



"""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,
)


lowerCAmelCase__              =							'''pytorch_model.bin'''
lowerCAmelCase__              =							'''pytorch_model.bin.index.json'''
lowerCAmelCase__              =							'''adapter_config.json'''
lowerCAmelCase__              =							'''adapter_model.bin'''
lowerCAmelCase__              =							'''adapter_model.safetensors'''
lowerCAmelCase__              =							'''tf_model.h5'''
lowerCAmelCase__              =							'''tf_model.h5.index.json'''
lowerCAmelCase__              =							'''model.ckpt'''
lowerCAmelCase__              =							'''flax_model.msgpack'''
lowerCAmelCase__              =							'''flax_model.msgpack.index.json'''
lowerCAmelCase__              =							'''model.safetensors'''
lowerCAmelCase__              =							'''model.safetensors.index.json'''
lowerCAmelCase__              =							'''config.json'''
lowerCAmelCase__              =							'''preprocessor_config.json'''
lowerCAmelCase__              =							FEATURE_EXTRACTOR_NAME
lowerCAmelCase__              =							'''generation_config.json'''
lowerCAmelCase__              =							'''modelcard.json'''

lowerCAmelCase__              =							'''▁'''
lowerCAmelCase__              =							SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

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






def 			snake_case_    ( A_   :						Dict		):


       '''simple docstring'''





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






'''simple docstring'''





import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import BridgeTowerImageProcessor
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			def __init__(  self							:			str			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Dict[str, int] = None			,    lowerCAmelCase_							:			int = 32			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Union[int, float] = 1 / 255			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			str=7			,    lowerCAmelCase_							:			Union[str, Any]=30			,    lowerCAmelCase_							:			str=400			,    lowerCAmelCase_							:			int=3			,    )      ->		str:
						__A=	parent
						__A=	do_resize
						__A=	size if size is not None else {'shortest_edge': 288}
						__A=	size_divisor
						__A=	do_rescale
						__A=	rescale_factor
						__A=	do_normalize
						__A=	do_center_crop
						__A=	image_mean
						__A=	image_std
						__A=	do_pad
						__A=	batch_size
						__A=	num_channels
						__A=	min_resolution
						__A=	max_resolution
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		int:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "size_divisor": self.size_divisor,
						}

			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			str=False	)      ->		str:
						if not batched:
									__A=	self.size['shortest_edge']
									__A=	image_inputs[0]
									if isinstance(lowerCAmelCase_			,    Image.Image	):
												__A,	__A=	image.size
									else:
												__A,	__A=	image.shape[1], image.shape[2]
									__A=	size / min(lowerCAmelCase_			,    lowerCAmelCase_	)
									if h < w:
												__A,	__A=	size, scale * w
									else:
												__A,	__A=	scale * h, size

									__A=	int((1_333 / 800) * size	)
									if max(lowerCAmelCase_			,    lowerCAmelCase_	) > max_size:
												__A=	max_size / max(lowerCAmelCase_			,    lowerCAmelCase_	)
												__A=	newh * scale
												__A=	neww * scale

									__A,	__A=	int(newh + 0.5	), int(neww + 0.5	)
									__A,	__A=	(
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

						else:
									__A=	[]
									for image in image_inputs:
												__A,	__A=	self.get_expected_values([image]	)
												expected_values.append((expected_height, expected_width)	)
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[0]	)[0]
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[1]	)[1]

						return expected_height, expected_width


@require_torch
@require_vision
class 						a__					(					a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				int					      =			BridgeTowerImageProcessor if is_vision_available() else None
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Optional[int]:
						__A=	BridgeTowerImageProcessingTester(self	)
			@property
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Union[str, Any]:
						return self.image_processor_tester.prepare_image_processor_dict()
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		List[Any]:
						__A=	self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_mean'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_std'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_normalize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_resize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size_divisor'	)	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						pass
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    Image.Image	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    numpify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    np.ndarray	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )

			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Tuple:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    torchify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    torch.Tensor	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )



