





'''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 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 :					int 							='''facebook/wmt19-en-de'''
lowerCAmelCase :					int 							=FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
lowerCAmelCase :					List[Any] 							=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 :					List[str] 							=FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
lowerCAmelCase :					Tuple 							=tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCAmelCase :					Union[str, Any] 							=tiny_model(**batch)

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

# Save
lowerCAmelCase :					Optional[int] 							='''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









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase :					int 							={
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Any 							=['''CLIPTokenizerFast''']

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					str 							=['''CLIPFeatureExtractor''']
 lowerCAmelCase :					Optional[int] 							=['''CLIPImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Any 							=[
     '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CLIPModel''',
     '''CLIPPreTrainedModel''',
     '''CLIPTextModel''',
     '''CLIPTextModelWithProjection''',
     '''CLIPVisionModel''',
     '''CLIPVisionModelWithProjection''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Union[str, Any] 							=[
     '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFCLIPModel''',
     '''TFCLIPPreTrainedModel''',
     '''TFCLIPTextModel''',
     '''TFCLIPVisionModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					List[Any] 							=[
     '''FlaxCLIPModel''',
     '''FlaxCLIPPreTrainedModel''',
     '''FlaxCLIPTextModel''',
     '''FlaxCLIPTextPreTrainedModel''',
     '''FlaxCLIPVisionModel''',
     '''FlaxCLIPVisionPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_clip import (
     CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     CLIPConfig,
     CLIPOnnxConfig,
     CLIPTextConfig,
     CLIPVisionConfig,
 )
 from .processing_clip import CLIPProcessor
 from .tokenization_clip import CLIPTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_clip_fast import CLIPTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_clip import CLIPFeatureExtractor
  from .image_processing_clip import CLIPImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_clip import (
      CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      CLIPModel,
      CLIPPreTrainedModel,
      CLIPTextModel,
      CLIPTextModelWithProjection,
      CLIPVisionModel,
      CLIPVisionModelWithProjection,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_clip import (
      TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCLIPModel,
      TFCLIPPreTrainedModel,
      TFCLIPTextModel,
      TFCLIPVisionModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_clip import (
      FlaxCLIPModel,
      FlaxCLIPPreTrainedModel,
      FlaxCLIPTextModel,
      FlaxCLIPTextPreTrainedModel,
      FlaxCLIPVisionModel,
      FlaxCLIPVisionPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase :					int 							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


