)"  ,     eos_token="(EOS)"  ,     do_normalize=a  ,     padding_value=1.0		)

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

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







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				Union[str, Any]:
								__UpperCamelCase			:     Optional[Any]         =      self.get_image_processor()
								__UpperCamelCase			:     Optional[int]         =      self.get_tokenizer()

								__UpperCamelCase			:     Dict         =      CLIPSegProcessor(tokenizer=a  ,     image_processor=a		)

								__UpperCamelCase			:     str         =      self.prepare_image_inputs()

								__UpperCamelCase			:     Union[str, Any]         =      image_processor(a  ,     return_tensors="np"		)
								__UpperCamelCase			:     Dict         =      processor(images=a  ,     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      _lowerCamelCase       (      self	:Optional[Any]		)	->				int:
								__UpperCamelCase			:     Union[str, Any]         =      self.get_image_processor()
								__UpperCamelCase			:     List[Any]         =      self.get_tokenizer()

								__UpperCamelCase			:     Union[str, Any]         =      CLIPSegProcessor(tokenizer=a  ,     image_processor=a		)

								__UpperCamelCase			:     int         =      "lower newer"

								__UpperCamelCase			:     Tuple         =      processor(text=a		)

								__UpperCamelCase			:     Union[str, Any]         =      tokenizer(a		)

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







	def      _lowerCamelCase       (      self	:Any		)	->				Optional[int]:
								__UpperCamelCase			:     Union[str, Any]         =      self.get_image_processor()
								__UpperCamelCase			:     str         =      self.get_tokenizer()

								__UpperCamelCase			:     int         =      CLIPSegProcessor(tokenizer=a  ,     image_processor=a		)

								__UpperCamelCase			:     str         =      "lower newer"
								__UpperCamelCase			:     Optional[int]         =      self.prepare_image_inputs()

								__UpperCamelCase			:     Dict         =      processor(text=a  ,     images=a		)

								self.assertListEqual(list(inputs.keys()		)  ,     ["input_ids", "attention_mask", "pixel_values"]		)

								# test if it raises when no input is passed
								with pytest.raises(a		):
															processor()







	def      _lowerCamelCase       (      self	:str		)	->				str:
								__UpperCamelCase			:     str         =      self.get_image_processor()
								__UpperCamelCase			:     Dict         =      self.get_tokenizer()

								__UpperCamelCase			:     Dict         =      CLIPSegProcessor(tokenizer=a  ,     image_processor=a		)

								__UpperCamelCase			:     List[str]         =      self.prepare_image_inputs()
								__UpperCamelCase			:     List[Any]         =      self.prepare_image_inputs()

								__UpperCamelCase			:     Union[str, Any]         =      processor(images=a  ,     visual_prompt=a		)

								self.assertListEqual(list(inputs.keys()		)  ,     ["pixel_values", "conditional_pixel_values"]		)

								# test if it raises when no input is passed
								with pytest.raises(a		):
															processor()







	def      _lowerCamelCase       (      self	:Tuple		)	->				List[str]:
								__UpperCamelCase			:     Any         =      self.get_image_processor()
								__UpperCamelCase			:     List[Any]         =      self.get_tokenizer()

								__UpperCamelCase			:     Any         =      CLIPSegProcessor(tokenizer=a  ,     image_processor=a		)

								__UpperCamelCase			:     Dict         =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								__UpperCamelCase			:     Union[str, Any]         =      processor.batch_decode(a		)
								__UpperCamelCase			:     str         =      tokenizer.batch_decode(a		)

								self.assertListEqual(a  ,     a		)

# 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,
)


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

lowercase						:      Optional[int]									= '▁'
lowercase						:      Any									= SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any])      ->					List[str]:






							'''simple docstring'''





							if version.parse(_lowerCamelCase) < version.parse(_lowerCamelCase):
														if "dev" in min_version:
																					__UpperCamelCase			:     List[str]         =      (
																					    "This example requires a source install from HuggingFace Transformers (see "
																					    "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
																					)
														else:
																					__UpperCamelCase			:     Optional[Any]         =      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.")