




# 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__					    =    """pytorch_model.bin"""
UpperCamelCase__					    =    """pytorch_model.bin.index.json"""
UpperCamelCase__					    =    """adapter_config.json"""
UpperCamelCase__					    =    """adapter_model.bin"""
UpperCamelCase__					    =    """adapter_model.safetensors"""
UpperCamelCase__					    =    """tf_model.h5"""
UpperCamelCase__					    =    """tf_model.h5.index.json"""
UpperCamelCase__					    =    """model.ckpt"""
UpperCamelCase__					    =    """flax_model.msgpack"""
UpperCamelCase__					    =    """flax_model.msgpack.index.json"""
UpperCamelCase__					    =    """model.safetensors"""
UpperCamelCase__					    =    """model.safetensors.index.json"""
UpperCamelCase__					    =    """config.json"""
UpperCamelCase__					    =    """preprocessor_config.json"""
UpperCamelCase__					    =    FEATURE_EXTRACTOR_NAME
UpperCamelCase__					    =    """generation_config.json"""
UpperCamelCase__					    =    """modelcard.json"""

UpperCamelCase__					    =    """▁"""
UpperCamelCase__					    =    SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

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







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

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:
					class     a__					:






											@staticmethod
											def        __SCREAMING_SNAKE_CASE(     *_A	,    **_A    ):




																"""simple docstring"""



																pass
@is_pipeline_test
@require_torch
@require_vision
class     a__					(      unittest.TestCase   ):
						_a							:					Any      =					MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="hf-internal-testing/tiny-vilt-random-vqa"    )
											__lowerCAmelCase            = [
											    {
											        "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
											        "question": "How many cats are there?",
											    },
											    {
											        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
											        "question": "How many cats are there?",
											    },
											]
											return vqa_pipeline, examples






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = vqa_pipeline(_A	,    top_k=1    )
											self.assertEqual(
											    _A	,    [
											        [{"score": ANY(_A    ), "answer": ANY(_A    )}],
											        [{"score": ANY(_A    ), "answer": ANY(_A    )}],
											    ]	,    )






						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="hf-internal-testing/tiny-vilt-random-vqa"    )
											__lowerCAmelCase            = "./tests/fixtures/tests_samples/COCO/000000039769.png"
											__lowerCAmelCase            = "How many cats are there?"

											__lowerCAmelCase            = vqa_pipeline(image=_A	,    question="How many cats are there?"	,    top_k=2    )
											self.assertEqual(
											    _A	,    [{"score": ANY(_A    ), "answer": ANY(_A    )}, {"score": ANY(_A    ), "answer": ANY(_A    )}]    )

											__lowerCAmelCase            = vqa_pipeline({"image": image, "question": question}	,    top_k=2    )
											self.assertEqual(
											    _A	,    [{"score": ANY(_A    ), "answer": ANY(_A    )}, {"score": ANY(_A    ), "answer": ANY(_A    )}]    )






						@slow
						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="dandelin/vilt-b32-finetuned-vqa"    )
											__lowerCAmelCase            = "./tests/fixtures/tests_samples/COCO/000000039769.png"
											__lowerCAmelCase            = "How many cats are there?"

											__lowerCAmelCase            = vqa_pipeline(image=_A	,    question=_A	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]    )

											__lowerCAmelCase            = vqa_pipeline({"image": image, "question": question}	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]    )

											__lowerCAmelCase            = vqa_pipeline(
											    [{"image": image, "question": question}, {"image": image, "question": question}]	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [[{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]] * 2	,    )






						@require_tf
						@unittest.skip("Visual question answering not implemented in TF"    )
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											pass




