



import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    GPTNeoXForCausalLM,
							    GPTNeoXForQuestionAnswering,
							    GPTNeoXForSequenceClassification,
							    GPTNeoXForTokenClassification,
							    GPTNeoXModel,
							)
class 	lowerCamelCase_							:







						def __init__(  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      str=13				,    lowerCAmelCase__   :      Optional[int]=7				,    lowerCAmelCase__   :      Dict=True				,    lowerCAmelCase__   :      List[Any]=True				,    lowerCAmelCase__   :      Optional[Any]=True				,    lowerCAmelCase__   :      List[str]=True				,    lowerCAmelCase__   :      List[Any]=99				,    lowerCAmelCase__   :      int=64				,    lowerCAmelCase__   :      Any=5				,    lowerCAmelCase__   :      List[Any]=4				,    lowerCAmelCase__   :      List[str]=37				,    lowerCAmelCase__   :      str="gelu"				,    lowerCAmelCase__   :      List[str]=0.1				,    lowerCAmelCase__   :      List[str]=0.1				,    lowerCAmelCase__   :      int=5_12				,    lowerCAmelCase__   :      Any=16				,    lowerCAmelCase__   :      Union[str, Any]=2				,    lowerCAmelCase__   :      int=0.02				,    lowerCAmelCase__   :      List[Any]=3				,    lowerCAmelCase__   :      Optional[Any]=4				,    lowerCAmelCase__   :      Optional[int]=None				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			parent
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			batch_size
													SCREAMING_SNAKE_CASE					:      int     					=			seq_length
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			is_training
													SCREAMING_SNAKE_CASE					:      Tuple     					=			use_input_mask
													SCREAMING_SNAKE_CASE					:      int     					=			use_token_type_ids
													SCREAMING_SNAKE_CASE					:      Tuple     					=			use_labels
													SCREAMING_SNAKE_CASE					:      Tuple     					=			vocab_size
													SCREAMING_SNAKE_CASE					:      Any     					=			hidden_size
													SCREAMING_SNAKE_CASE					:      Tuple     					=			num_hidden_layers
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			num_attention_heads
													SCREAMING_SNAKE_CASE					:      int     					=			intermediate_size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      str     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      Dict     					=			max_position_embeddings
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			type_vocab_size
													SCREAMING_SNAKE_CASE					:      Tuple     					=			type_sequence_label_size
													SCREAMING_SNAKE_CASE					:      str     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			num_labels
													SCREAMING_SNAKE_CASE					:      str     					=			num_choices
													SCREAMING_SNAKE_CASE					:      int     					=			scope
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			vocab_size - 1







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			None
													if self.use_input_mask:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			random_attention_mask([self.batch_size, self.seq_length]				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			None
													if self.use_labels:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_config()

													return config, input_ids, input_mask, token_labels







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													return GPTNeoXConfig(
													    vocab_size=self.vocab_size				,    hidden_size=self.hidden_size				,    num_hidden_layers=self.num_hidden_layers				,    num_attention_heads=self.num_attention_heads				,    intermediate_size=self.intermediate_size				,    hidden_act=self.hidden_act				,    hidden_dropout_prob=self.hidden_dropout_prob				,    attention_probs_dropout_prob=self.attention_probs_dropout_prob				,    max_position_embeddings=self.max_position_embeddings				,    type_vocab_size=self.type_vocab_size				,    is_decoder=lowerCAmelCase__				,    initializer_range=self.initializer_range				,    pad_token_id=self.pad_token_id				,    )







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.prepare_config_and_inputs()

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			True

													return config, input_ids, input_mask, token_labels







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			GPTNeoXModel(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      int     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model(lowerCAmelCase__				)
													self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)				)







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			True
													SCREAMING_SNAKE_CASE					:      Dict     					=			GPTNeoXModel(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)
													self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)				)







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			GPTNeoXForCausalLM(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)				)







						def 					__lowercase    (  self   :      Tuple				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			GPTNeoXForQuestionAnswering(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)
													self.parent.assertEqual(result.start_logits.shape				,    (self.batch_size, self.seq_length)				)
													self.parent.assertEqual(result.end_logits.shape				,    (self.batch_size, self.seq_length)				)







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      str     					=			GPTNeoXForSequenceClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Dict     					=			ids_tensor([self.batch_size]				,    self.type_sequence_label_size				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)				)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Any     					=			GPTNeoXForTokenClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.num_labels)				)







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			True
													SCREAMING_SNAKE_CASE					:      Tuple     					=			GPTNeoXForCausalLM(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()

													# first forward pass
													SCREAMING_SNAKE_CASE					:      int     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    use_cache=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			outputs.past_key_values

													# create hypothetical multiple next token and extent to next_input_ids
													SCREAMING_SNAKE_CASE					:      Any     					=			ids_tensor((self.batch_size, 3)				,    config.vocab_size				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			ids_tensor((self.batch_size, 3)				,    vocab_size=2				)

													# append to next input_ids and
													SCREAMING_SNAKE_CASE					:      int     					=			torch.cat([input_ids, next_tokens]				,    dim=-1				)
													SCREAMING_SNAKE_CASE					:      str     					=			torch.cat([input_mask, next_mask]				,    dim=-1				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    output_hidden_states=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			output_from_no_past['''hidden_states'''][0]
													SCREAMING_SNAKE_CASE					:      str     					=			model(
													    lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    past_key_values=lowerCAmelCase__				,    output_hidden_states=lowerCAmelCase__				,    )['''hidden_states'''][0]

													# select random slice
													SCREAMING_SNAKE_CASE					:      Any     					=			ids_tensor((1,)				,    output_from_past.shape[-1]				).item()
													SCREAMING_SNAKE_CASE					:      Dict     					=			output_from_no_past[:, -3:, random_slice_idx].detach()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			output_from_past[:, :, random_slice_idx].detach()

													self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

													# test that outputs are equal for slice
													self.parent.assertTrue(torch.allclose(lowerCAmelCase__				,    lowerCAmelCase__				,    atol=1e-3				)				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Tuple     					=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
													return config, inputs_dict


@require_torch
class 	lowerCamelCase_							(					lowercase__    ,				lowercase__    ,				lowercase__    ,				unittest.TestCase			):
						_lowerCAmelCase     :   List[Any]	     =				(
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowerCAmelCase     :   str	     =				(GPTNeoXForCausalLM,) if is_torch_available() else ()
						_lowerCAmelCase     :   Union[str, Any]	     =				(
						    {
						        'feature-extraction': GPTNeoXModel,
						        'question-answering': GPTNeoXForQuestionAnswering,
						        'text-classification': GPTNeoXForSequenceClassification,
						        'text-generation': GPTNeoXForCausalLM,
						        'token-classification': GPTNeoXForTokenClassification,
						        'zero-shot': GPTNeoXForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowerCAmelCase     :   Union[str, Any]	     =				False
						_lowerCAmelCase     :   int	     =				False
						_lowerCAmelCase     :   Dict	     =				False
						_lowerCAmelCase     :   Any	     =				False







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			GPTNeoXModelTester(self				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			ConfigTester(self				,    config_class=lowerCAmelCase__				,    hidden_size=64				,    num_attention_heads=8				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													self.config_tester.run_common_tests()







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			self.model_tester.prepare_config_and_inputs_for_decoder()
													self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													# This regression test was failing with PyTorch < 1.3
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs_for_decoder()

													SCREAMING_SNAKE_CASE					:      Any     					=			None

													self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_causal_lm(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__				)







						@unittest.skip(reason='''Feed forward chunking is not implemented'''				)
						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													pass







						@parameterized.expand([('''linear''',), ('''dynamic''',)]				)
						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Tuple     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Dict     					=			ids_tensor([1, 10]				,    config.vocab_size				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			ids_tensor([1, int(config.max_position_embeddings * 1.5				)]				,    config.vocab_size				)

													set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
													SCREAMING_SNAKE_CASE					:      str     					=			GPTNeoXModel(lowerCAmelCase__				)
													original_model.to(lowerCAmelCase__				)
													original_model.eval()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			original_model(lowerCAmelCase__				).last_hidden_state
													SCREAMING_SNAKE_CASE					:      str     					=			original_model(lowerCAmelCase__				).last_hidden_state

													set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			{'''type''': scaling_type, '''factor''': 10.0}
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			GPTNeoXModel(lowerCAmelCase__				)
													scaled_model.to(lowerCAmelCase__				)
													scaled_model.eval()
													SCREAMING_SNAKE_CASE					:      Dict     					=			scaled_model(lowerCAmelCase__				).last_hidden_state
													SCREAMING_SNAKE_CASE					:      List[str]     					=			scaled_model(lowerCAmelCase__				).last_hidden_state

													# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
													# maximum sequence length, so the outputs for the short input should match.
													if scaling_type == "dynamic":
																				self.assertTrue(torch.allclose(lowerCAmelCase__				,    lowerCAmelCase__				,    atol=1e-5				)				)
													else:
																				self.assertFalse(torch.allclose(lowerCAmelCase__				,    lowerCAmelCase__				,    atol=1e-5				)				)

													# The output should be different for long inputs
													self.assertFalse(torch.allclose(lowerCAmelCase__				,    lowerCAmelCase__				,    atol=1e-5				)				)


@require_torch
class 	lowerCamelCase_							(					unittest.TestCase			):







						@slow
						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''				)
													for checkpointing in [True, False]:
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''				)

																				if checkpointing:
																											model.gradient_checkpointing_enable()
																				else:
																											model.gradient_checkpointing_disable()
																				model.to(lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer('''My favorite food is'''				,    return_tensors='''pt'''				).to(lowerCAmelCase__				)
																				# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
																				# See: https://github.com/huggingface/transformers/pull/24193
																				SCREAMING_SNAKE_CASE					:      Any     					=			'''My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'''

																				SCREAMING_SNAKE_CASE					:      Tuple     					=			model.generate(**lowerCAmelCase__				,    do_sample=lowerCAmelCase__				,    max_new_tokens=20				)
																				SCREAMING_SNAKE_CASE					:      str     					=			tokenizer.batch_decode(lowerCAmelCase__				)[0]

																				self.assertEqual(lowerCAmelCase__				,    lowerCAmelCase__				)









'''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)




