



"""simple docstring"""






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case   					=				logging.get_logger(__name__)

snake_case   					=				{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	Dict           =  '''sew'''



   def __init__(       self    :	Any							,       __lowerCamelCase    :	Optional[int]=3_2							,       __lowerCamelCase    :	List[Any]=7_6_8							,       __lowerCamelCase    :	Optional[int]=1_2							,       __lowerCamelCase    :	Union[str, Any]=1_2							,       __lowerCamelCase    :	List[Any]=3_0_7_2							,       __lowerCamelCase    :	int=2							,       __lowerCamelCase    :	List[str]="gelu"							,       __lowerCamelCase    :	Dict=0.1							,       __lowerCamelCase    :	str=0.1							,       __lowerCamelCase    :	Tuple=0.1							,       __lowerCamelCase    :	List[Any]=0.0							,       __lowerCamelCase    :	Union[str, Any]=0.1							,       __lowerCamelCase    :	Optional[int]=0.1							,       __lowerCamelCase    :	Any=0.0_2							,       __lowerCamelCase    :	Dict=1E-5							,       __lowerCamelCase    :	Dict="group"							,       __lowerCamelCase    :	List[str]="gelu"							,       __lowerCamelCase    :	Union[str, Any]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)							,       __lowerCamelCase    :	int=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)							,       __lowerCamelCase    :	Tuple=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)							,       __lowerCamelCase    :	Optional[int]=False							,       __lowerCamelCase    :	str=1_2_8							,       __lowerCamelCase    :	Tuple=1_6							,       __lowerCamelCase    :	Optional[Any]=True							,       __lowerCamelCase    :	Optional[Any]=0.0_5							,       __lowerCamelCase    :	int=1_0							,       __lowerCamelCase    :	Union[str, Any]=2							,       __lowerCamelCase    :	Tuple=0.0							,       __lowerCamelCase    :	int=1_0							,       __lowerCamelCase    :	Union[str, Any]=0							,       __lowerCamelCase    :	List[str]="mean"							,       __lowerCamelCase    :	Union[str, Any]=False							,       __lowerCamelCase    :	Any=False							,       __lowerCamelCase    :	int=2_5_6							,       __lowerCamelCase    :	Tuple=0							,       __lowerCamelCase    :	str=1							,       __lowerCamelCase    :	Optional[int]=2							,       **__lowerCamelCase    :	Optional[int]							,       ):



     """simple docstring"""
     super().__init__(**__lowerCamelCase							,       pad_token_id=__lowerCamelCase							,       bos_token_id=__lowerCamelCase							,       eos_token_id=__lowerCamelCase				)
     _snake_case   	=      hidden_size
     _snake_case   	=      feat_extract_norm
     _snake_case   	=      feat_extract_activation
     _snake_case   	=      list(__lowerCamelCase				)
     _snake_case   	=      list(__lowerCamelCase				)
     _snake_case   	=      list(__lowerCamelCase				)
     _snake_case   	=      conv_bias
     _snake_case   	=      num_conv_pos_embeddings
     _snake_case   	=      num_conv_pos_embedding_groups
     _snake_case   	=      len(self.conv_dim				)
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      intermediate_size
     _snake_case   	=      squeeze_factor
     _snake_case   	=      hidden_act
     _snake_case   	=      num_attention_heads
     _snake_case   	=      hidden_dropout
     _snake_case   	=      attention_dropout
     _snake_case   	=      activation_dropout
     _snake_case   	=      feat_proj_dropout
     _snake_case   	=      final_dropout
     _snake_case   	=      layerdrop
     _snake_case   	=      layer_norm_eps
     _snake_case   	=      initializer_range
     _snake_case   	=      vocab_size

     if (
         (len(self.conv_stride				) != self.num_feat_extract_layers)
         or (len(self.conv_kernel				) != self.num_feat_extract_layers)
         or (len(self.conv_dim				) != self.num_feat_extract_layers)
     ):
       raise ValueError(
           '''Configuration for convolutional layers is incorrect.'''
           '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
           f"""but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)"""
           f"""= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`."""				)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     _snake_case   	=      apply_spec_augment
     _snake_case   	=      mask_time_prob
     _snake_case   	=      mask_time_length
     _snake_case   	=      mask_time_min_masks
     _snake_case   	=      mask_feature_prob
     _snake_case   	=      mask_feature_length
     _snake_case   	=      mask_feature_min_masks

     # ctc loss
     _snake_case   	=      ctc_loss_reduction
     _snake_case   	=      ctc_zero_infinity

     # sequence classification
     _snake_case   	=      use_weighted_layer_sum
     _snake_case   	=      classifier_proj_size





   @property
   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     return functools.reduce(operator.mul							,       self.conv_stride							,       1				)










"""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





