




'''simple docstring'''


import numpy

# List of input, output pairs
_snake_case : Tuple  				=						(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_snake_case : str  				=						(((515, 22, 13), 555), ((61, 35, 49), 150))
_snake_case : Optional[Any]  				=						[2, 4, 1, 5]
_snake_case : List[Any]  				=						len(train_data)
_snake_case : str  				=						0.009
def 						snake_case_       (UpperCamelCase			:    Any					,		UpperCamelCase			:    Tuple="train" ):





							'''simple docstring'''


							return calculate_hypothesis_value(UpperCamelCase					,		UpperCamelCase ) - output(
							    UpperCamelCase					,		UpperCamelCase )
def 						snake_case_       (UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							0
							for i in range(len(UpperCamelCase ) - 1 ):
														hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
							hyp_val += parameter_vector[0]
							return hyp_val
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							if data_set == "train":
														return train_data[example_no][1]
							elif data_set == "test":
														return test_data[example_no][1]
							return None
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    int ):





							'''simple docstring'''


							if data_set == "train":
														return _hypothesis_value(train_data[example_no][0] )
							elif data_set == "test":
														return _hypothesis_value(test_data[example_no][0] )
							return None
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    Union[str, Any]=m ):





							'''simple docstring'''


							_a      	=							0
							for i in range(UpperCamelCase ):
														if index == -1:
																					summation_value += _error(UpperCamelCase )
														else:
																					summation_value += _error(UpperCamelCase ) * train_data[i][0][index]
							return summation_value
def 						snake_case_       (UpperCamelCase			:    Dict ):





							'''simple docstring'''


							_a      	=							summation_of_cost_derivative(UpperCamelCase					,		UpperCamelCase ) / m
							return cost_derivative_value
def 						snake_case_       ():





							'''simple docstring'''


							global parameter_vector
							# Tune these values to set a tolerance value for predicted output
							_a      	=							0.000002
							_a      	=							0
							_a      	=							0
							while True:
														j += 1
														_a      	=							[0, 0, 0, 0]
														for i in range(0					,		len(UpperCamelCase ) ):
																					_a      	=							get_cost_derivative(i - 1 )
																					_a      	=							(
																					    parameter_vector[i] - LEARNING_RATE * cost_derivative
																					)
														if numpy.allclose(
														    UpperCamelCase					,		UpperCamelCase					,		atol=UpperCamelCase					,		rtol=UpperCamelCase					,		):
																					break
														_a      	=							temp_parameter_vector
							print(('''Number of iterations:''', j) )
def 						snake_case_       ():





							'''simple docstring'''


							for i in range(len(UpperCamelCase ) ):
														print(('''Actual output value:''', output(UpperCamelCase					,		'''test''' )) )
														print(('''Hypothesis output:''', calculate_hypothesis_value(UpperCamelCase					,		'''test''' )) )


if __name__ == "__main__":
				run_gradient_descent()
				print('\nTesting gradient descent for a linear hypothesis function.\n')
				test_gradient_descent()





# 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			:			Union[str, Any]												=					'''tiny-wmt19-en-ru'''

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
	a			:			Union[str, Any]												=					Path(tmpdirname)
	a			:			Dict												=					build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
	a			:			Optional[int]												=					build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
	a			:			Tuple												=					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			:			Optional[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=1000,
    tgt_vocab_size=1000,
    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			:			Dict												=					tokenizer(['''Making tiny model'''], return_tensors='''pt''')
a			:			Union[str, Any]												=					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


