






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


lowerCamelCase			:					Optional[Any]         =	get_logger(__name__)


lowerCamelCase			:					Union[str, Any]         =	Path(__file__).parent / '''model_card_template.md'''
lowerCamelCase			:					Any         =	uuida().hex
lowerCamelCase			:					str         =	os.getenv('''HF_HUB_OFFLINE''', '''''').upper() in ENV_VARS_TRUE_VALUES
lowerCamelCase			:					Any         =	os.getenv('''DISABLE_TELEMETRY''', '''''').upper() in ENV_VARS_TRUE_VALUES
lowerCamelCase			:					Optional[Any]         =	HUGGINGFACE_CO_RESOLVE_ENDPOINT + '''/api/telemetry/'''





def       __lowerCAmelCase      (						__snake_case = None       ):
     __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(__snake_case					, __snake_case       ):
          ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items()       )
     elif isinstance(__snake_case					, __snake_case       ):
          ua += "; " + user_agent
     return ua





def       __lowerCAmelCase      (						__snake_case					, __snake_case = None					, __snake_case = None       ):
     if token is None:
          __lowerCAmelCase								=					HfFolder.get_token()
     if organization is None:
          __lowerCAmelCase								=					whoami(__snake_case       )["name"]
          return F"""{username}/{model_id}"""
     else:
          return F"""{organization}/{model_id}"""





def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     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(__snake_case					, "local_rank"       ) and args.local_rank not in [-1, 0]:
          return

     __lowerCAmelCase								=					args.hub_token if hasattr(__snake_case					, "hub_token"       ) else None
     __lowerCAmelCase								=					get_full_repo_name(__snake_case					, token=__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=__snake_case					, model_name=__snake_case					, repo_name=__snake_case					, dataset_name=args.dataset_name if hasattr(__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(__snake_case					, "gradient_accumulation_steps"       ) else None
         )					, adam_betaa=args.adam_betaa if hasattr(__snake_case					, "adam_beta1"       ) else None					, adam_betaa=args.adam_betaa if hasattr(__snake_case					, "adam_beta2"       ) else None					, adam_weight_decay=args.adam_weight_decay if hasattr(__snake_case					, "adam_weight_decay"       ) else None					, adam_epsilon=args.adam_epsilon if hasattr(__snake_case					, "adam_epsilon"       ) else None					, lr_scheduler=args.lr_scheduler if hasattr(__snake_case					, "lr_scheduler"       ) else None					, lr_warmup_steps=args.lr_warmup_steps if hasattr(__snake_case					, "lr_warmup_steps"       ) else None					, ema_inv_gamma=args.ema_inv_gamma if hasattr(__snake_case					, "ema_inv_gamma"       ) else None					, ema_power=args.ema_power if hasattr(__snake_case					, "ema_power"       ) else None					, ema_max_decay=args.ema_max_decay if hasattr(__snake_case					, "ema_max_decay"       ) else None					, mixed_precision=args.mixed_precision					, )

     __lowerCAmelCase								=					os.path.join(args.output_dir					, "README.md"       )
     model_card.save(__snake_case       )





def       __lowerCAmelCase      (						__snake_case					, __snake_case = None       ):
     if resolved_file is None or commit_hash is not None:
          return commit_hash
     __lowerCAmelCase								=					str(Path(__snake_case       ).as_posix()       )
     __lowerCAmelCase								=					re.search(r"snapshots/([^/]+)/"					, __snake_case       )
     if search is None:
          return None
     __lowerCAmelCase								=					search.groups()[0]
     return commit_hash if REGEX_COMMIT_HASH.match(__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.
lowerCamelCase			:					List[str]         =	os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
lowerCamelCase			:					Optional[Any]         =	os.path.join(hf_cache_home, '''diffusers''')





def       __lowerCAmelCase      (						__snake_case = None					, __snake_case = None       ):
     if new_cache_dir is None:
          __lowerCAmelCase								=					DIFFUSERS_CACHE
     if old_cache_dir is None:
          __lowerCAmelCase								=					old_diffusers_cache

     __lowerCAmelCase								=					Path(__snake_case       ).expanduser()
     __lowerCAmelCase								=					Path(__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(__snake_case       )
               new_blob_path.parent.mkdir(parents=__snake_case					, exist_ok=__snake_case       )
               os.replace(__snake_case					, __snake_case       )
               try:
                    os.symlink(__snake_case					, __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).


lowerCamelCase			:					Tuple         =	os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
 lowerCamelCase			:					Dict         =	0
else:
 with open(cache_version_file) as f:
  try:
   lowerCamelCase			:					Union[str, Any]         =	int(f.read())
  except ValueError:
   lowerCamelCase			:					Any         =	0

if cache_version < 1:
 lowerCamelCase			:					int         =	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:
   lowerCamelCase			:					Tuple         =	'''\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       __lowerCAmelCase      (						__snake_case					, __snake_case = None       ):
     if variant is not None:
          __lowerCAmelCase								=					weights_name.split("."       )
          __lowerCAmelCase								=					splits[:-1] + [variant] + splits[-1:]
          __lowerCAmelCase								=					".".join(__snake_case       )

     return weights_name





def       __lowerCAmelCase      (						__snake_case					, *,
    __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case=None					, ):
     __lowerCAmelCase								=					str(__snake_case       )
     if os.path.isfile(__snake_case       ):
          return pretrained_model_name_or_path
     elif os.path.isdir(__snake_case       ):
          if os.path.isfile(os.path.join(__snake_case					, __snake_case       )       ):
               # Load from a PyTorch checkpoint
               __lowerCAmelCase								=					os.path.join(__snake_case					, __snake_case       )
               return model_file
          elif subfolder is not None and os.path.isfile(
              os.path.join(__snake_case					, __snake_case					, __snake_case       )       ):
               __lowerCAmelCase								=					os.path.join(__snake_case					, __snake_case					, __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(__snake_case       ).base_version       ) >= version.parse("0.20.0"       )
          ):
               try:
                    __lowerCAmelCase								=					hf_hub_download(
                        __snake_case					, filename=_add_variant(__snake_case					, __snake_case       )					, cache_dir=__snake_case					, force_download=__snake_case					, proxies=__snake_case					, resume_download=__snake_case					, local_files_only=__snake_case					, use_auth_token=__snake_case					, user_agent=__snake_case					, subfolder=__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."""					, __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(__snake_case					, __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(__snake_case					, __snake_case       )}' so that the correct variant file can be added."""					, __snake_case					, )
          try:
               # 2. Load model file as usual
               __lowerCAmelCase								=					hf_hub_download(
                   __snake_case					, filename=__snake_case					, cache_dir=__snake_case					, force_download=__snake_case					, proxies=__snake_case					, resume_download=__snake_case					, local_files_only=__snake_case					, use_auth_token=__snake_case					, user_agent=__snake_case					, subfolder=__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}"""       )














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


lowerCamelCase			:					Optional[Any]         =	logging.get_logger(__name__)



class 							_UpperCamelCase			(a_			):
      snake_case_  =							["""pixel_values"""]


      def __init__(	self					,						__UpperCamelCase = True					,						__UpperCamelCase = None					,						__UpperCamelCase = PILImageResampling.BICUBIC					,						__UpperCamelCase = True					,						__UpperCamelCase = True					,						__UpperCamelCase = 1 / 2_5_5					,						__UpperCamelCase = None					,						__UpperCamelCase = True					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						**__UpperCamelCase					,						)->	None:
           super().__init__(**__UpperCamelCase	)
           __lowerCAmelCase								=					size if size is not None else {"height": 2_2_4, "width": 2_2_4}
           __lowerCAmelCase								=					get_size_dict(__UpperCamelCase	)
           __lowerCAmelCase								=					crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
           __lowerCAmelCase								=					get_size_dict(__UpperCamelCase					,						default_to_square=__UpperCamelCase					,						param_name="crop_size"	)

           __lowerCAmelCase								=					do_resize
           __lowerCAmelCase								=					do_rescale
           __lowerCAmelCase								=					do_normalize
           __lowerCAmelCase								=					do_center_crop
           __lowerCAmelCase								=					crop_size
           __lowerCAmelCase								=					size
           __lowerCAmelCase								=					resample
           __lowerCAmelCase								=					rescale_factor
           __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  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase = PILImageResampling.BILINEAR					,						__UpperCamelCase = None					,						**__UpperCamelCase					,						)->	np.ndarray:
           __lowerCAmelCase								=					get_size_dict(__UpperCamelCase	)

           if "shortest_edge" in size:
                __lowerCAmelCase								=					get_resize_output_image_size(__UpperCamelCase					,						size=size["shortest_edge"]					,						default_to_square=__UpperCamelCase	)
                # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
           elif "height" in size and "width" in size:
                __lowerCAmelCase								=					(size["height"], size["width"])
           else:
                raise ValueError(F"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""	)
           return resize(__UpperCamelCase					,						size=__UpperCamelCase					,						resample=__UpperCamelCase					,						data_format=__UpperCamelCase					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase = None					,						**__UpperCamelCase					,						)->	np.ndarray:
           __lowerCAmelCase								=					get_size_dict(__UpperCamelCase	)
           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(__UpperCamelCase					,						size=(size["height"], size["width"])					,						data_format=__UpperCamelCase					,						**__UpperCamelCase	)


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


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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = ChannelDimension.FIRST					,						**__UpperCamelCase					,						)->	BatchFeature:
           __lowerCAmelCase								=					do_resize if do_resize is not None else self.do_resize
           __lowerCAmelCase								=					do_rescale if do_rescale is not None else self.do_rescale
           __lowerCAmelCase								=					do_normalize if do_normalize is not None else self.do_normalize
           __lowerCAmelCase								=					do_center_crop if do_center_crop is not None else self.do_center_crop
           __lowerCAmelCase								=					crop_size if crop_size is not None else self.crop_size
           __lowerCAmelCase								=					get_size_dict(__UpperCamelCase					,						param_name="crop_size"					,						default_to_square=__UpperCamelCase	)
           __lowerCAmelCase								=					resample if resample is not None else self.resample
           __lowerCAmelCase								=					rescale_factor if rescale_factor is not None else self.rescale_factor
           __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(__UpperCamelCase	)

           if not is_batched(__UpperCamelCase	):
                __lowerCAmelCase								=					[images]

           if not valid_images(__UpperCamelCase	):
                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.
           __lowerCAmelCase								=					[to_numpy_array(__UpperCamelCase	) for image in images]

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

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

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

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

           __lowerCAmelCase								=					[to_channel_dimension_format(__UpperCamelCase					,						__UpperCamelCase	) for image in images]

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






