'''simple docstring'''





from pathlib import Path
import json
import tempfile

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

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

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
    SCREAMING_SNAKE_CASE_:					List[str]					=Path(tmpdirname)
    SCREAMING_SNAKE_CASE_:					int					=build_dir / VOCAB_FILES_NAMES['src_vocab_file']
    SCREAMING_SNAKE_CASE_:					Tuple					=build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
    SCREAMING_SNAKE_CASE_:					Optional[int]					=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))

    SCREAMING_SNAKE_CASE_:					List[str]					=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,
    )

SCREAMING_SNAKE_CASE_:					int					=FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=10_00,
    tgt_vocab_size=10_00,
    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_:					int					=FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")

# Test
SCREAMING_SNAKE_CASE_:					int					=tokenizer(['Making tiny model'], return_tensors='pt')
SCREAMING_SNAKE_CASE_:					Optional[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





'''simple docstring'''





from random import randint
from tempfile import TemporaryFile

import numpy as np





def      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			Any ,   snake_case_	:			int      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       0
     if start < end:
          UpperCAmelCase_	   =       randint(snake_case_ ,   snake_case_      )
          UpperCAmelCase_	   =       a[end]
          UpperCAmelCase_	   =       a[pivot]
          UpperCAmelCase_	   =       temp

          UpperCAmelCase_   ,			UpperCAmelCase_	   =       _in_place_partition(snake_case_ ,   snake_case_ ,   snake_case_      )
          count += _in_place_quick_sort(snake_case_ ,   snake_case_ ,   p - 1      )
          count += _in_place_quick_sort(snake_case_ ,   p + 1 ,   snake_case_      )
     return count





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			Dict ,   snake_case_	:			List[str]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       randint(snake_case_ ,   snake_case_      )
     UpperCAmelCase_	   =       a[end]
     UpperCAmelCase_	   =       a[pivot]
     UpperCAmelCase_	   =       temp
     UpperCAmelCase_	   =       start - 1
     for index in range(snake_case_ ,   snake_case_      ):
          count += 1
          if a[index] < a[end]:  # check if current val is less than pivot value
               UpperCAmelCase_	   =       new_pivot_index + 1
               UpperCAmelCase_	   =       a[new_pivot_index]
               UpperCAmelCase_	   =       a[index]
               UpperCAmelCase_	   =       temp

     UpperCAmelCase_	   =       a[new_pivot_index + 1]
     UpperCAmelCase_	   =       a[end]
     UpperCAmelCase_	   =       temp
     return new_pivot_index + 1, count


SCREAMING_SNAKE_CASE_:					List[str]  =TemporaryFile()
SCREAMING_SNAKE_CASE_:					int  =1_00  # 1000 elements are to be sorted


SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_:					str  =0, 1  # mean and standard deviation
SCREAMING_SNAKE_CASE_:					List[str]  =np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
SCREAMING_SNAKE_CASE_:					str  =np.load(outfile)
SCREAMING_SNAKE_CASE_:					List[Any]  =len(M) - 1
SCREAMING_SNAKE_CASE_:					Dict  =_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)






print(z)




