






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    (      a :Union[Dict, str, None] = None )							->			str:
 a		  =      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(a	,							a ):
  ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items() )
 elif isinstance(a	,							a ):
  ua += "; " + user_agent
 return ua
def 						_a    (      a :str	,							a :Optional[str] = None	,							a :Optional[str] = None )							->			Union[str, Any]:
 if token is None:
  a		  =      HfFolder.get_token()
 if organization is None:
  a		  =      whoami(a )['''name''']
  return F"""{username}/{model_id}"""
 else:
  return F"""{organization}/{model_id}"""
def 						_a    (      a :List[Any]	,							a :Tuple )							->			List[str]:
 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(a	,							'''local_rank''' ) and args.local_rank not in [-1, 0]:
  return

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

 a		  =      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=a	,							model_name=a	,							repo_name=a	,							dataset_name=args.dataset_name if hasattr(a	,							'''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(a	,							'''gradient_accumulation_steps''' ) else None
     )	,							adam_betaa=args.adam_betaa if hasattr(a	,							'''adam_beta1''' ) else None	,							adam_betaa=args.adam_betaa if hasattr(a	,							'''adam_beta2''' ) else None	,							adam_weight_decay=args.adam_weight_decay if hasattr(a	,							'''adam_weight_decay''' ) else None	,							adam_epsilon=args.adam_epsilon if hasattr(a	,							'''adam_epsilon''' ) else None	,							lr_scheduler=args.lr_scheduler if hasattr(a	,							'''lr_scheduler''' ) else None	,							lr_warmup_steps=args.lr_warmup_steps if hasattr(a	,							'''lr_warmup_steps''' ) else None	,							ema_inv_gamma=args.ema_inv_gamma if hasattr(a	,							'''ema_inv_gamma''' ) else None	,							ema_power=args.ema_power if hasattr(a	,							'''ema_power''' ) else None	,							ema_max_decay=args.ema_max_decay if hasattr(a	,							'''ema_max_decay''' ) else None	,							mixed_precision=args.mixed_precision	,							)

 a		  =      os.path.join(args.output_dir	,							'''README.md''' )
 model_card.save(a )
def 						_a    (      a :Optional[str]	,							a :Optional[str] = None )							->			Optional[int]:
 if resolved_file is None or commit_hash is not None:
  return commit_hash
 a		  =      str(Path(a ).as_posix() )
 a		  =      re.search(r'''snapshots/([^/]+)/'''	,							a )
 if search is None:
  return None
 a		  =      search.groups()[0]
 return commit_hash if REGEX_COMMIT_HASH.match(a ) 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    (      a :Optional[str] = None	,							a :Optional[str] = None )							->			None:
 if new_cache_dir is None:
  a		  =      DIFFUSERS_CACHE
 if old_cache_dir is None:
  a		  =      old_diffusers_cache

 a		  =      Path(a ).expanduser()
 a		  =      Path(a ).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		  =      new_cache_dir / old_blob_path.relative_to(a )
   new_blob_path.parent.mkdir(parents=a	,							exist_ok=a )
   os.replace(a	,							a )
   try:
    os.symlink(a	,							a )
   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    (      a :str	,							a :Optional[str] = None )							->			str:
 if variant is not None:
  a		  =      weights_name.split('''.''' )
  a		  =      splits[:-1] + [variant] + splits[-1:]
  a		  =      '''.'''.join(a )

 return weights_name

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










import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Tuple:




       """simple docstring"""


       a		  =      tempfile.mkdtemp()

       # fmt: off
       a		  =      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
       # fmt: on
       a		  =      os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''vocab_file''']  )
       with open(self.vocab_file ,					'''w''' ,					encoding='''utf-8'''  ) as vocab_writer:
        vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )

       a		  =      {
           '''do_resize''': True,
           '''size''': {'''height''': 18, '''width''': 18},
           '''do_normalize''': True,
           '''image_mean''': [0.5, 0.5, 0.5],
           '''image_std''': [0.5, 0.5, 0.5],
       }
       a		  =      os.path.join(self.tmpdirname ,					__UpperCAmelCase  )
       with open(self.image_processor_file ,					'''w''' ,					encoding='''utf-8'''  ) as fp:
        json.dump(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						List[Any] ,					**__UpperCAmelCase      :						List[Any]  )    ->int:




       """simple docstring"""


       return BertTokenizer.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any] ,					**__UpperCAmelCase      :						Optional[int]  )    ->Union[str, Any]:




       """simple docstring"""


       return ViTImageProcessor.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


       shutil.rmtree(self.tmpdirname  )







      def       __lowerCAmelCase					(      self      :						str  )    ->Dict:




       """simple docstring"""


       a		  =      [np.random.randint(255 ,					size=(3, 30, 400) ,					dtype=np.uinta  )]

       a		  =      [Image.fromarray(np.moveaxis(__UpperCAmelCase ,					0 ,					-1  )  ) for x in image_inputs]

       return image_inputs







      def       __lowerCAmelCase					(      self      :						Any  )    ->Tuple:




       """simple docstring"""


       a		  =      self.get_tokenizer()
       a		  =      self.get_image_processor()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       processor.save_pretrained(self.tmpdirname  )
       a		  =      VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname  )

       self.assertEqual(processor.tokenizer.get_vocab() ,					tokenizer.get_vocab()  )
       self.assertIsInstance(processor.tokenizer ,					(BertTokenizer, BertTokenizerFast)  )

       self.assertEqual(processor.image_processor.to_json_string() ,					image_processor.to_json_string()  )
       self.assertIsInstance(processor.image_processor ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						int  )    ->Dict:




       """simple docstring"""


       a		  =      VisionTextDualEncoderProcessor(
           tokenizer=self.get_tokenizer() ,					image_processor=self.get_image_processor()  )
       processor.save_pretrained(self.tmpdirname  )

       a		  =      self.get_tokenizer(bos_token='''(BOS)''' ,					eos_token='''(EOS)'''  )
       a		  =      self.get_image_processor(do_normalize=__UpperCAmelCase ,					padding_value=1.0  )

       a		  =      VisionTextDualEncoderProcessor.from_pretrained(
           self.tmpdirname ,					bos_token='''(BOS)''' ,					eos_token='''(EOS)''' ,					do_normalize=__UpperCAmelCase ,					padding_value=1.0  )

       self.assertEqual(processor.tokenizer.get_vocab() ,					tokenizer_add_kwargs.get_vocab()  )
       self.assertIsInstance(processor.tokenizer ,					(BertTokenizer, BertTokenizerFast)  )

       self.assertEqual(processor.image_processor.to_json_string() ,					image_processor_add_kwargs.to_json_string()  )
       self.assertIsInstance(processor.image_processor ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Dict:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      self.prepare_image_inputs()

       a		  =      image_processor(__UpperCAmelCase ,					return_tensors='''np'''  )
       a		  =      processor(images=__UpperCAmelCase ,					return_tensors='''np'''  )

       for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum() ,					input_processor[key].sum() ,					delta=1e-2  )







      def       __lowerCAmelCase					(      self      :						List[str]  )    ->str:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      '''lower newer'''

       a		  =      processor(text=__UpperCAmelCase  )

       a		  =      tokenizer(__UpperCAmelCase  )

       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key] ,					encoded_processor[key]  )







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      '''lower newer'''
       a		  =      self.prepare_image_inputs()

       a		  =      processor(text=__UpperCAmelCase ,					images=__UpperCAmelCase  )

       self.assertListEqual(list(inputs.keys()  ) ,					['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']  )

       # test if it raises when no input is passed
       with self.assertRaises(__UpperCAmelCase  ):
        processor()







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       a		  =      processor.batch_decode(__UpperCAmelCase  )
       a		  =      tokenizer.batch_decode(__UpperCAmelCase  )

       self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Dict:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      '''lower newer'''
       a		  =      self.prepare_image_inputs()

       a		  =      processor(text=__UpperCAmelCase ,					images=__UpperCAmelCase  )

       self.assertListEqual(list(inputs.keys()  ) ,					processor.model_input_names  )


