



"""simple docstring"""






import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')






def 	snake_case_  (				A_ :		Optional[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			test_file.split(os.path.sep						)
       if components[0:2] != ["tests", "models"]:
              raise ValueError(
                  '''`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '''
                  F'''{test_file} instead.'''						)
       _lowerCamelCase						:    Tuple         =			components[-1]
       if not test_fn.endswith('''py'''						):
              raise ValueError(F'''`test_file` should be a python file. Got {test_fn} instead.'''						)
       if not test_fn.startswith('''test_modeling_'''						):
              raise ValueError(
                  F'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''						)

       _lowerCamelCase						:    Dict         =			components[:-1] + [test_fn.replace('''.py''',    ''''''						)]
       _lowerCamelCase						:    Optional[int]         =			'''.'''.join(A_						)

       return test_module_path






def 	snake_case_  (				A_ :		Union[str, Any]						):


       '''simple docstring'''





       _lowerCamelCase						:    List[str]         =			get_module_path(A_						)
       _lowerCamelCase						:    str         =			importlib.import_module(A_						)

       return test_module






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       _lowerCamelCase						:    Tuple         =			[]
       _lowerCamelCase						:    List[Any]         =			get_test_module(A_						)
       for attr in dir(A_						):
              if attr.endswith('''ModelTester'''						):
                     tester_classes.append(getattr(A_,    A_						)						)

    # sort with class names
       return sorted(A_,    key=lambda A_						: x.__name__						)






def 	snake_case_  (				A_ :		Union[str, Any]						):


       '''simple docstring'''





       _lowerCamelCase						:    List[Any]         =			[]
       _lowerCamelCase						:    int         =			get_test_module(A_						)
       for attr in dir(A_						):
              _lowerCamelCase						:    Optional[Any]         =			getattr(A_,    A_						)
              # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
              # `all_model_classes` is not empty (which also excludes other special classes).
              _lowerCamelCase						:    int         =			getattr(A_,    '''all_model_classes''',    []						)
              if len(A_						) > 0:
                     test_classes.append(A_						)

    # sort with class names
       return sorted(A_,    key=lambda A_						: x.__name__						)






def 	snake_case_  (				A_ :		Optional[int]						):


       '''simple docstring'''





       _lowerCamelCase						:    Tuple         =			get_test_classes(A_						)
       _lowerCamelCase						:    Optional[int]         =			set()
       for test_class in test_classes:
              model_classes.update(test_class.all_model_classes						)

       # sort with class names
       return sorted(A_,    key=lambda A_						: x.__name__						)






def 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			test_class()
       if hasattr(A_,    '''setUp'''						):
              test.setUp()

       _lowerCamelCase						:    int         =			None
       if hasattr(A_,    '''model_tester'''						):
              # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
              if test.model_tester is not None:
                     _lowerCamelCase						:    Tuple         =			test.model_tester.__class__

       return model_tester






def 	snake_case_  (				A_ :		Dict,    A_ :		Any						):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[int]         =			get_test_classes(A_						)

       _lowerCamelCase						:    List[str]         =			[]
       for test_class in test_classes:
              if model_class in test_class.all_model_classes:
                     target_test_classes.append(A_						)

    # sort with class names
       return sorted(A_,    key=lambda A_						: x.__name__						)






def 	snake_case_  (				A_ :		Optional[Any],    A_ :		Optional[int]						):


       '''simple docstring'''





       _lowerCamelCase						:    List[str]         =			get_test_classes_for_model(A_,    A_						)

       _lowerCamelCase						:    Any         =			[]
       for test_class in test_classes:
              _lowerCamelCase						:    Union[str, Any]         =			get_model_tester_from_test_class(A_						)
              if tester_class is not None:
                     tester_classes.append(A_						)

    # sort with class names
       return sorted(A_,    key=lambda A_						: x.__name__						)






def 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





       _lowerCamelCase						:    str         =			get_test_classes(A_						)
       _lowerCamelCase						:    str         =			{test_class: get_model_tester_from_test_class(A_						) for test_class in test_classes}
       return test_tester_mapping






def 	snake_case_  (				A_ :		Optional[int]						):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[int]         =			get_model_classes(A_						)
       _lowerCamelCase						:    Optional[Any]         =			{
           model_class: get_test_classes_for_model(A_,    A_						) for model_class in model_classes
       }
       return model_test_mapping






def 	snake_case_  (				A_ :		List[str]						):


       '''simple docstring'''





       _lowerCamelCase						:    str         =			get_model_classes(A_						)
       _lowerCamelCase						:    int         =			{
           model_class: get_tester_classes_for_model(A_,    A_						) for model_class in model_classes
       }
       return model_to_tester_mapping







def 	snake_case_  (				A_ :		Dict						):


       '''simple docstring'''





       if isinstance(A_,    A_						):
              return o
       elif isinstance(A_,    A_						):
              return o.__name__
       elif isinstance(A_,    (list, tuple)						):
              return [to_json(A_						) for x in o]
       elif isinstance(A_,    A_						):
              return {to_json(A_						): to_json(A_						) for k, v in o.items()}
       else:
              return o







"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      =    logging.get_logger(__name__)

lowerCAmelCase__      =    {
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class 	__snake_case	(     _lowercase):
  snake_case__    :       List[Any]        =						"xglm"
  snake_case__    :       Dict        =						["past_key_values"]

  snake_case__    :       str        =						{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }




  def __init__(			self					:						List[str]						, __lowerCAmelCase					:						List[Any]=2_5_6_0_0_8						, __lowerCAmelCase					:						int=2_0_4_8						, __lowerCAmelCase					:						Dict=1_0_2_4						, __lowerCAmelCase					:						List[str]=4_0_9_6						, __lowerCAmelCase					:						Tuple=2_4						, __lowerCAmelCase					:						Dict=1_6						, __lowerCAmelCase					:						Tuple="gelu"						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						Optional[Any]=0.0						, __lowerCAmelCase					:						List[Any]=0.0						, __lowerCAmelCase					:						int=0.02						, __lowerCAmelCase					:						Any=True						, __lowerCAmelCase					:						Tuple=True						, __lowerCAmelCase					:						str=2						, __lowerCAmelCase					:						Dict=1						, __lowerCAmelCase					:						Dict=0						, __lowerCAmelCase					:						List[Any]=2						, **__lowerCAmelCase					:						Optional[Any]						, ):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			vocab_size
         _lowerCamelCase						:    List[Any]         =			max_position_embeddings
         _lowerCamelCase						:    int         =			d_model
         _lowerCamelCase						:    Optional[Any]         =			ffn_dim
         _lowerCamelCase						:    Any         =			num_layers
         _lowerCamelCase						:    Union[str, Any]         =			attention_heads
         _lowerCamelCase						:    List[str]         =			activation_function
         _lowerCamelCase						:    Union[str, Any]         =			dropout
         _lowerCamelCase						:    int         =			attention_dropout
         _lowerCamelCase						:    Optional[int]         =			activation_dropout
         _lowerCamelCase						:    Any         =			layerdrop
         _lowerCamelCase						:    List[str]         =			init_std
         _lowerCamelCase						:    Union[str, Any]         =			scale_embedding  # scale factor will be sqrt(d_model) if True
         _lowerCamelCase						:    str         =			use_cache

         super().__init__(
             pad_token_id=__lowerCAmelCase						, bos_token_id=__lowerCAmelCase						, eos_token_id=__lowerCAmelCase						, decoder_start_token_id=__lowerCAmelCase						, **__lowerCAmelCase						, )


