



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


UpperCAmelCase__   =       get_logger(__name__)


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





def 							A	(     _UpperCAmelCase				:   List[Any] = None    )				-> str:







			'''simple docstring'''
			_UpperCAmelCase		       =   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	(     _UpperCAmelCase				:   List[str]							,   _UpperCAmelCase				:   Union[str, Any] = None							,   _UpperCAmelCase				:   int = None    )				-> Dict:







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





def 							A	(     _UpperCAmelCase				:   Tuple							,   _UpperCAmelCase				:   List[Any]    )				-> List[Any]:







			'''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

			_UpperCAmelCase		       =   args.hub_token if hasattr(SCREAMING_SNAKE_CASE__							,   'hub_token'    ) else None
			_UpperCAmelCase		       =   get_full_repo_name(SCREAMING_SNAKE_CASE__							,   token=SCREAMING_SNAKE_CASE__    )

			_UpperCAmelCase		       =   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							,   )

			_UpperCAmelCase		       =   os.path.join(args.output_dir							,   'README.md'    )
			model_card.save(SCREAMING_SNAKE_CASE__    )





def 							A	(     _UpperCAmelCase				:   List[str]							,   _UpperCAmelCase				:   str = None    )				-> Tuple:







			'''simple docstring'''
			if resolved_file is None or commit_hash is not None:
						return commit_hash
			_UpperCAmelCase		       =   str(Path(SCREAMING_SNAKE_CASE__    ).as_posix()    )
			_UpperCAmelCase		       =   re.search(R'snapshots/([^/]+)/'							,   SCREAMING_SNAKE_CASE__    )
			if search is None:
						return None
			_UpperCAmelCase		       =   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.
UpperCAmelCase__   =       os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
UpperCAmelCase__   =       os.path.join(hf_cache_home, "diffusers")





def 							A	(     _UpperCAmelCase				:   List[str] = None							,   _UpperCAmelCase				:   Any = None    )				-> None:







			'''simple docstring'''
			if new_cache_dir is None:
						_UpperCAmelCase		       =   DIFFUSERS_CACHE
			if old_cache_dir is None:
						_UpperCAmelCase		       =   old_diffusers_cache

			_UpperCAmelCase		       =   Path(SCREAMING_SNAKE_CASE__    ).expanduser()
			_UpperCAmelCase		       =   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():
									_UpperCAmelCase		       =   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).


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

if cache_version < 1:
							UpperCAmelCase__   =       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:
																					UpperCAmelCase__   =       """\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	(     _UpperCAmelCase				:   Optional[int]							,   _UpperCAmelCase				:   str = None    )				-> str:







			'''simple docstring'''
			if variant is not None:
						_UpperCAmelCase		       =   weights_name.split('.'    )
						_UpperCAmelCase		       =   splits[:-1] + [variant] + splits[-1:]
						_UpperCAmelCase		       =   """.""".join(SCREAMING_SNAKE_CASE__    )

			return weights_name





def 							A	(     _UpperCAmelCase				:   Union[str, Any]							,   *,
    _UpperCAmelCase				:   Union[str, Any]							,   _UpperCAmelCase				:   Optional[Any]							,   _UpperCAmelCase				:   List[Any]							,   _UpperCAmelCase				:   Any							,   _UpperCAmelCase				:   List[str]							,   _UpperCAmelCase				:   Optional[Any]							,   _UpperCAmelCase				:   Tuple							,   _UpperCAmelCase				:   List[Any]							,   _UpperCAmelCase				:   List[Any]							,   _UpperCAmelCase				:   Optional[int]							,   _UpperCAmelCase				:   Union[str, Any]=None							,   )				-> Any:







			'''simple docstring'''
			_UpperCAmelCase		       =   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
									_UpperCAmelCase		       =   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__    )    ):
									_UpperCAmelCase		       =   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:
												_UpperCAmelCase		       =   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
									_UpperCAmelCase		       =   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}"    )


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_tf_available():
  import tensorflow as tf

  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  from ..tf_utils import stable_softmax

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowercase  :					Any						    =							logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           super().__init__(*snake_case		,**snake_case      )
           requires_backends(self		,"""vision"""      )
           self.check_model_type(
               TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
               if self.framework == """tf"""
               else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      {}
           if top_k is not None:
                  lowercase							:     int       	=      top_k
           return {}, {}, postprocess_params
    def __call__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      load_image(snake_case      )
           lowercase							:     List[Any]       	=      self.image_processor(images=snake_case		,return_tensors=self.framework      )
           return model_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.model(**snake_case      )
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=5      ):



           '''simple docstring'''







           if top_k > self.model.config.num_labels:
                  lowercase							:     Tuple       	=      self.model.config.num_labels

           if self.framework == "pt":
                  lowercase							:     str       	=      model_outputs.logits.softmax(-1      )[0]
                  lowercase	,			lowercase							:     Dict       	=      probs.topk(snake_case      )
           elif self.framework == "tf":
                  lowercase							:     Optional[int]       	=      stable_softmax(model_outputs.logits		,axis=-1      )[0]
                  lowercase							:     Union[str, Any]       	=      tf.math.top_k(snake_case		,k=snake_case      )
                  lowercase	,			lowercase							:     List[str]       	=      topk.values.numpy(), topk.indices.numpy()
           else:
                  raise ValueError(f"Unsupported framework: {self.framework}"      )

           lowercase							:     Tuple       	=      scores.tolist()
           lowercase							:     Dict       	=      ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case		,snake_case      )]
