

from typing import TYPE_CHECKING

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


_UpperCAmelCase						:  str									=			{
    """configuration_layoutlmv3""": [
        """LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LayoutLMv3Config""",
        """LayoutLMv3OnnxConfig""",
    ],
    """processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
    """tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  Tuple									=			["""LayoutLMv3TokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  Dict									=			[
					    """LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LayoutLMv3ForQuestionAnswering""",
					    """LayoutLMv3ForSequenceClassification""",
					    """LayoutLMv3ForTokenClassification""",
					    """LayoutLMv3Model""",
					    """LayoutLMv3PreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  Tuple									=			[
					    """TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFLayoutLMv3ForQuestionAnswering""",
					    """TFLayoutLMv3ForSequenceClassification""",
					    """TFLayoutLMv3ForTokenClassification""",
					    """TFLayoutLMv3Model""",
					    """TFLayoutLMv3PreTrainedModel""",
					]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  Optional[int]									=			["""LayoutLMv3FeatureExtractor"""]
					_UpperCAmelCase						:  Tuple									=			["""LayoutLMv3ImageProcessor"""]


if TYPE_CHECKING:
					from .configuration_layoutlmva import (
					    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    LayoutLMvaConfig,
					    LayoutLMvaOnnxConfig,
					)
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_layoutlmva import (
										    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLayoutLMvaForQuestionAnswering,
										    TFLayoutLMvaForSequenceClassification,
										    TFLayoutLMvaForTokenClassification,
										    TFLayoutLMvaModel,
										    TFLayoutLMvaPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
										from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
					import sys

					_UpperCAmelCase						:  Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






"""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

_A     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			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,
    )
)

_A     :			int  						=			FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

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

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

# Save
_A     :			List[Any]  						=			"""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


