






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


__lowerCamelCase :      str										=				[
    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)

__lowerCamelCase :      str										=				logging.getLogger()







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Dict       =  argparse.ArgumentParser()
	parser.add_argument("-f"       )
	snake_case__			:				Any       =  parser.parse_args()
	return args.f







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							List[str]="eval"       ):
	snake_case__			:				Union[str, Any]       =  os.path.join(snake_case_					,  F'''{split}_results.json'''       )
	if os.path.exists(snake_case_       ):
		with open(snake_case_					,  "r"       ) as f:
			return json.load(snake_case_       )
	raise ValueError(F'''can\'t find {path}'''       )


__lowerCamelCase :      Optional[Any]										=				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






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

						with patch.object(__A							,		"argv"							,		__A	):
							run_flax_glue.main()
							snake_case__			:				int       =  get_results(__A	)
							self.assertGreaterEqual(result["eval_accuracy"]							,		0.7_5	)



					@slow
					def 	_lowercase					( self					:  int	):
						snake_case__			:				int       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				Optional[int]       =  f'''
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

						with patch.object(__A							,		"argv"							,		__A	):
							run_clm_flax.main()
							snake_case__			:				Union[str, Any]       =  get_results(__A	)
							self.assertLess(result["eval_perplexity"]							,		1_0_0	)



					@slow
					def 	_lowercase					( self					:  Any	):
						snake_case__			:				str       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				Union[str, Any]       =  f'''
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        '''.split()

						with patch.object(__A							,		"argv"							,		__A	):
							run_summarization_flax.main()
							snake_case__			:				Any       =  get_results(__A							,		split="test"	)
							self.assertGreaterEqual(result["test_rouge1"]							,		1_0	)
							self.assertGreaterEqual(result["test_rouge2"]							,		2	)
							self.assertGreaterEqual(result["test_rougeL"]							,		7	)
							self.assertGreaterEqual(result["test_rougeLsum"]							,		7	)



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

						with patch.object(__A							,		"argv"							,		__A	):
							run_mlm_flax.main()
							snake_case__			:				List[Any]       =  get_results(__A	)
							self.assertLess(result["eval_perplexity"]							,		4_2	)



					@slow
					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				int       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				Dict       =  f'''
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

						with patch.object(__A							,		"argv"							,		__A	):
							run_ta_mlm_flax.main()
							snake_case__			:				List[Any]       =  get_results(__A	)
							self.assertGreaterEqual(result["eval_accuracy"]							,		0.4_2	)



					@slow
					def 	_lowercase					( self					:  Any	):
						# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
						snake_case__			:				Tuple       =  7 if get_gpu_count() > 1 else 2

						snake_case__			:				int       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				str       =  f'''
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

						with patch.object(__A							,		"argv"							,		__A	):
							run_flax_ner.main()
							snake_case__			:				Any       =  get_results(__A	)
							self.assertGreaterEqual(result["eval_accuracy"]							,		0.7_5	)
							self.assertGreaterEqual(result["eval_f1"]							,		0.3	)






					@slow
					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				Tuple       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				List[str]       =  f'''
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

						with patch.object(__A							,		"argv"							,		__A	):
							run_qa.main()
							snake_case__			:				List[Any]       =  get_results(__A	)
							self.assertGreaterEqual(result["eval_f1"]							,		3_0	)
							self.assertGreaterEqual(result["eval_exact"]							,		3_0	)











import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):
	# Initialise PyTorch model
	snake_case__			:				List[str]       =  MobileBertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				Dict       =  MobileBertForPreTraining(snake_case_       )
	# Load weights from tf checkpoint
	snake_case__			:				Any       =  load_tf_weights_in_mobilebert(snake_case_					,  snake_case_					,  snake_case_       )
	# Save pytorch-model
	print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
	torch.save(model.state_dict()					,  snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      List[Any]										=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--mobilebert_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained MobileBERT model. \n"""
	        """This specifies the model architecture."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	__lowerCamelCase :      int										=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



