






'''simple docstring'''







from typing import Any





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Any       )      ->      Dict:
							a_		:	Tuple									=   data
							a_		:	Tuple									=   None





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      Any       )      ->      List[Any]:
							a_		:	str									=   None






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      int:
							a_		:	Optional[int]									=   self.head
							while temp is not None:
											print(temp.data	,    end=''' '''       )
											a_		:	List[str]									=   temp.next
							print()






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Any       )      ->      str:
							a_		:	int									=   Node(__SCREAMING_SNAKE_CASE       )
							a_		:	Union[str, Any]									=   self.head
							a_		:	List[str]									=   new_node







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      List[Any]:
							if node_data_a == node_data_a:
											return
							else:
											a_		:	Optional[int]									=   self.head
											while node_a is not None and node_a.data != node_data_a:
															a_		:	Union[str, Any]									=   node_a.next

											a_		:	int									=   self.head
											while node_a is not None and node_a.data != node_data_a:
															a_		:	int									=   node_a.next

											if node_a is None or node_a is None:
															return

											a_					,      a_		:	Union[str, Any]									=   node_a.data, node_a.data


if __name__ == "__main__":
		__lowerCAmelCase									=   LinkedList()
		for i in range(5, 0, -1):
				ll.push(i)

		ll.print_list()

		ll.swap_nodes(1, 4)
		print('After swapping')
		ll.print_list()












'''simple docstring'''







# 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									=   'facebook/wmt19-en-de'
__lowerCAmelCase									=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase									=   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									=   FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

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

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

# Save
__lowerCAmelCase									=   '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




