




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 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]	):




 """simple docstring"""







 UpperCAmelCase__							 =						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.'''	)
 UpperCAmelCase__							 =						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.'''	)

 UpperCAmelCase__							 =						components[:-1] + [test_fn.replace(".py"						,							""	)]
 UpperCAmelCase__							 =						".".join(_lowerCAmelCase	)

 return test_module_path



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						get_module_path(_lowerCAmelCase	)
 UpperCAmelCase__							 =						importlib.import_module(_lowerCAmelCase	)

 return test_module



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[str]	):




 """simple docstring"""







 UpperCAmelCase__							 =						[]
 UpperCAmelCase__							 =						get_test_module(_lowerCAmelCase	)
 for attr in dir(_lowerCAmelCase	):
  if attr.endswith("ModelTester"	):
   tester_classes.append(getattr(_lowerCAmelCase						,							_lowerCAmelCase	)	)

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any	):




 """simple docstring"""







 UpperCAmelCase__							 =						[]
 UpperCAmelCase__							 =						get_test_module(_lowerCAmelCase	)
 for attr in dir(_lowerCAmelCase	):
  UpperCAmelCase__							 =						getattr(_lowerCAmelCase						,							_lowerCAmelCase	)
  # (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).
  UpperCAmelCase__							 =						getattr(_lowerCAmelCase						,							"all_model_classes"						,							[]	)
  if len(_lowerCAmelCase	) > 0:
   test_classes.append(_lowerCAmelCase	)

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict	):




 """simple docstring"""







 UpperCAmelCase__							 =						get_test_classes(_lowerCAmelCase	)
 UpperCAmelCase__							 =						set()
 for test_class in test_classes:
  model_classes.update(test_class.all_model_classes	)

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						test_class()
 if hasattr(_lowerCAmelCase						,							"setUp"	):
  test.setUp()

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

 return model_tester



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   Dict	):




 """simple docstring"""







 UpperCAmelCase__							 =						get_test_classes(_lowerCAmelCase	)

 UpperCAmelCase__							 =						[]
 for test_class in test_classes:
  if model_class in test_class.all_model_classes:
   target_test_classes.append(_lowerCAmelCase	)

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   Optional[int]	):




 """simple docstring"""







 UpperCAmelCase__							 =						get_test_classes_for_model(_lowerCAmelCase						,							_lowerCAmelCase	)

 UpperCAmelCase__							 =						[]
 for test_class in test_classes:
  UpperCAmelCase__							 =						get_model_tester_from_test_class(_lowerCAmelCase	)
  if tester_class is not None:
   tester_classes.append(_lowerCAmelCase	)

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict	):




 """simple docstring"""







 UpperCAmelCase__							 =						get_test_classes(_lowerCAmelCase	)
 UpperCAmelCase__							 =						{test_class: get_model_tester_from_test_class(_lowerCAmelCase	) for test_class in test_classes}
 return test_tester_mapping



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict	):




 """simple docstring"""







 UpperCAmelCase__							 =						get_model_classes(_lowerCAmelCase	)
 UpperCAmelCase__							 =						{
     model_class: get_test_classes_for_model(_lowerCAmelCase						,							_lowerCAmelCase	) for model_class in model_classes
 }
 return model_test_mapping



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any	):




 """simple docstring"""







 UpperCAmelCase__							 =						get_model_classes(_lowerCAmelCase	)
 UpperCAmelCase__							 =						{
     model_class: get_tester_classes_for_model(_lowerCAmelCase						,							_lowerCAmelCase	) for model_class in model_classes
 }
 return model_to_tester_mapping



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict	):




 """simple docstring"""







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











# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCAmelCase						:   int		=   "facebook/wmt19-en-de"
_lowerCAmelCase						:   int		=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase						:   Dict		=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCAmelCase						:   List[Any]		=   FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCAmelCase						:   Optional[Any]		=   tokenizer(["Making tiny model"], return_tensors="pt")
_lowerCAmelCase						:   Optional[Any]		=   tiny_model(**batch)

print("test output:", len(outputs.logits[0]))

# Save
_lowerCAmelCase						:   Optional[Any]		=   "tiny-wmt19-en-de"
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de





