




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


_snake_case										=       get_logger(__name__)


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







def 				lowerCAmelCase_	(     snake_case_ = None     ):
      _A							:     List[Any]			=						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:
            _A							:     Union[str, Any]			=						HfFolder.get_token()
      if organization is None:
            _A							:     Any			=						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

      _A							:     Any			=						args.hub_token if hasattr(snake_case_,"""hub_token"""     ) else None
      _A							:     int			=						get_full_repo_name(snake_case_,token=snake_case_     )

      _A							:     Optional[Any]			=						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,)

      _A							:     int			=						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
      _A							:     int			=						str(Path(snake_case_     ).as_posix()     )
      _A							:     List[Any]			=						re.search(r"""snapshots/([^/]+)/""",snake_case_     )
      if search is None:
            return None
      _A							:     int			=						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.
_snake_case										=       os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_snake_case										=       os.path.join(hf_cache_home, "diffusers")







def 				lowerCAmelCase_	(     snake_case_ = None,snake_case_ = None     ):
      if new_cache_dir is None:
            _A							:     Union[str, Any]			=						DIFFUSERS_CACHE
      if old_cache_dir is None:
            _A							:     str			=						old_diffusers_cache

      _A							:     Any			=						Path(snake_case_     ).expanduser()
      _A							:     Tuple			=						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():
                  _A							:     Union[str, Any]			=						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).


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

if cache_version < 1:
  _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:
      _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 				lowerCAmelCase_	(     snake_case_,snake_case_ = None     ):
      if variant is not None:
            _A							:     List[str]			=						weights_name.split("""."""     )
            _A							:     Dict			=						splits[:-1] + [variant] + splits[-1:]
            _A							:     Any			=						""".""".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,):
      _A							:     Tuple			=						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
                  _A							:     Tuple			=						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_     )     ):
                  _A							:     List[Any]			=						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:
                        _A							:     List[Any]			=						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
                  _A							:     Dict			=						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}'''     )



def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					def update_area_of_max_square(UpperCamelCase_	,	UpperCamelCase_		) -> int:
										# BASE CASE
										if row >= rows or col >= cols:
															return 0

										SCREAMING_SNAKE_CASE__													= update_area_of_max_square(UpperCamelCase_	,	col + 1		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square(row + 1	,	col + 1		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square(row + 1	,	UpperCamelCase_		)

										if mat[row][col]:
															SCREAMING_SNAKE_CASE__													= 1 + min([right, diagonal, down]		)
															SCREAMING_SNAKE_CASE__													= max(largest_square_area[0]	,	UpperCamelCase_		)
															return sub_problem_sol
										else:
															return 0

					SCREAMING_SNAKE_CASE__													= [0]
					update_area_of_max_square(0	,	0		)
					return largest_square_area[0]






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					def update_area_of_max_square_using_dp_array(
					    UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		) -> int:
										if row >= rows or col >= cols:
															return 0
										if dp_array[row][col] != -1:
															return dp_array[row][col]

										SCREAMING_SNAKE_CASE__													= update_area_of_max_square_using_dp_array(UpperCamelCase_	,	col + 1	,	UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square_using_dp_array(row + 1	,	col + 1	,	UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square_using_dp_array(row + 1	,	UpperCamelCase_	,	UpperCamelCase_		)

										if mat[row][col]:
															SCREAMING_SNAKE_CASE__													= 1 + min([right, diagonal, down]		)
															SCREAMING_SNAKE_CASE__													= max(largest_square_area[0]	,	UpperCamelCase_		)
															SCREAMING_SNAKE_CASE__													= sub_problem_sol
															return sub_problem_sol
										else:
															return 0

					SCREAMING_SNAKE_CASE__													= [0]
					SCREAMING_SNAKE_CASE__													= [[-1] * cols for _ in range(UpperCamelCase_		)]
					update_area_of_max_square_using_dp_array(0	,	0	,	UpperCamelCase_		)

					return largest_square_area[0]






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= [[0] * (cols + 1) for _ in range(rows + 1		)]
					SCREAMING_SNAKE_CASE__													= 0
					for row in range(rows - 1	,	-1	,	-1		):
										for col in range(cols - 1	,	-1	,	-1		):
															SCREAMING_SNAKE_CASE__													= dp_array[row][col + 1]
															SCREAMING_SNAKE_CASE__													= dp_array[row + 1][col + 1]
															SCREAMING_SNAKE_CASE__													= dp_array[row + 1][col]

															if mat[row][col] == 1:
																				SCREAMING_SNAKE_CASE__													= 1 + min(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)
																				SCREAMING_SNAKE_CASE__													= max(dp_array[row][col]	,	UpperCamelCase_		)
															else:
																				SCREAMING_SNAKE_CASE__													= 0

					return largest_square_area






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= [0] * (cols + 1)
					SCREAMING_SNAKE_CASE__													= [0] * (cols + 1)
					SCREAMING_SNAKE_CASE__													= 0
					for row in range(rows - 1	,	-1	,	-1		):
										for col in range(cols - 1	,	-1	,	-1		):
															SCREAMING_SNAKE_CASE__													= current_row[col + 1]
															SCREAMING_SNAKE_CASE__													= next_row[col + 1]
															SCREAMING_SNAKE_CASE__													= next_row[col]

															if mat[row][col] == 1:
																				SCREAMING_SNAKE_CASE__													= 1 + min(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)
																				SCREAMING_SNAKE_CASE__													= max(current_row[col]	,	UpperCamelCase_		)
															else:
																				SCREAMING_SNAKE_CASE__													= 0
										SCREAMING_SNAKE_CASE__													= current_row

					return largest_square_area


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))





