


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
						__lowerCAmelCase	: Optional[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')
						__lowerCAmelCase	: str  				=parser.parse_args()

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

						__lowerCAmelCase	: Tuple  				=model.state_dict()
						__lowerCAmelCase	: Dict  				={}

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

						__lowerCAmelCase	: Dict  				=0
						for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
												for w in ["weight", "bias"]:
																		__lowerCAmelCase	: Optional[Any]  				=state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
																		]
																		__lowerCAmelCase	: Tuple  				=state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
																		]
																		__lowerCAmelCase	: int  				=state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
																		]

																		__lowerCAmelCase	: List[Any]  				=state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
																		]
																		__lowerCAmelCase	: Optional[Any]  				=state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
																		]

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

						__lowerCAmelCase	: str  				=state_dict['cls.predictions.decoder.weight']
						__lowerCAmelCase	: str  				=state_dict['cls.predictions.bias']
						if args.vocab_transform:
												for w in ["weight", "bias"]:
																		__lowerCAmelCase	: List[Any]  				=state_dict[f"""cls.predictions.transform.dense.{w}"""]
																		__lowerCAmelCase	: 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)




import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
						from transformers import MarkupLMFeatureExtractor
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def __init__(       self			:Optional[Any]			,			lowerCAmelCase__			:Optional[Any]	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		parent



				def 		__magic_name__(       self			:List[Any]	)		->			Tuple:
										return {}




def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		'''<HTML>

    <HEAD>
    <TITLE>sample document</TITLE>
    </HEAD>

    <BODY BGCOLOR="FFFFFF">
    <HR>
    <a href="http://google.com">Goog</a>
    <H1>This is one header</H1>
    <H2>This is a another Header</H2>
    <P>Travel from
        <P>
        <B>SFO to JFK</B>
        <BR>
        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
        <HR>
        <div style="color:#0000FF">
            <h3>Traveler <b> name </b> is
            <p> John Doe </p>
        </div>'''

						__SCREAMING_SNAKE_CASE						:     str					=		'''
    <!DOCTYPE html>
    <html>
    <body>

    <h1>My First Heading</h1>
    <p>My first paragraph.</p>

    </body>
    </html>
    '''

						return [html_string_a, html_string_a]
@require_bsa
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		MarkupLMFeatureExtractor if is_bsa_available() else None


				def 		__magic_name__(       self			:int	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		MarkupLMFeatureExtractionTester(self	)


				@property
				def 		__magic_name__(       self			:Any	)		->			Optional[Any]:
										return self.feature_extract_tester.prepare_feat_extract_dict()



				def 		__magic_name__(       self			:Optional[int]	)		->			Any:
										# Initialize feature_extractor
										__SCREAMING_SNAKE_CASE						:     int					=		self.feature_extraction_class()

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Tuple					=		get_html_strings()[0]
										__SCREAMING_SNAKE_CASE						:     Dict					=		feature_extractor(lowerCAmelCase__	)

										# fmt: off
										__SCREAMING_SNAKE_CASE						:     str					=		[['''sample document''', '''Goog''', '''This is one header''', '''This is a another Header''', '''Travel from''', '''SFO to JFK''', '''on May 2, 2015 at 2:00 pm. For details go to confirm.com''', '''Traveler''', '''name''', '''is''', '''John Doe''']]
										__SCREAMING_SNAKE_CASE						:     List[str]					=		[['''/html/head/title''', '''/html/body/a''', '''/html/body/h1''', '''/html/body/h2''', '''/html/body/p''', '''/html/body/p/p/b[1]''', '''/html/body/p/p/b[2]/i''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/b''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/p''']]
										# fmt: on

										self.assertEqual(encoding.nodes			,			lowerCAmelCase__	)
										self.assertEqual(encoding.xpaths			,			lowerCAmelCase__	)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Tuple					=		get_html_strings()
										__SCREAMING_SNAKE_CASE						:     Dict					=		feature_extractor(lowerCAmelCase__	)

										# fmt: off
										__SCREAMING_SNAKE_CASE						:     int					=		expected_nodes + [['''My First Heading''', '''My first paragraph.''']]
										__SCREAMING_SNAKE_CASE						:     str					=		expected_xpaths + [['''/html/body/h1''', '''/html/body/p''']]

										self.assertEqual(len(encoding.nodes	)			,			2	)
										self.assertEqual(len(encoding.xpaths	)			,			2	)

										self.assertEqual(encoding.nodes			,			lowerCAmelCase__	)
										self.assertEqual(encoding.xpaths			,			lowerCAmelCase__	)
