


# 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

UpperCAmelCase      =   'tiny-wmt19-en-ru'

# Build

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

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

   UpperCAmelCase      =   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,
   )

UpperCAmelCase      =   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,
)

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

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



import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''a''', '''b''', '''c''']

       # Defaults to last layer if both are None
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[2] )

       # Out indices set to match out features
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(['''a''', '''c''']		,				_UpperCAmelCase		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[0, 2] )

       # Out features set to match out indices
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				[0, 2]		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[0, 2] )

       # Out features selected from negative indices
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				[-3, -1]		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[-3, -1] )



 def 						UpperCamelCase__					(     self ):
       # Stage names must be set
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 1)		,				_UpperCAmelCase )

       # Out features must be a list
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(('''a''', '''b''')		,				(0, 1)		,				['''a''', '''b'''] )

       # Out features must be a subset of stage names
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 1)		,				['''a'''] )

       # Out indices must be a list or tuple
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(_UpperCAmelCase		,				0		,				['''a''', '''b'''] )

       # Out indices must be a subset of stage names
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(_UpperCAmelCase		,				(0, 1)		,				['''a'''] )

       # Out features and out indices must be the same length
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0,)		,				['''a''', '''b''', '''c'''] )

       # Out features should match out indices
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 2)		,				['''a''', '''b''', '''c'''] )

       # Out features and out indices should be in order
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''b''', '''a''']		,				(0, 1)		,				['''a''', '''b'''] )

       # Check passes with valid inputs
       verify_out_features_out_indices(['''a''', '''b''', '''d''']		,				(0, 1, -1)		,				['''a''', '''b''', '''c''', '''d'''] )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				BackboneMixin()

       snake_case_	=				['''a''', '''b''', '''c''']
       snake_case_	=				['''a''', '''c''']
       snake_case_	=				[0, 2]

       # Check that the output features and indices are set correctly
       self.assertEqual(backbone.out_features		,				['''a''', '''c'''] )
       self.assertEqual(backbone.out_indices		,				[0, 2] )

       # Check out features and indices are updated correctly
       snake_case_	=				['''a''', '''b''']
       self.assertEqual(backbone.out_features		,				['''a''', '''b'''] )
       self.assertEqual(backbone.out_indices		,				[0, 1] )

       snake_case_	=				[-3, -1]
       self.assertEqual(backbone.out_features		,				['''a''', '''c'''] )
       self.assertEqual(backbone.out_indices		,				[-3, -1] )