






'''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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A	:			Dict         ='''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A	:			List[str]         =[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A	:			str         =dict(zip(vocab, range(len(vocab))))
_A	:			List[str]         =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			_A	:			Union[str, Any]         =Path(tmpdirname)
			_A	:			str         =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			_A	:			int         =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			_A	:			List[Any]         =build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			_A	:			int         =FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

_A	:			List[str]         =FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    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,
)

_A	:			Union[str, Any]         =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
_A	:			List[str]         =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A	:			Tuple         =tiny_model(**batch)

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

# Save
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-ru









'''simple docstring'''







class _lowercase	:




							def __init__(							self:				Optional[Any]     ):
														lowerCamelCase__					:	dict[str, TrieNode]         =       {}  # Mapping from char to TrieNode
														lowerCamelCase__					:	List[str]         =       False




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				list[str]     ):
														for word in words:
																					self.insert(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	List[Any]         =       self
														for char in word:
																					if char not in curr.nodes:
																												lowerCamelCase__					:	Tuple         =       TrieNode()
																					lowerCamelCase__					:	List[Any]         =       curr.nodes[char]
														lowerCamelCase__					:	Any         =       True




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	Union[str, Any]         =       self
														for char in word:
																					if char not in curr.nodes:
																												return False
																					lowerCamelCase__					:	Any         =       curr.nodes[char]
														return curr.is_leaf




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				str     ):

														def _delete(UpperCamelCase__:				TrieNode			,							UpperCamelCase__:				str			,							UpperCamelCase__:				int     ) -> bool:
																					if index == len(UpperCamelCase__     ):
																												# If word does not exist
																												if not curr.is_leaf:
																																			return False
																												lowerCamelCase__					:	str         =       False
																												return len(curr.nodes     ) == 0
																					lowerCamelCase__					:	List[str]         =       word[index]
																					lowerCamelCase__					:	Dict         =       curr.nodes.get(UpperCamelCase__     )
																					# If char not in current trie node
																					if not char_node:
																												return False
																					# Flag to check if node can be deleted
																					lowerCamelCase__					:	List[Any]         =       _delete(UpperCamelCase__			,							UpperCamelCase__			,							index + 1     )
																					if delete_curr:
																												del curr.nodes[char]
																												return len(curr.nodes     ) == 0
																					return delete_curr

														_delete(self			,							UpperCamelCase__			,							0     )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       None:
							if node.is_leaf:
														print(UpperCamelCase				,			end=""" """      )

							for key, value in node.nodes.items():
														print_words(UpperCamelCase				,			word + key      )





def  SCREAMING_SNAKE_CASE_       ()      ->       bool:
							lowerCamelCase__					:	str         =       """banana bananas bandana band apple all beast""".split()
							lowerCamelCase__					:	Union[str, Any]         =       TrieNode()
							root.insert_many(UpperCamelCase      )
							# print_words(root, "")
							assert all(root.find(UpperCamelCase      ) for word in words      )
							assert root.find("""banana"""      )
							assert not root.find("""bandanas"""      )
							assert not root.find("""apps"""      )
							assert root.find("""apple"""      )
							assert root.find("""all"""      )
							root.delete("""all"""      )
							assert not root.find("""all"""      )
							root.delete("""banana"""      )
							assert not root.find("""banana"""      )
							assert root.find("""bananas"""      )
							return True





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       None:
							print(str(UpperCamelCase      )				,			"""works!""" if passes else """doesn't work :("""      )





def  SCREAMING_SNAKE_CASE_       ()      ->       None:
							assert test_trie()





def  SCREAMING_SNAKE_CASE_       ()      ->       None:
							print_results("""Testing trie functionality"""				,			test_trie()      )


if __name__ == "__main__":
			main()

