


# 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,
)


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

snake_case							:       Tuple     			=      '''▁'''
snake_case							:       Optional[Any]     			=      SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

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






def 							__lowercase   ( __lowerCAmelCase							:				int   ):
							if version.parse(__lowerCAmelCase   ) < version.parse(__lowerCAmelCase   ):
														if "dev" in min_version:
																					a__             =     (
																					    'This example requires a source install from HuggingFace Transformers (see '
																					    '`https://huggingface.co/docs/transformers/installation#install-from-source`),'
																					)
														else:
																					a__             =     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.'   )








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case							:       Union[str, Any]     			=      logging.get_logger(__name__)







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    List[Any]          =			['''pixel_values''']
					def __init__(      self   :Optional[int]			,__snake_case   :bool = True			,__snake_case   :Optional[Dict[str, int]] = None			,__snake_case   :PILImageResampling = PILImageResampling.BICUBIC			,__snake_case   :bool = True			,__snake_case   :bool = True			,__snake_case   :Union[int, float] = 1 / 2_55			,__snake_case   :Dict[str, int] = None			,__snake_case   :bool = True			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,**__snake_case   :Dict			,)					->    None:
												super().__init__(**__snake_case      )
												a__             =     size if size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case      )
												a__             =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case			,default_to_square=__snake_case			,param_name='crop_size'      )

												a__             =     do_resize
												a__             =     do_rescale
												a__             =     do_normalize
												a__             =     do_center_crop
												a__             =     crop_size
												a__             =     size
												a__             =     resample
												a__             =     rescale_factor
												a__             =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												a__             =     image_std if image_std is not None else IMAGENET_DEFAULT_STD
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :PILImageResampling = PILImageResampling.BILINEAR			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :List[Any]			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )

												if "shortest_edge" in size:
																			a__             =     get_resize_output_image_size(__snake_case			,size=size['shortest_edge']			,default_to_square=__snake_case      )
																			# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
												elif "height" in size and "width" in size:
																			a__             =     (size['height'], size['width'])
												else:
																			raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'      )
												return resize(__snake_case			,size=__snake_case			,resample=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )
												if "height" not in size or "width" not in size:
																			raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'      )
												return center_crop(__snake_case			,size=(size['height'], size['width'])			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :np.ndarray			,__snake_case   :float			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :int      )					->    np.ndarray:
												return rescale(__snake_case			,scale=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :np.ndarray			,__snake_case   :Union[float, List[float]]			,__snake_case   :Union[float, List[float]]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												return normalize(__snake_case			,mean=__snake_case			,std=__snake_case			,data_format=__snake_case			,**__snake_case      )



					def  lowerCamelCase__(      self   :Any			,__snake_case   :ImageInput			,__snake_case   :Optional[bool] = None			,__snake_case   :Dict[str, int] = None			,__snake_case   :PILImageResampling = None			,__snake_case   :bool = None			,__snake_case   :int = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[float] = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[str, TensorType]] = None			,__snake_case   :Union[str, ChannelDimension] = ChannelDimension.FIRST			,**__snake_case   :Optional[int]			,)					->    BatchFeature:
												a__             =     do_resize if do_resize is not None else self.do_resize
												a__             =     do_rescale if do_rescale is not None else self.do_rescale
												a__             =     do_normalize if do_normalize is not None else self.do_normalize
												a__             =     do_center_crop if do_center_crop is not None else self.do_center_crop
												a__             =     crop_size if crop_size is not None else self.crop_size
												a__             =     get_size_dict(__snake_case			,param_name='crop_size'			,default_to_square=__snake_case      )
												a__             =     resample if resample is not None else self.resample
												a__             =     rescale_factor if rescale_factor is not None else self.rescale_factor
												a__             =     image_mean if image_mean is not None else self.image_mean
												a__             =     image_std if image_std is not None else self.image_std

												a__             =     size if size is not None else self.size
												a__             =     get_size_dict(__snake_case      )

												if not is_batched(__snake_case      ):
																			a__             =     [images]

												if not valid_images(__snake_case      ):
																			raise ValueError(
																			    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																			    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

												if do_resize and size is None:
																			raise ValueError('Size must be specified if do_resize is True.'      )

												if do_center_crop and crop_size is None:
																			raise ValueError('Crop size must be specified if do_center_crop is True.'      )

												if do_rescale and rescale_factor is None:
																			raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

												# All transformations expect numpy arrays.
												a__             =     [to_numpy_array(__snake_case      ) for image in images]

												if do_resize:
																			a__             =     [self.resize(image=__snake_case			,size=__snake_case			,resample=__snake_case      ) for image in images]

												if do_center_crop:
																			a__             =     [self.center_crop(image=__snake_case			,size=__snake_case      ) for image in images]

												if do_rescale:
																			a__             =     [self.rescale(image=__snake_case			,scale=__snake_case      ) for image in images]

												if do_normalize:
																			a__             =     [self.normalize(image=__snake_case			,mean=__snake_case			,std=__snake_case      ) for image in images]

												a__             =     [to_channel_dimension_format(__snake_case			,__snake_case      ) for image in images]

												a__             =     {'pixel_values': images}
												return BatchFeature(data=__snake_case			,tensor_type=__snake_case      )




