



import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
							__snake_case    :		Union[str, Any]  =argparse.ArgumentParser(
							    description=(
							        'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
							        ' Distillation'
							    )
							)
							parser.add_argument('--model_type', default='bert', choices=['bert'])
							parser.add_argument('--model_name', default='bert-base-uncased', type=str)
							parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
							parser.add_argument('--vocab_transform', action='store_true')
							__snake_case    :		Union[str, Any]  =parser.parse_args()

							if args.model_type == "bert":
														__snake_case    :		Tuple  =BertForMaskedLM.from_pretrained(args.model_name)
														__snake_case    :		Dict  ='bert'
							else:
														raise ValueError('args.model_type should be "bert".')

							__snake_case    :		Optional[Any]  =model.state_dict()
							__snake_case    :		List[str]  ={}

							for w in ["word_embeddings", "position_embeddings"]:
														__snake_case    :		List[Any]  =state_dict[f"""{prefix}.embeddings.{w}.weight"""]
							for w in ["weight", "bias"]:
														__snake_case    :		Any  =state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

							__snake_case    :		List[Any]  =0
							for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
														for w in ["weight", "bias"]:
																					__snake_case    :		Union[str, Any]  =state_dict[
																					    f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
																					]
																					__snake_case    :		Any  =state_dict[
																					    f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
																					]
																					__snake_case    :		List[Any]  =state_dict[
																					    f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
																					]

																					__snake_case    :		Tuple  =state_dict[
																					    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
																					]
																					__snake_case    :		str  =state_dict[
																					    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
																					]

																					__snake_case    :		Union[str, Any]  =state_dict[
																					    f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
																					]
																					__snake_case    :		Optional[Any]  =state_dict[
																					    f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
																					]
																					__snake_case    :		Tuple  =state_dict[
																					    f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
																					]
														std_idx += 1

							__snake_case    :		Tuple  =state_dict['cls.predictions.decoder.weight']
							__snake_case    :		Optional[int]  =state_dict['cls.predictions.bias']
							if args.vocab_transform:
														for w in ["weight", "bias"]:
																					__snake_case    :		Dict  =state_dict[f"""cls.predictions.transform.dense.{w}"""]
																					__snake_case    :		Optional[int]  =state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

							print(f"""N layers selected for distillation: {std_idx}""")
							print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

							print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
							torch.save(compressed_sd, args.dump_checkpoint)






from typing import TYPE_CHECKING

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


__snake_case    :		Any  ={
    'configuration_blenderbot_small': [
        'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotSmallConfig',
        'BlenderbotSmallOnnxConfig',
    ],
    'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Union[str, Any]  =['BlenderbotSmallTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =[
							    'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BlenderbotSmallForCausalLM',
							    'BlenderbotSmallForConditionalGeneration',
							    'BlenderbotSmallModel',
							    'BlenderbotSmallPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		str  =[
							    'TFBlenderbotSmallForConditionalGeneration',
							    'TFBlenderbotSmallModel',
							    'TFBlenderbotSmallPreTrainedModel',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Dict  =[
							    'FlaxBlenderbotSmallForConditionalGeneration',
							    'FlaxBlenderbotSmallModel',
							    'FlaxBlenderbotSmallPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_blenderbot_small import (
							    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotSmallConfig,
							    BlenderbotSmallOnnxConfig,
							)
							from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot_small import (
														    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotSmallForCausalLM,
														    BlenderbotSmallForConditionalGeneration,
														    BlenderbotSmallModel,
														    BlenderbotSmallPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot_small import (
														    TFBlenderbotSmallForConditionalGeneration,
														    TFBlenderbotSmallModel,
														    TFBlenderbotSmallPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot_small import (
														    FlaxBlenderbotSmallForConditionalGeneration,
														    FlaxBlenderbotSmallModel,
														    FlaxBlenderbotSmallPreTrainedModel,
														)

else:
							import sys

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

