






# 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,
)


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

__magic_name__:					Optional[int]							=     "▁"
__magic_name__:					str							=     SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

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

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







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








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 OwlViTImageProcessor, OwlViTProcessor





@require_vision
class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       List[str]       						= tempfile.mkdtemp()

							# fmt: off
							__magic_name__      :       Union[str, Any]       						= ["""""", """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
							__magic_name__      :       Any       						= dict(zip(lowerCAmelCase__      ,       range(len(lowerCAmelCase__			)			)			)			)
							__magic_name__      :       int       						= ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
							__magic_name__      :       Any       						= {"""unk_token""": """<unk>"""}

							__magic_name__      :       Optional[Any]       						= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["""vocab_file"""]			)
							__magic_name__      :       Optional[int]       						= 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(lowerCAmelCase__			) + """\n"""			)
							with open(self.merges_file      ,       """w"""      ,       encoding="""utf-8"""			) as fp:
													fp.write("""\n""".join(lowerCAmelCase__			)			)

							__magic_name__      :       int       						= {
							    """do_resize""": True,
							    """size""": 20,
							    """do_center_crop""": True,
							    """crop_size""": 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],
							}
							__magic_name__      :       List[str]       						= os.path.join(self.tmpdirname      ,       lowerCAmelCase__			)
							with open(self.image_processor_file      ,       """w"""      ,       encoding="""utf-8"""			) as fp:
													json.dump(lowerCAmelCase__      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				List[str]:
							return CLIPTokenizer.from_pretrained(self.tmpdirname      ,       pad_token="""!"""      ,       **lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Any:
							return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,       pad_token="""!"""      ,       **lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Optional[Any]:
							return OwlViTImageProcessor.from_pretrained(self.tmpdirname      ,       **lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							shutil.rmtree(self.tmpdirname			)
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       str       						= [np.random.randint(2_55      ,       size=(3, 30, 4_00)      ,       dtype=np.uinta			)]

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

							return image_inputs
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Any       						= self.get_tokenizer()
							__magic_name__      :       str       						= self.get_rust_tokenizer()
							__magic_name__      :       Tuple       						= self.get_image_processor()

							__magic_name__      :       List[str]       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)
							processor_slow.save_pretrained(self.tmpdirname			)
							__magic_name__      :       Optional[int]       						= OwlViTProcessor.from_pretrained(self.tmpdirname      ,       use_fast=lowerCAmelCase__			)

							__magic_name__      :       int       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)
							processor_fast.save_pretrained(self.tmpdirname			)
							__magic_name__      :       List[Any]       						= OwlViTProcessor.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       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       Optional[Any]       						= OwlViTProcessor(tokenizer=self.get_tokenizer()      ,       image_processor=self.get_image_processor()			)
							processor.save_pretrained(self.tmpdirname			)

							__magic_name__      :       str       						= self.get_tokenizer(bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""			)
							__magic_name__      :       Any       						= self.get_image_processor(do_normalize=lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= OwlViTProcessor.from_pretrained(
							    self.tmpdirname      ,       bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""      ,       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       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       int       						= self.get_image_processor()
							__magic_name__      :       int       						= self.get_tokenizer()

							__magic_name__      :       Union[str, Any]       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)

							__magic_name__      :       Dict       						= self.prepare_image_inputs()

							__magic_name__      :       Any       						= image_processor(lowerCAmelCase__      ,       return_tensors="""np"""			)
							__magic_name__      :       str       						= processor(images=lowerCAmelCase__      ,       return_tensors="""np"""			)

							for key in input_image_proc.keys():
													self.assertAlmostEqual(input_image_proc[key].sum()      ,       input_processor[key].sum()      ,       delta=1e-2			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Union[str, Any]       						= self.get_image_processor()
							__magic_name__      :       int       						= self.get_tokenizer()

							__magic_name__      :       int       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= """lower newer"""

							__magic_name__      :       Tuple       						= processor(text=lowerCAmelCase__      ,       return_tensors="""np"""			)

							__magic_name__      :       Optional[int]       						= tokenizer(lowerCAmelCase__      ,       return_tensors="""np"""			)

							for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key][0].tolist()      ,       encoded_processor[key][0].tolist()			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Tuple       						= self.get_image_processor()
							__magic_name__      :       Union[str, Any]       						= self.get_tokenizer()

							__magic_name__      :       List[str]       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)

							__magic_name__      :       Any       						= """lower newer"""
							__magic_name__      :       Union[str, Any]       						= self.prepare_image_inputs()

							__magic_name__      :       int       						= processor(text=lowerCAmelCase__      ,       images=lowerCAmelCase__			)

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

							# test if it raises when no input is passed
							with pytest.raises(lowerCAmelCase__			):
													processor()
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Dict       						= """google/owlvit-base-patch32"""
							__magic_name__      :       int       						= OwlViTProcessor.from_pretrained(lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= ["""cat""", """nasa badge"""]
							__magic_name__      :       Any       						= processor(text=lowerCAmelCase__			)

							__magic_name__      :       Dict       						= 16
							self.assertListEqual(list(inputs.keys()			)      ,       ["""input_ids""", """attention_mask"""]			)
							self.assertEqual(inputs["""input_ids"""].shape      ,       (2, seq_length)			)

							# test if it raises when no input is passed
							with pytest.raises(lowerCAmelCase__			):
													processor()
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       List[str]       						= """google/owlvit-base-patch32"""
							__magic_name__      :       Optional[Any]       						= OwlViTProcessor.from_pretrained(lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= [["""cat""", """nasa badge"""], ["""person"""]]
							__magic_name__      :       Tuple       						= processor(text=lowerCAmelCase__			)

							__magic_name__      :       str       						= 16
							__magic_name__      :       str       						= len(lowerCAmelCase__			)
							__magic_name__      :       int       						= max([len(lowerCAmelCase__			) for texts in input_texts]			)

							self.assertListEqual(list(inputs.keys()			)      ,       ["""input_ids""", """attention_mask"""]			)
							self.assertEqual(inputs["""input_ids"""].shape      ,       (batch_size * num_max_text_queries, seq_length)			)

							# test if it raises when no input is passed
							with pytest.raises(lowerCAmelCase__			):
													processor()
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Optional[int]       						= """google/owlvit-base-patch32"""
							__magic_name__      :       Any       						= OwlViTProcessor.from_pretrained(lowerCAmelCase__			)

							__magic_name__      :       str       						= ["""cat""", """nasa badge"""]
							__magic_name__      :       List[str]       						= processor(text=lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= 16
							__magic_name__      :       Any       						= inputs["""input_ids"""]
							__magic_name__      :       Optional[Any]       						= [
							    [4_94_06, 23_68, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
							    [4_94_06, 68_41, 1_13_01, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
							]

							self.assertListEqual(list(inputs.keys()			)      ,       ["""input_ids""", """attention_mask"""]			)
							self.assertEqual(inputs["""input_ids"""].shape      ,       (2, seq_length)			)
							self.assertListEqual(list(input_ids[0]			)      ,       predicted_ids[0]			)
							self.assertListEqual(list(input_ids[1]			)      ,       predicted_ids[1]			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       List[str]       						= self.get_image_processor()
							__magic_name__      :       Dict       						= self.get_tokenizer()

							__magic_name__      :       Tuple       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= self.prepare_image_inputs()
							__magic_name__      :       List[Any]       						= self.prepare_image_inputs()

							__magic_name__      :       List[str]       						= processor(images=lowerCAmelCase__      ,       query_images=lowerCAmelCase__			)

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

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







	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Optional[Any]       						= self.get_image_processor()
							__magic_name__      :       List[Any]       						= self.get_tokenizer()

							__magic_name__      :       Tuple       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							__magic_name__      :       Optional[Any]       						= processor.batch_decode(lowerCAmelCase__			)
							__magic_name__      :       Optional[int]       						= tokenizer.batch_decode(lowerCAmelCase__			)

							self.assertListEqual(lowerCAmelCase__      ,       lowerCAmelCase__			)
