





# 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

SCREAMING_SNAKE_CASE_		     = """facebook/wmt19-en-de"""
SCREAMING_SNAKE_CASE_		     = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_CASE_		     = 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,
    )
)

SCREAMING_SNAKE_CASE_		     = FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
SCREAMING_SNAKE_CASE_		     = tokenizer(["""Making tiny model"""], return_tensors="""pt""")
SCREAMING_SNAKE_CASE_		     = tiny_model(**batch)

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

# Save
SCREAMING_SNAKE_CASE_		     = """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









# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					Optional[Any]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					bool = False )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		scheduler
           SCREAMING_SNAKE_CASE	=		optimizers if isinstance(lowerCamelCase__		,(list, tuple) ) else [optimizers]
           SCREAMING_SNAKE_CASE	=		split_batches
           SCREAMING_SNAKE_CASE	=		step_with_optimizer
           SCREAMING_SNAKE_CASE	=		GradientState()






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,*lowerCamelCase__		:					Union[str, Any]		,**lowerCamelCase__		:					Optional[int] )  ->	Dict:




           '''simple docstring'''


           if not self.step_with_optimizer:
                 # No link between scheduler and optimizer -> just step
                 self.scheduler.step(*lowerCamelCase__		,**lowerCamelCase__ )
                 return

           # Otherwise, first make sure the optimizer was stepped.
           if not self.gradient_state.sync_gradients:
                 if self.gradient_state.adjust_scheduler:
                       self.scheduler._step_count += 1
                 return

           for opt in self.optimizers:
                 if opt.step_was_skipped:
                       return
           if self.split_batches:
                 # Split batches -> the training dataloader batch size is not changed so one step per training step
                 self.scheduler.step(*lowerCamelCase__		,**lowerCamelCase__ )
           else:
                 # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
                 # num_processes steps per training step
                 SCREAMING_SNAKE_CASE	=		AcceleratorState().num_processes
                 for _ in range(lowerCamelCase__ ):
                       # Special case when using OneCycle and `drop_last` was not used
                       if hasattr(self.scheduler		,"""total_steps""" ):
                             if self.scheduler._step_count <= self.scheduler.total_steps:
                                   self.scheduler.step(*lowerCamelCase__		,**lowerCamelCase__ )
                       else:
                             self.scheduler.step(*lowerCamelCase__		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Optional[Any]:




           '''simple docstring'''


           return self.scheduler.get_last_lr()






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           return self.scheduler.state_dict()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					str )  ->	int:




           '''simple docstring'''


           self.scheduler.load_state_dict(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           return self.scheduler.get_lr()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,*lowerCamelCase__		:					int		,**lowerCamelCase__		:					Tuple )  ->	Optional[int]:




           '''simple docstring'''


           return self.scheduler.print_lr(*lowerCamelCase__		,**lowerCamelCase__ )


