


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

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

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

# Save
lowerCAmelCase   : 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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase   : List[Any]             =				{
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Any             =				["""AlbertTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Optional[Any]             =				["""AlbertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : List[str]             =				[
					    """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """AlbertForMaskedLM""",
					    """AlbertForMultipleChoice""",
					    """AlbertForPreTraining""",
					    """AlbertForQuestionAnswering""",
					    """AlbertForSequenceClassification""",
					    """AlbertForTokenClassification""",
					    """AlbertModel""",
					    """AlbertPreTrainedModel""",
					    """load_tf_weights_in_albert""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Tuple             =				[
					    """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFAlbertForMaskedLM""",
					    """TFAlbertForMultipleChoice""",
					    """TFAlbertForPreTraining""",
					    """TFAlbertForQuestionAnswering""",
					    """TFAlbertForSequenceClassification""",
					    """TFAlbertForTokenClassification""",
					    """TFAlbertMainLayer""",
					    """TFAlbertModel""",
					    """TFAlbertPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Optional[int]             =				[
					    """FlaxAlbertForMaskedLM""",
					    """FlaxAlbertForMultipleChoice""",
					    """FlaxAlbertForPreTraining""",
					    """FlaxAlbertForQuestionAnswering""",
					    """FlaxAlbertForSequenceClassification""",
					    """FlaxAlbertForTokenClassification""",
					    """FlaxAlbertModel""",
					    """FlaxAlbertPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert import AlbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert_fast import AlbertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_albert import (
										    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AlbertForMaskedLM,
										    AlbertForMultipleChoice,
										    AlbertForPreTraining,
										    AlbertForQuestionAnswering,
										    AlbertForSequenceClassification,
										    AlbertForTokenClassification,
										    AlbertModel,
										    AlbertPreTrainedModel,
										    load_tf_weights_in_albert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_albert import (
										    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAlbertForMaskedLM,
										    TFAlbertForMultipleChoice,
										    TFAlbertForPreTraining,
										    TFAlbertForQuestionAnswering,
										    TFAlbertForSequenceClassification,
										    TFAlbertForTokenClassification,
										    TFAlbertMainLayer,
										    TFAlbertModel,
										    TFAlbertPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_albert import (
										    FlaxAlbertForMaskedLM,
										    FlaxAlbertForMultipleChoice,
										    FlaxAlbertForPreTraining,
										    FlaxAlbertForQuestionAnswering,
										    FlaxAlbertForSequenceClassification,
										    FlaxAlbertForTokenClassification,
										    FlaxAlbertModel,
										    FlaxAlbertPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase   : Union[str, Any]             =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


