
"""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

_snake_case          =						'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_snake_case          =						[
    '''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>''',
]
_snake_case          =						dict(zip(vocab, range(len(vocab))))
_snake_case          =						['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
	_snake_case          =						Path(tmpdirname)
	_snake_case          =						build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
	_snake_case          =						build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
	_snake_case          =						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))

	_snake_case          =						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,
	)

_snake_case          =						FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    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,
)

_snake_case          =						FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
_snake_case          =						tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_snake_case          =						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"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			generate_pascal_triangle(SCREAMING_SNAKE_CASE						)
							for row_idx in range(SCREAMING_SNAKE_CASE						):
														# Print left spaces
														for _ in range(num_rows - row_idx - 1						):
																					print(end=' '						)
														# Print row values
														for col_idx in range(row_idx + 1						):
																					if col_idx != row_idx:
																												print(triangle[row_idx][col_idx]     ,  end=' '						)
																					else:
																												print(triangle[row_idx][col_idx]     ,  end=''						)
														print()






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							if not isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
														raise TypeError('The input value of \'num_rows\' should be \'int\''						)

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'						)

							_lowerCAmelCase  	=			[]
							for current_row_idx in range(SCREAMING_SNAKE_CASE						):
														_lowerCAmelCase  	=			populate_current_row(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
														triangle.append(SCREAMING_SNAKE_CASE						)
							return triangle






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					list[list[int]]     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							_lowerCAmelCase	,		_lowerCAmelCase  	=			1, 1
							for current_col_idx in range(1     ,  SCREAMING_SNAKE_CASE						):
														calculate_current_element(
														    SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							return current_row






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					list[list[int]]     ,  SCREAMING_SNAKE_CASE:					list[int]     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					int     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			triangle[current_row_idx - 1][current_col_idx - 1]
							_lowerCAmelCase  	=			triangle[current_row_idx - 1][current_col_idx]
							_lowerCAmelCase  	=			above_to_left_elt + above_to_right_elt






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							if not isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
														raise TypeError('The input value of \'num_rows\' should be \'int\''						)

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'						)

							_lowerCAmelCase  	=			[[1]]

							for row_index in range(1     ,  SCREAMING_SNAKE_CASE						):
														_lowerCAmelCase  	=			[0] + result[-1] + [0]
														_lowerCAmelCase  	=			row_index + 1
														# Calculate the number of distinct elements in a row
														_lowerCAmelCase  	=			sum(divmod(SCREAMING_SNAKE_CASE     ,  2						)						)
														_lowerCAmelCase  	=			[
														    temp_row[i - 1] + temp_row[i] for i in range(1     ,  distinct_elements + 1						)
														]
														_lowerCAmelCase  	=			row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														_lowerCAmelCase  	=			row_first_half + row_second_half
														result.append(SCREAMING_SNAKE_CASE						)

							return result






def 	__snake_case  (    ):






							"""simple docstring"""



							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(SCREAMING_SNAKE_CASE:					Callable     ,  SCREAMING_SNAKE_CASE:					int						) -> None:
														_lowerCAmelCase  	=			f"""{func.__name__}({value})"""
														_lowerCAmelCase  	=			timeit(f"""__main__.{call}"""     ,  setup='import __main__'						)
														# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
														print(f"""{call:38} -- {timing:.4f} seconds"""						)

							for value in range(15						):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
														print()


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()






