




import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCamelCase								=	'src/diffusers'

# Matches is_xxx_available()
UpperCamelCase								=	re.compile(R'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
UpperCamelCase								=	re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


UpperCamelCase								=	'\n{0} = None\n'

UpperCamelCase								=	'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


UpperCamelCase								=	'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'



def 				_A  (		lowerCAmelCase_	:   Union[str, Any]     ):
     """simple docstring"""

     lowerCAmelCase__            =					_re_backend.findall(lowerCAmelCase_     )
     if len(lowerCAmelCase_     ) == 0:
          return None

     return "_and_".join(lowerCAmelCase_     )



def 				_A  (		):
     """simple docstring"""

     with open(os.path.join(lowerCAmelCase_  ,							"__init__.py"     )  ,							"r"  ,							encoding="utf-8"  ,							newline="\n"     ) as f:
          lowerCAmelCase__            =					f.readlines()

     # Get to the point we do the actual imports for type checking
     lowerCAmelCase__            =					0
     lowerCAmelCase__            =					{}
     # Go through the end of the file
     while line_index < len(lowerCAmelCase_     ):
          # If the line contains is_backend_available, we grab all objects associated with the `else` block
          lowerCAmelCase__            =					find_backend(lines[line_index]     )
          if backend is not None:
               while not lines[line_index].startswith("else:"     ):
                    line_index += 1
               line_index += 1
               lowerCAmelCase__            =					[]
               # Until we unindent, add backend objects to the list
               while line_index < len(lowerCAmelCase_     ) and len(lines[line_index]     ) > 1:
                    lowerCAmelCase__            =					lines[line_index]
                    lowerCAmelCase__            =					_re_single_line_import.search(lowerCAmelCase_     )
                    if single_line_import_search is not None:
                         objects.extend(single_line_import_search.groups()[0].split(", "     )     )
                    elif line.startswith(" " * 8     ):
                         objects.append(line[8:-2]     )
                    line_index += 1

               if len(lowerCAmelCase_     ) > 0:
                    lowerCAmelCase__            =					objects
          else:
               line_index += 1

     return backend_specific_objects



def 				_A  (		lowerCAmelCase_	:   int  ,							lowerCAmelCase_	:   List[Any]     ):
     """simple docstring"""

     if name.isupper():
          return DUMMY_CONSTANT.format(lowerCAmelCase_     )
     elif name.islower():
          return DUMMY_FUNCTION.format(lowerCAmelCase_  ,							lowerCAmelCase_     )
     else:
          return DUMMY_CLASS.format(lowerCAmelCase_  ,							lowerCAmelCase_     )



def 				_A  (		lowerCAmelCase_	:   Tuple=None     ):
     """simple docstring"""

     if backend_specific_objects is None:
          lowerCAmelCase__            =					read_init()
     # For special correspondence backend to module name as used in the function requires_modulename
     lowerCAmelCase__            =					{}

     for backend, objects in backend_specific_objects.items():
          lowerCAmelCase__            =					"[" + ", ".join(F'"{b}"' for b in backend.split("_and_"     )     ) + "]"
          lowerCAmelCase__            =					"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
          dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
          dummy_file += "\n".join([create_dummy_object(lowerCAmelCase_  ,							lowerCAmelCase_     ) for o in objects]     )
          lowerCAmelCase__            =					dummy_file

     return dummy_files



def 				_A  (		lowerCAmelCase_	:   Optional[int]=False     ):
     """simple docstring"""

     lowerCAmelCase__            =					create_dummy_files()
     # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
     lowerCAmelCase__            =					{"torch": "pt"}

     # Locate actual dummy modules and read their content.
     lowerCAmelCase__            =					os.path.join(lowerCAmelCase_  ,							"utils"     )
     lowerCAmelCase__            =					{
         backend: os.path.join(lowerCAmelCase_  ,							F'dummy_{short_names.get(lowerCAmelCase_  ,							lowerCAmelCase_     )}_objects.py'     )
         for backend in dummy_files.keys()
     }

     lowerCAmelCase__            =					{}
     for backend, file_path in dummy_file_paths.items():
          if os.path.isfile(lowerCAmelCase_     ):
               with open(lowerCAmelCase_  ,							"r"  ,							encoding="utf-8"  ,							newline="\n"     ) as f:
                    lowerCAmelCase__            =					f.read()
          else:
               lowerCAmelCase__            =					""

     for backend in dummy_files.keys():
          if dummy_files[backend] != actual_dummies[backend]:
               if overwrite:
                    print(
                        F'Updating diffusers.utils.dummy_{short_names.get(lowerCAmelCase_  ,							lowerCAmelCase_     )}_objects.py as the main '
                        "__init__ has new objects."     )
                    with open(dummy_file_paths[backend]  ,							"w"  ,							encoding="utf-8"  ,							newline="\n"     ) as f:
                         f.write(dummy_files[backend]     )
               else:
                    raise ValueError(
                        "The main __init__ has objects that are not present in "
                        F'diffusers.utils.dummy_{short_names.get(lowerCAmelCase_  ,							lowerCAmelCase_     )}_objects.py. Run `make fix-copies` '
                        "to fix this."     )


if __name__ == "__main__":
     UpperCamelCase								=	argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     UpperCamelCase								=	parser.parse_args()

     check_dummies(args.fix_and_overwrite)





'''simple docstring'''





import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.albert.modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
  )






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=13					,  A=7					,  A=True					,  A=True					,  A=True					,  A=True					,  A=99					,  A=32					,  A=5					,  A=4					,  A=37					,  A="gelu"					,  A=0.1					,  A=0.1					,  A=512					,  A=16					,  A=2					,  A=0.02					,  A=4					,  )       ->	List[str]:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    seq_length
      _a           =    is_training
      _a           =    use_attention_mask
      _a           =    use_token_type_ids
      _a           =    use_labels
      _a           =    vocab_size
      _a           =    hidden_size
      _a           =    num_hidden_layers
      _a           =    num_attention_heads
      _a           =    intermediate_size
      _a           =    hidden_act
      _a           =    hidden_dropout_prob
      _a           =    attention_probs_dropout_prob
      _a           =    max_position_embeddings
      _a           =    type_vocab_size
      _a           =    type_sequence_label_size
      _a           =    initializer_range
      _a           =    num_choices



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size			)

      _a           =    None
      if self.use_attention_mask:
           _a           =    random_attention_mask([self.batch_size, self.seq_length]			)

      _a           =    None
      if self.use_token_type_ids:
           _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size			)

      _a           =    AlbertConfig(
          vocab_size=self.vocab_size					,  hidden_size=self.hidden_size					,  num_hidden_layers=self.num_hidden_layers					,  num_attention_heads=self.num_attention_heads					,  intermediate_size=self.intermediate_size					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  type_vocab_size=self.type_vocab_size					,  is_decoder=A					,  initializer_range=self.initializer_range					,  )

      return config, input_ids, token_type_ids, attention_mask



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


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      _a    ,       _a    ,       _a    ,       _a           =    config_and_inputs
      _a           =    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
      return config, inputs_dict






@require_flax
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[int]		=			(
     (
         FlaxAlbertModel,
         FlaxAlbertForPreTraining,
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    FlaxAlbertModelTester(self			)



 @slow
 def    a__    (self			)       ->	str:


      """simple docstring"""



      for model_class_name in self.all_model_classes:
           _a           =    model_class_name.from_pretrained('''albert-base-v2'''			)
           _a           =    model(np.ones((1, 1)			)			)
           self.assertIsNotNone(A			)






@require_flax
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 @slow
 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    FlaxAlbertModel.from_pretrained('''albert-base-v2'''			)
      _a           =    np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]			)
      _a           =    np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
      _a           =    model(A					,  attention_mask=A			)[0]
      _a           =    (1, 11, 768)
      self.assertEqual(output.shape					,  A			)
      _a           =    np.array(
          [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]			)

      self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]					,  A					,  atol=1E-4			)			)



