


# 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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__lowerCamelCase        =     '''facebook/wmt19-en-de'''
__lowerCamelCase        =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCamelCase        =     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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,
    )
)

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

# Test
__lowerCamelCase        =     tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__lowerCamelCase        =     tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
__lowerCamelCase        =     '''tiny-wmt19-en-de'''
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-de







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCamelCase        =     {
    '''configuration_altclip''': [
        '''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AltCLIPConfig''',
        '''AltCLIPTextConfig''',
        '''AltCLIPVisionConfig''',
    ],
    '''processing_altclip''': ['''AltCLIPProcessor'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''AltCLIPPreTrainedModel''',
           '''AltCLIPModel''',
           '''AltCLIPTextModel''',
           '''AltCLIPVisionModel''',
       ]


if TYPE_CHECKING:
       from .configuration_altclip import (
           ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           AltCLIPConfig,
           AltCLIPTextConfig,
           AltCLIPVisionConfig,
       )
       from .processing_altclip import AltCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_altclip import (
                  ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  AltCLIPModel,
                  AltCLIPPreTrainedModel,
                  AltCLIPTextModel,
                  AltCLIPVisionModel,
              )


else:
       import sys

       __lowerCamelCase        =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



