

'''simple docstring'''





import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCamelCase_							=					[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-classification",
        "language-modeling",
        "summarization",
        "token-classification",
        "question-answering",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
							import run_clm_flax
							import run_flax_glue
							import run_flax_ner
							import run_mlm_flax
							import run_qa
							import run_summarization_flax
							import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase_							=					logging.getLogger()






def 							_lowerCAmelCase (      )							->     int:
						lowercase   :       Union[str, Any]								=argparse.ArgumentParser()
						parser.add_argument('''-f'''	)
						lowercase   :       str								=parser.parse_args()
						return args.f






def 							_lowerCAmelCase (      __magic_name__	:    List[Any]      ,   __magic_name__	:    str="eval"	)							->     Optional[int]:
						lowercase   :       Tuple								=os.path.join(_SCREAMING_SNAKE_CASE      ,   f'''{split}_results.json'''	)
						if os.path.exists(_SCREAMING_SNAKE_CASE	):
												with open(_SCREAMING_SNAKE_CASE      ,   '''r'''	) as f:
																		return json.load(_SCREAMING_SNAKE_CASE	)
						raise ValueError(f'''can\'t find {path}'''	)


UpperCamelCase_							=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Tuple								=self.get_auto_remove_tmp_dir()
										lowercase   :       List[Any]								=F'''\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '''.split()

										with patch.object(__UpperCamelCase		,				'''argv'''		,				__UpperCamelCase      ):
																run_flax_glue.main()
																lowercase   :       str								=get_results(__UpperCamelCase      )
																self.assertGreaterEqual(result['''eval_accuracy''']		,				0.75      )







				@slow
				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Any								=self.get_auto_remove_tmp_dir()
										lowercase   :       List[str]								=F'''\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '''.split()

										with patch.object(__UpperCamelCase		,				'''argv'''		,				__UpperCamelCase      ):
																run_clm_flax.main()
																lowercase   :       List[Any]								=get_results(__UpperCamelCase      )
																self.assertLess(result['''eval_perplexity''']		,				100      )







				@slow
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       str								=self.get_auto_remove_tmp_dir()
										lowercase   :       Tuple								=F'''\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        '''.split()

										with patch.object(__UpperCamelCase		,				'''argv'''		,				__UpperCamelCase      ):
																run_summarization_flax.main()
																lowercase   :       Optional[Any]								=get_results(__UpperCamelCase		,				split='''test'''      )
																self.assertGreaterEqual(result['''test_rouge1''']		,				10      )
																self.assertGreaterEqual(result['''test_rouge2''']		,				2      )
																self.assertGreaterEqual(result['''test_rougeL''']		,				7      )
																self.assertGreaterEqual(result['''test_rougeLsum''']		,				7      )







				@slow
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       int								=self.get_auto_remove_tmp_dir()
										lowercase   :       Dict								=F'''\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '''.split()

										with patch.object(__UpperCamelCase		,				'''argv'''		,				__UpperCamelCase      ):
																run_mlm_flax.main()
																lowercase   :       Any								=get_results(__UpperCamelCase      )
																self.assertLess(result['''eval_perplexity''']		,				42      )







				@slow
				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=self.get_auto_remove_tmp_dir()
										lowercase   :       Any								=F'''\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '''.split()

										with patch.object(__UpperCamelCase		,				'''argv'''		,				__UpperCamelCase      ):
																run_ta_mlm_flax.main()
																lowercase   :       Union[str, Any]								=get_results(__UpperCamelCase      )
																self.assertGreaterEqual(result['''eval_accuracy''']		,				0.42      )







				@slow
				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
										lowercase   :       List[str]								=7 if get_gpu_count() > 1 else 2

										lowercase   :       Dict								=self.get_auto_remove_tmp_dir()
										lowercase   :       List[Any]								=F'''\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '''.split()

										with patch.object(__UpperCamelCase		,				'''argv'''		,				__UpperCamelCase      ):
																run_flax_ner.main()
																lowercase   :       int								=get_results(__UpperCamelCase      )
																self.assertGreaterEqual(result['''eval_accuracy''']		,				0.75      )
																self.assertGreaterEqual(result['''eval_f1''']		,				0.3      )







				@slow
				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       int								=self.get_auto_remove_tmp_dir()
										lowercase   :       Dict								=F'''\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '''.split()

										with patch.object(__UpperCamelCase		,				'''argv'''		,				__UpperCamelCase      ):
																run_qa.main()
																lowercase   :       int								=get_results(__UpperCamelCase      )
																self.assertGreaterEqual(result['''eval_f1''']		,				30      )
																self.assertGreaterEqual(result['''eval_exact''']		,				30      )



'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							UpperCamelCase_							=					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""")
							UpperCamelCase_							=					parser.parse_args()

							if args.model_type == "roberta":
														UpperCamelCase_							=					RobertaForMaskedLM.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""roberta"""
							elif args.model_type == "gpt2":
														UpperCamelCase_							=					GPTaLMHeadModel.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""transformer"""

							UpperCamelCase_							=					model.state_dict()
							UpperCamelCase_							=					{}

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

    # Transformer Blocks #
							UpperCamelCase_							=					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"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																																			]
																					UpperCamelCase_							=					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"]:
																																			UpperCamelCase_							=					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"]:
																					UpperCamelCase_							=					state_dict[f'''{layer}''']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												UpperCamelCase_							=					state_dict[f'''lm_head.dense.{w}''']
																												UpperCamelCase_							=					state_dict[f'''lm_head.layer_norm.{w}''']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.ln_f.{w}''']
														UpperCamelCase_							=					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)





