



'''simple docstring'''



import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


lowerCAmelCase_     :       int								=						get_tests_dir('fixtures/dummy_feature_extractor_config.json')
lowerCAmelCase_     :       Optional[int]								=						get_tests_dir('fixtures/vocab.json')
lowerCAmelCase_     :       str								=						get_tests_dir('fixtures')
class 	lowerCamelCase_							(					unittest.TestCase			):
						_lowerCAmelCase     :   str	     =				['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			0







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''				)
													self.assertIsInstance(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													with tempfile.TemporaryDirectory() as tmpdirname:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			WavaVecaConfig()
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''				)

																				# save in new folder
																				model_config.save_pretrained(lowerCAmelCase__				)
																				processor.save_pretrained(lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			AutoProcessor.from_pretrained(lowerCAmelCase__				)

													self.assertIsInstance(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													with tempfile.TemporaryDirectory() as tmpdirname:
																				# copy relevant files
																				copyfile(lowerCAmelCase__				,    os.path.join(lowerCAmelCase__				,    lowerCAmelCase__				)				)
																				copyfile(lowerCAmelCase__				,    os.path.join(lowerCAmelCase__				,    '''vocab.json'''				)				)

																				SCREAMING_SNAKE_CASE					:      Tuple     					=			AutoProcessor.from_pretrained(lowerCAmelCase__				)

													self.assertIsInstance(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													with tempfile.TemporaryDirectory() as tmpdirname:
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			WavaVecaFeatureExtractor()
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''				)

																				SCREAMING_SNAKE_CASE					:      int     					=			WavaVecaProcessor(lowerCAmelCase__				,    lowerCAmelCase__				)

																				# save in new folder
																				processor.save_pretrained(lowerCAmelCase__				)

																				# drop `processor_class` in tokenizer
																				with open(os.path.join(lowerCAmelCase__				,    lowerCAmelCase__				)				,    '''r'''				) as f:
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			json.load(lowerCAmelCase__				)
																											config_dict.pop('''processor_class'''				)

																				with open(os.path.join(lowerCAmelCase__				,    lowerCAmelCase__				)				,    '''w'''				) as f:
																											f.write(json.dumps(lowerCAmelCase__				)				)

																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			AutoProcessor.from_pretrained(lowerCAmelCase__				)

													self.assertIsInstance(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													with tempfile.TemporaryDirectory() as tmpdirname:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			WavaVecaFeatureExtractor()
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''				)

																				SCREAMING_SNAKE_CASE					:      int     					=			WavaVecaProcessor(lowerCAmelCase__				,    lowerCAmelCase__				)

																				# save in new folder
																				processor.save_pretrained(lowerCAmelCase__				)

																				# drop `processor_class` in feature extractor
																				with open(os.path.join(lowerCAmelCase__				,    lowerCAmelCase__				)				,    '''r'''				) as f:
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			json.load(lowerCAmelCase__				)
																											config_dict.pop('''processor_class'''				)

																				with open(os.path.join(lowerCAmelCase__				,    lowerCAmelCase__				)				,    '''w'''				) as f:
																											f.write(json.dumps(lowerCAmelCase__				)				)

																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			AutoProcessor.from_pretrained(lowerCAmelCase__				)

													self.assertIsInstance(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													with tempfile.TemporaryDirectory() as tmpdirname:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			WavaVecaConfig(processor_class='''Wav2Vec2Processor'''				)
																				model_config.save_pretrained(lowerCAmelCase__				)
																				# copy relevant files
																				copyfile(lowerCAmelCase__				,    os.path.join(lowerCAmelCase__				,    '''vocab.json'''				)				)
																				# create emtpy sample processor
																				with open(os.path.join(lowerCAmelCase__				,    lowerCAmelCase__				)				,    '''w'''				) as f:
																											f.write('''{}'''				)

																				SCREAMING_SNAKE_CASE					:      List[str]     					=			AutoProcessor.from_pretrained(lowerCAmelCase__				)

													self.assertIsInstance(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													# If remote code is not set, we will time out when asking whether to load the model.
													with self.assertRaises(lowerCAmelCase__				):
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''				)
													# If remote code is disabled, we can't load this config.
													with self.assertRaises(lowerCAmelCase__				):
																				SCREAMING_SNAKE_CASE					:      Dict     					=			AutoProcessor.from_pretrained(
																				    '''hf-internal-testing/test_dynamic_processor'''				,    trust_remote_code=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''				,    trust_remote_code=lowerCAmelCase__				)
													self.assertTrue(processor.special_attribute_present				)
													self.assertEqual(processor.__class__.__name__				,    '''NewProcessor'''				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			processor.feature_extractor
													self.assertTrue(feature_extractor.special_attribute_present				)
													self.assertEqual(feature_extractor.__class__.__name__				,    '''NewFeatureExtractor'''				)

													SCREAMING_SNAKE_CASE					:      Any     					=			processor.tokenizer
													self.assertTrue(tokenizer.special_attribute_present				)
													if is_tokenizers_available():
																				self.assertEqual(tokenizer.__class__.__name__				,    '''NewTokenizerFast'''				)

																				# Test we can also load the slow version
																				SCREAMING_SNAKE_CASE					:      str     					=			AutoProcessor.from_pretrained(
																				    '''hf-internal-testing/test_dynamic_processor'''				,    trust_remote_code=lowerCAmelCase__				,    use_fast=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			new_processor.tokenizer
																				self.assertTrue(new_tokenizer.special_attribute_present				)
																				self.assertEqual(new_tokenizer.__class__.__name__				,    '''NewTokenizer'''				)
													else:
																				self.assertEqual(tokenizer.__class__.__name__				,    '''NewTokenizer'''				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													try:
																				AutoConfig.register('''custom'''				,    lowerCAmelCase__				)
																				AutoFeatureExtractor.register(lowerCAmelCase__				,    lowerCAmelCase__				)
																				AutoTokenizer.register(lowerCAmelCase__				,    slow_tokenizer_class=lowerCAmelCase__				)
																				AutoProcessor.register(lowerCAmelCase__				,    lowerCAmelCase__				)
																				# Trying to register something existing in the Transformers library will raise an error
																				with self.assertRaises(lowerCAmelCase__				):
																											AutoProcessor.register(lowerCAmelCase__				,    lowerCAmelCase__				)

																				# Now that the config is registered, it can be used as any other config with the auto-API
																				SCREAMING_SNAKE_CASE					:      Dict     					=			CustomFeatureExtractor.from_pretrained(lowerCAmelCase__				)

																				with tempfile.TemporaryDirectory() as tmp_dir:
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			os.path.join(lowerCAmelCase__				,    '''vocab.txt'''				)
																											with open(lowerCAmelCase__				,    '''w'''				,    encoding='''utf-8'''				) as vocab_writer:
																																		vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]				)				)
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			CustomTokenizer(lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			CustomProcessor(lowerCAmelCase__				,    lowerCAmelCase__				)

																				with tempfile.TemporaryDirectory() as tmp_dir:
																											processor.save_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			AutoProcessor.from_pretrained(lowerCAmelCase__				)
																											self.assertIsInstance(lowerCAmelCase__				,    lowerCAmelCase__				)

													finally:
																				if "custom" in CONFIG_MAPPING._extra_content:
																											del CONFIG_MAPPING._extra_content["custom"]
																				if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																											del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
																				if CustomConfig in TOKENIZER_MAPPING._extra_content:
																											del TOKENIZER_MAPPING._extra_content[CustomConfig]
																				if CustomConfig in PROCESSOR_MAPPING._extra_content:
																											del PROCESSOR_MAPPING._extra_content[CustomConfig]







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													class 	lowerCamelCase_							(					snake_case_			):
																			_lowerCAmelCase     :   Optional[int]	     =				False


													class 	lowerCamelCase_							(					snake_case_			):
																			_lowerCAmelCase     :   int	     =				False


													class 	lowerCamelCase_							(					snake_case_			):
																			_lowerCAmelCase     :   str	     =				'AutoFeatureExtractor'
																			_lowerCAmelCase     :   Tuple	     =				'AutoTokenizer'
																			_lowerCAmelCase     :   int	     =				False

													try:
																				AutoConfig.register('''custom'''				,    lowerCAmelCase__				)
																				AutoFeatureExtractor.register(lowerCAmelCase__				,    lowerCAmelCase__				)
																				AutoTokenizer.register(lowerCAmelCase__				,    slow_tokenizer_class=lowerCAmelCase__				)
																				AutoProcessor.register(lowerCAmelCase__				,    lowerCAmelCase__				)
																				# If remote code is not set, the default is to use local classes.
																				SCREAMING_SNAKE_CASE					:      str     					=			AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''				)
																				self.assertEqual(processor.__class__.__name__				,    '''NewProcessor'''				)
																				self.assertFalse(processor.special_attribute_present				)
																				self.assertFalse(processor.feature_extractor.special_attribute_present				)
																				self.assertFalse(processor.tokenizer.special_attribute_present				)

																				# If remote code is disabled, we load the local ones.
																				SCREAMING_SNAKE_CASE					:      str     					=			AutoProcessor.from_pretrained(
																				    '''hf-internal-testing/test_dynamic_processor'''				,    trust_remote_code=lowerCAmelCase__				)
																				self.assertEqual(processor.__class__.__name__				,    '''NewProcessor'''				)
																				self.assertFalse(processor.special_attribute_present				)
																				self.assertFalse(processor.feature_extractor.special_attribute_present				)
																				self.assertFalse(processor.tokenizer.special_attribute_present				)

																				# If remote is enabled, we load from the Hub.
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			AutoProcessor.from_pretrained(
																				    '''hf-internal-testing/test_dynamic_processor'''				,    trust_remote_code=lowerCAmelCase__				)
																				self.assertEqual(processor.__class__.__name__				,    '''NewProcessor'''				)
																				self.assertTrue(processor.special_attribute_present				)
																				self.assertTrue(processor.feature_extractor.special_attribute_present				)
																				self.assertTrue(processor.tokenizer.special_attribute_present				)

													finally:
																				if "custom" in CONFIG_MAPPING._extra_content:
																											del CONFIG_MAPPING._extra_content["custom"]
																				if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																											del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
																				if CustomConfig in TOKENIZER_MAPPING._extra_content:
																											del TOKENIZER_MAPPING._extra_content[CustomConfig]
																				if CustomConfig in PROCESSOR_MAPPING._extra_content:
																											del PROCESSOR_MAPPING._extra_content[CustomConfig]







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''				)
													self.assertEqual(processor.__class__.__name__				,    '''BertTokenizerFast'''				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''				)
													self.assertEqual(processor.__class__.__name__				,    '''ConvNextImageProcessor'''				)


@is_staging_test
class 	lowerCamelCase_							(					unittest.TestCase			):
						_lowerCAmelCase     :   Union[str, Any]	     =				['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']







						@classmethod
						def 					__lowercase    (  cls   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			TOKEN
													HfFolder.save_token(lowerCAmelCase__				)







						@classmethod
						def 					__lowercase    (  cls   :      Dict				):

													"""simple docstring"""
													try:
																				delete_repo(token=cls._token				,    repo_id='''test-processor'''				)
													except HTTPError:
																				pass

													try:
																				delete_repo(token=cls._token				,    repo_id='''valid_org/test-processor-org'''				)
													except HTTPError:
																				pass

													try:
																				delete_repo(token=cls._token				,    repo_id='''test-dynamic-processor'''				)
													except HTTPError:
																				pass







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			WavaVecaProcessor.from_pretrained(lowerCAmelCase__				)
													with tempfile.TemporaryDirectory() as tmp_dir:
																				processor.save_pretrained(
																				    os.path.join(lowerCAmelCase__				,    '''test-processor'''				)				,    push_to_hub=lowerCAmelCase__				,    use_auth_token=self._token				)

																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			WavaVecaProcessor.from_pretrained(F"""{USER}/test-processor"""				)
																				for k, v in processor.feature_extractor.__dict__.items():
																											self.assertEqual(lowerCAmelCase__				,    getattr(new_processor.feature_extractor				,    lowerCAmelCase__				)				)
																				self.assertDictEqual(new_processor.tokenizer.get_vocab()				,    processor.tokenizer.get_vocab()				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			WavaVecaProcessor.from_pretrained(lowerCAmelCase__				)

													with tempfile.TemporaryDirectory() as tmp_dir:
																				processor.save_pretrained(
																				    os.path.join(lowerCAmelCase__				,    '''test-processor-org'''				)				,    push_to_hub=lowerCAmelCase__				,    use_auth_token=self._token				,    organization='''valid_org'''				,    )

																				SCREAMING_SNAKE_CASE					:      Dict     					=			WavaVecaProcessor.from_pretrained('''valid_org/test-processor-org'''				)
																				for k, v in processor.feature_extractor.__dict__.items():
																											self.assertEqual(lowerCAmelCase__				,    getattr(new_processor.feature_extractor				,    lowerCAmelCase__				)				)
																				self.assertDictEqual(new_processor.tokenizer.get_vocab()				,    processor.tokenizer.get_vocab()				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													CustomFeatureExtractor.register_for_auto_class()
													CustomTokenizer.register_for_auto_class()
													CustomProcessor.register_for_auto_class()

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			CustomFeatureExtractor.from_pretrained(lowerCAmelCase__				)

													with tempfile.TemporaryDirectory() as tmp_dir:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			os.path.join(lowerCAmelCase__				,    '''vocab.txt'''				)
																				with open(lowerCAmelCase__				,    '''w'''				,    encoding='''utf-8'''				) as vocab_writer:
																											vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]				)				)
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			CustomTokenizer(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			CustomProcessor(lowerCAmelCase__				,    lowerCAmelCase__				)

													with tempfile.TemporaryDirectory() as tmp_dir:
																				create_repo(F"""{USER}/test-dynamic-processor"""				,    token=self._token				)
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			Repository(lowerCAmelCase__				,    clone_from=F"""{USER}/test-dynamic-processor"""				,    token=self._token				)
																				processor.save_pretrained(lowerCAmelCase__				)

																				# This has added the proper auto_map field to the feature extractor config
																				self.assertDictEqual(
																				    processor.feature_extractor.auto_map				,    {
																				        '''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor''',
																				        '''AutoProcessor''': '''custom_processing.CustomProcessor''',
																				    }				,    )

																				# This has added the proper auto_map field to the tokenizer config
																				with open(os.path.join(lowerCAmelCase__				,    '''tokenizer_config.json'''				)				) as f:
																											SCREAMING_SNAKE_CASE					:      Any     					=			json.load(lowerCAmelCase__				)
																				self.assertDictEqual(
																				    tokenizer_config['''auto_map''']				,    {
																				        '''AutoTokenizer''': ['''custom_tokenization.CustomTokenizer''', None],
																				        '''AutoProcessor''': '''custom_processing.CustomProcessor''',
																				    }				,    )

																				# The code has been copied from fixtures
																				self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__				,    '''custom_feature_extraction.py'''				)				)				)
																				self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__				,    '''custom_tokenization.py'''				)				)				)
																				self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__				,    '''custom_processing.py'''				)				)				)

																				repo.push_to_hub()

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			AutoProcessor.from_pretrained(F"""{USER}/test-dynamic-processor"""				,    trust_remote_code=lowerCAmelCase__				)
													# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
													self.assertEqual(new_processor.__class__.__name__				,    '''CustomProcessor'''				)









'''simple docstring'''



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowerCAmelCase_     :       Any								=						argparse.ArgumentParser(
							    description=(
							        'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
							        ' Distillation'
							    )
							)
							parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
							parser.add_argument('--model_name', default='roberta-large', type=str)
							parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
							parser.add_argument('--vocab_transform', action='store_true')
							lowerCAmelCase_     :       Dict								=						parser.parse_args()

							if args.model_type == "roberta":
														lowerCAmelCase_     :       str								=						RobertaForMaskedLM.from_pretrained(args.model_name)
														lowerCAmelCase_     :       Tuple								=						'roberta'
							elif args.model_type == "gpt2":
														lowerCAmelCase_     :       List[Any]								=						GPTaLMHeadModel.from_pretrained(args.model_name)
														lowerCAmelCase_     :       Optional[Any]								=						'transformer'

							lowerCAmelCase_     :       List[Any]								=						model.state_dict()
							lowerCAmelCase_     :       List[Any]								=						{}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					lowerCAmelCase_     :       List[str]								=						state_dict[f'{prefix}.{param_name}']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					lowerCAmelCase_     :       Any								=						f'{prefix}.embeddings.{w}.weight'
																					lowerCAmelCase_     :       Any								=						state_dict[param_name]
														for w in ["weight", "bias"]:
																					lowerCAmelCase_     :       str								=						f'{prefix}.embeddings.LayerNorm.{w}'
																					lowerCAmelCase_     :       str								=						state_dict[param_name]

    # Transformer Blocks #
							lowerCAmelCase_     :       List[str]								=						0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			lowerCAmelCase_     :       List[str]								=						state_dict[
																																			    f'{prefix}.h.{teacher_idx}.{layer}.{w}'
																																			]
																					lowerCAmelCase_     :       Any								=						state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			lowerCAmelCase_     :       Any								=						state_dict[
																																			    f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					lowerCAmelCase_     :       List[Any]								=						state_dict[f'{layer}']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowerCAmelCase_     :       str								=						state_dict[f'lm_head.dense.{w}']
																												lowerCAmelCase_     :       Optional[int]								=						state_dict[f'lm_head.layer_norm.{w}']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowerCAmelCase_     :       Dict								=						state_dict[f'{prefix}.ln_f.{w}']
														lowerCAmelCase_     :       Any								=						state_dict['lm_head.weight']

							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)




