





'''simple docstring'''


import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


_SCREAMING_SNAKE_CASE									=				get_logger(__name__)


_SCREAMING_SNAKE_CASE									=				Path(__file__).parent / "model_card_template.md"
_SCREAMING_SNAKE_CASE									=				uuida().hex
_SCREAMING_SNAKE_CASE									=				os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
_SCREAMING_SNAKE_CASE									=				os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
_SCREAMING_SNAKE_CASE									=				HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"


def 						__a(SCREAMING_SNAKE_CASE_	:       Union[Dict, str, None] = None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   F'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
    if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
        return ua + "; telemetry/off"
    if is_torch_available():
        ua += F'''; torch/{_torch_version}'''
    if is_flax_available():
        ua += F'''; jax/{_jax_version}'''
        ua += F'''; flax/{_flax_version}'''
    if is_onnx_available():
        ua += F'''; onnxruntime/{_onnxruntime_version}'''
    # CI will set this value to True
    if os.environ.get("DIFFUSERS_IS_CI"		,							""     ).upper() in ENV_VARS_TRUE_VALUES:
        ua += "; is_ci/true"
    if isinstance(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     ):
        ua += "; " + "; ".join(F'''{k}/{v}''' for k, v in user_agent.items()     )
    elif isinstance(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     ):
        ua += "; " + user_agent
    return ua


def 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       Optional[str] = None		,							SCREAMING_SNAKE_CASE_	:       Optional[str] = None     ):



    '''simple docstring'''





    if token is None:
        _lowerCAmelCase						      =   HfFolder.get_token()
    if organization is None:
        _lowerCAmelCase						      =   whoami(SCREAMING_SNAKE_CASE_     )["name"]
        return F'''{username}/{model_id}'''
    else:
        return F'''{organization}/{model_id}'''


def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    if not is_jinja_available():
        raise ValueError(
            "Modelcard rendering is based on Jinja templates."
            " Please make sure to have `jinja` installed before using `create_model_card`."
            " To install it, please run `pip install Jinja2`."     )

    if hasattr(SCREAMING_SNAKE_CASE_		,							"local_rank"     ) and args.local_rank not in [-1, 0]:
        return

    _lowerCAmelCase						      =   args.hub_token if hasattr(SCREAMING_SNAKE_CASE_		,							"hub_token"     ) else None
    _lowerCAmelCase						      =   get_full_repo_name(SCREAMING_SNAKE_CASE_		,							token=SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   ModelCard.from_template(
        card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
            language="en"		,							license="apache-2.0"		,							library_name="diffusers"		,							tags=[]		,							datasets=args.dataset_name		,							metrics=[]		,							)		,							template_path=SCREAMING_SNAKE_CASE_		,							model_name=SCREAMING_SNAKE_CASE_		,							repo_name=SCREAMING_SNAKE_CASE_		,							dataset_name=args.dataset_name if hasattr(SCREAMING_SNAKE_CASE_		,							"dataset_name"     ) else None		,							learning_rate=args.learning_rate		,							train_batch_size=args.train_batch_size		,							eval_batch_size=args.eval_batch_size		,							gradient_accumulation_steps=(
            args.gradient_accumulation_steps if hasattr(SCREAMING_SNAKE_CASE_		,							"gradient_accumulation_steps"     ) else None
        )		,							adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE_		,							"adam_beta1"     ) else None		,							adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE_		,							"adam_beta2"     ) else None		,							adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE_		,							"adam_weight_decay"     ) else None		,							adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE_		,							"adam_epsilon"     ) else None		,							lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE_		,							"lr_scheduler"     ) else None		,							lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE_		,							"lr_warmup_steps"     ) else None		,							ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE_		,							"ema_inv_gamma"     ) else None		,							ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE_		,							"ema_power"     ) else None		,							ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE_		,							"ema_max_decay"     ) else None		,							mixed_precision=args.mixed_precision		,							)

    _lowerCAmelCase						      =   os.path.join(args.output_dir		,							"README.md"     )
    model_card.save(SCREAMING_SNAKE_CASE_     )


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[str]		,							SCREAMING_SNAKE_CASE_	:       Optional[str] = None     ):



    '''simple docstring'''





    if resolved_file is None or commit_hash is not None:
        return commit_hash
    _lowerCAmelCase						      =   str(Path(SCREAMING_SNAKE_CASE_     ).as_posix()     )
    _lowerCAmelCase						      =   re.search(R"snapshots/([^/]+)/"		,							SCREAMING_SNAKE_CASE_     )
    if search is None:
        return None
    _lowerCAmelCase						      =   search.groups()[0]
    return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE_     ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_SCREAMING_SNAKE_CASE									=				os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_SCREAMING_SNAKE_CASE									=				os.path.join(hf_cache_home, "diffusers")


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[str] = None		,							SCREAMING_SNAKE_CASE_	:       Optional[str] = None     ):



    '''simple docstring'''





    if new_cache_dir is None:
        _lowerCAmelCase						      =   DIFFUSERS_CACHE
    if old_cache_dir is None:
        _lowerCAmelCase						      =   old_diffusers_cache

    _lowerCAmelCase						      =   Path(SCREAMING_SNAKE_CASE_     ).expanduser()
    _lowerCAmelCase						      =   Path(SCREAMING_SNAKE_CASE_     ).expanduser()
    for old_blob_path in old_cache_dir.glob("**/blobs/*"     ):
        if old_blob_path.is_file() and not old_blob_path.is_symlink():
            _lowerCAmelCase						      =   new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE_     )
            new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE_		,							exist_ok=SCREAMING_SNAKE_CASE_     )
            os.replace(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
            try:
                os.symlink(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
            except OSError:
                logger.warning(
                    "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."     )
    # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


_SCREAMING_SNAKE_CASE									=				os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
       _SCREAMING_SNAKE_CASE									=				0
else:
       with open(cache_version_file) as f:
              try:
                     _SCREAMING_SNAKE_CASE									=				int(f.read())
              except ValueError:
                     _SCREAMING_SNAKE_CASE									=				0

if cache_version < 1:
       _SCREAMING_SNAKE_CASE									=				os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
       if old_cache_is_not_empty:
              logger.warning(
                  "The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
                  "existing cached models. This is a one-time operation, you can interrupt it or run it "
                  "later by calling `diffusers.utils.hub_utils.move_cache()`."
              )
              try:
                     move_cache()
              except Exception as e:
                     _SCREAMING_SNAKE_CASE									=				"\n".join(traceback.format_tb(e.__traceback__))
                     logger.error(
                         f'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
                         "file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
                         "message and we will do our best to help."
                     )

if cache_version < 1:
       try:
              os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
              with open(cache_version_file, "w") as f:
                     f.write("1")
       except Exception:
              logger.warning(
                  f'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
                  "the directory exists and can be written to."
              )


def 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       Optional[str] = None     ):



    '''simple docstring'''





    if variant is not None:
        _lowerCAmelCase						      =   weights_name.split("."     )
        _lowerCAmelCase						      =   splits[:-1] + [variant] + splits[-1:]
        _lowerCAmelCase						      =   ".".join(SCREAMING_SNAKE_CASE_     )

    return weights_name





def 						__a(SCREAMING_SNAKE_CASE_	:       List[Any]		,							*,
    SCREAMING_SNAKE_CASE_	:       Dict		,							SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       Optional[int]		,							SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       List[str]=None		,							):



    '''simple docstring'''





    _lowerCAmelCase						      =   str(SCREAMING_SNAKE_CASE_     )
    if os.path.isfile(SCREAMING_SNAKE_CASE_     ):
        return pretrained_model_name_or_path
    elif os.path.isdir(SCREAMING_SNAKE_CASE_     ):
        if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )     ):
            # Load from a PyTorch checkpoint
            _lowerCAmelCase						      =   os.path.join(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
            return model_file
        elif subfolder is not None and os.path.isfile(
            os.path.join(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )     ):
            _lowerCAmelCase						      =   os.path.join(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
            return model_file
        else:
            raise EnvironmentError(
                F'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'''     )
    else:
        # 1. First check if deprecated way of loading from branches is used
        if (
            revision in DEPRECATED_REVISION_ARGS
            and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
            and version.parse(version.parse(SCREAMING_SNAKE_CASE_     ).base_version     ) >= version.parse("0.20.0"     )
        ):
            try:
                _lowerCAmelCase						      =   hf_hub_download(
                    SCREAMING_SNAKE_CASE_		,							filename=_add_variant(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )		,							cache_dir=SCREAMING_SNAKE_CASE_		,							force_download=SCREAMING_SNAKE_CASE_		,							proxies=SCREAMING_SNAKE_CASE_		,							resume_download=SCREAMING_SNAKE_CASE_		,							local_files_only=SCREAMING_SNAKE_CASE_		,							use_auth_token=SCREAMING_SNAKE_CASE_		,							user_agent=SCREAMING_SNAKE_CASE_		,							subfolder=SCREAMING_SNAKE_CASE_		,							revision=revision or commit_hash		,							)
                warnings.warn(
                    F'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.'''		,							SCREAMING_SNAKE_CASE_		,							)
                return model_file
            except:  # noqa: E722
                warnings.warn(
                    F'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )}\' so that the correct variant file can be added.'''		,							SCREAMING_SNAKE_CASE_		,							)
        try:
            # 2. Load model file as usual
            _lowerCAmelCase						      =   hf_hub_download(
                SCREAMING_SNAKE_CASE_		,							filename=SCREAMING_SNAKE_CASE_		,							cache_dir=SCREAMING_SNAKE_CASE_		,							force_download=SCREAMING_SNAKE_CASE_		,							proxies=SCREAMING_SNAKE_CASE_		,							resume_download=SCREAMING_SNAKE_CASE_		,							local_files_only=SCREAMING_SNAKE_CASE_		,							use_auth_token=SCREAMING_SNAKE_CASE_		,							user_agent=SCREAMING_SNAKE_CASE_		,							subfolder=SCREAMING_SNAKE_CASE_		,							revision=revision or commit_hash		,							)
            return model_file

        except RepositoryNotFoundError:
            raise EnvironmentError(
                F'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
                "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
                "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
                "login`."     )
        except RevisionNotFoundError:
            raise EnvironmentError(
                F'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
                "this model name. Check the model page at "
                F'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'''     )
        except EntryNotFoundError:
            raise EnvironmentError(
                F'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'''     )
        except HTTPError as err:
            raise EnvironmentError(
                F'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'''     )
        except ValueError:
            raise EnvironmentError(
                F'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
                F''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
                F''' directory containing a file named {weights_name} or'''
                " \nCheckout your internet connection or see how to run the library in"
                " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."     )
        except EnvironmentError:
            raise EnvironmentError(
                F'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
                "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
                F'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
                F'''containing a file named {weights_name}'''     )









'''simple docstring'''


from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				int								=       ["pixel_values"]




 def __init__(						self      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = PILImageResampling.BICUBIC      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = 1 / 255      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = IMAGENET_DEFAULT_MEAN      ,  _lowerCAmelCase = IMAGENET_DEFAULT_STD      ,  **_lowerCAmelCase      ,  )     ->				None:
     super().__init__(**_lowerCAmelCase		)
     _lowerCAmelCase						      =   size if size is not None else {"shortest_edge": 224}
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     _lowerCAmelCase						      =   crop_size if crop_size is not None else {"height": 224, "width": 224}
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  param_name="crop_size"		)

     _lowerCAmelCase						      =   do_resize
     _lowerCAmelCase						      =   size
     _lowerCAmelCase						      =   resample
     _lowerCAmelCase						      =   do_center_crop
     _lowerCAmelCase						      =   crop_size
     _lowerCAmelCase						      =   do_rescale
     _lowerCAmelCase						      =   rescale_factor
     _lowerCAmelCase						      =   do_normalize
     _lowerCAmelCase						      =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     _lowerCAmelCase						      =   image_std if image_std is not None else IMAGENET_DEFAULT_STD




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = PILImageResampling.BICUBIC      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
     if "shortest_edge" in size:
         _lowerCAmelCase						      =   int((256 / 224) * size["shortest_edge"]		)
         _lowerCAmelCase						      =   get_resize_output_image_size(_lowerCAmelCase      ,  size=_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
         _lowerCAmelCase						      =   {"height": output_size[0], "width": output_size[1]}
     if "height" not in size_dict or "width" not in size_dict:
         raise ValueError(
             f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''		)
     return resize(
         _lowerCAmelCase      ,  size=(size_dict["height"], size_dict["width"])      ,  resample=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase		)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
     return center_crop(_lowerCAmelCase      ,  size=(size["height"], size["width"])      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     return rescale(_lowerCAmelCase      ,  scale=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     return normalize(_lowerCAmelCase      ,  mean=_lowerCAmelCase      ,  std=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = ChannelDimension.FIRST      ,  **_lowerCAmelCase      ,  )     ->				BatchFeature:
     _lowerCAmelCase						      =   do_resize if do_resize is not None else self.do_resize
     _lowerCAmelCase						      =   resample if resample is not None else self.resample
     _lowerCAmelCase						      =   do_center_crop if do_center_crop is not None else self.do_center_crop
     _lowerCAmelCase						      =   do_rescale if do_rescale is not None else self.do_rescale
     _lowerCAmelCase						      =   rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowerCAmelCase						      =   do_normalize if do_normalize is not None else self.do_normalize
     _lowerCAmelCase						      =   image_mean if image_mean is not None else self.image_mean
     _lowerCAmelCase						      =   image_std if image_std is not None else self.image_std

     _lowerCAmelCase						      =   size if size is not None else self.size
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     _lowerCAmelCase						      =   crop_size if crop_size is not None else self.crop_size
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  param_name="crop_size"		)

     _lowerCAmelCase						      =   make_list_of_images(_lowerCAmelCase		)

     if not valid_images(_lowerCAmelCase		):
         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."		)

     if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."		)

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

     if do_resize:
         _lowerCAmelCase						      =   [self.resize(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

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

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

     if do_normalize:
         _lowerCAmelCase						      =   [self.normalize(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     _lowerCAmelCase						      =   [to_channel_dimension_format(_lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     _lowerCAmelCase						      =   {"pixel_values": images}
     return BatchFeature(data=_lowerCAmelCase      ,  tensor_type=_lowerCAmelCase		)


