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

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__lowerCamelCase :     Optional[Any]          =  [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-generation''',
        '''text-classification''',
        '''token-classification''',
        '''language-modeling''',
        '''multiple-choice''',
        '''question-answering''',
        '''summarization''',
        '''translation''',
        '''image-classification''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
		import run_clm
		import run_image_classification
		import run_mlm
		import run_ner
		import run_qa as run_squad
		import run_summarization
		import run_swag
		import run_text_classification
		import run_translation


logging.basicConfig(level=logging.DEBUG)

__lowerCamelCase :     Optional[int]          =  logging.getLogger()




def 				_snake_case     (       ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int    				=   argparse.ArgumentParser()
		parser.add_argument("-f"     )
		SCREAMING_SNAKE_CASE_  :		Dict    				=   parser.parse_args()
		return args.f




def 				_snake_case     (       lowerCAmelCase							:       Optional[int]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict    				=   {}
		SCREAMING_SNAKE_CASE_  :		Dict    				=   os.path.join(lowercase_       ,			"all_results.json"     )
		if os.path.exists(lowercase_     ):
				with open(lowercase_       ,			"r"     ) as f:
						SCREAMING_SNAKE_CASE_  :		Any    				=   json.load(lowercase_     )
		else:
				raise ValueError(f'can\'t find {path}'     )
		return results




def 				_snake_case     (       ):
		"""simple docstring"""







		return bool(tf.config.list_physical_devices("GPU"     )     )


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







class 		a__						( lowercase__ ):

					@skip("Skipping until shape inference for to_tf_dataset PR is merged."   )
					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.get_auto_remove_tmp_dir()
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   F'\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

							if is_cuda_available():
									testargs.append("--fp16"   )

							with patch.object(lowerCAmelCase__,"argv",lowerCAmelCase__   ):
									run_text_classification.main()
									# Reset the mixed precision policy so we don't break other tests
									tf.keras.mixed_precision.set_global_policy("float32"   )
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   get_results(lowerCAmelCase__   )
									self.assertGreaterEqual(result["eval_accuracy"],0.75   )

					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   self.get_auto_remove_tmp_dir()
							SCREAMING_SNAKE_CASE_  :		int    				=   F'\n            run_clm.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 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

							if len(tf.config.list_physical_devices("GPU"   )   ) > 1:
									# Skipping because there are not enough batches to train the model + would need a drop_last to work.
									return

							with patch.object(lowerCAmelCase__,"argv",lowerCAmelCase__   ):
									run_clm.main()
									SCREAMING_SNAKE_CASE_  :		Dict    				=   get_results(lowerCAmelCase__   )
									self.assertLess(result["eval_perplexity"],100   )

					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   self.get_auto_remove_tmp_dir()
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   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            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '.split()

							with patch.object(lowerCAmelCase__,"argv",lowerCAmelCase__   ):
									run_mlm.main()
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   get_results(lowerCAmelCase__   )
									self.assertLess(result["eval_perplexity"],42   )

					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   7 if get_gpu_count() > 1 else 2

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   self.get_auto_remove_tmp_dir()
							SCREAMING_SNAKE_CASE_  :		int    				=   F'\n            run_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            --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(lowerCAmelCase__,"argv",lowerCAmelCase__   ):
									run_ner.main()
									SCREAMING_SNAKE_CASE_  :		str    				=   get_results(lowerCAmelCase__   )
									self.assertGreaterEqual(result["accuracy"],0.75   )

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   self.get_auto_remove_tmp_dir()
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   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            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

							with patch.object(lowerCAmelCase__,"argv",lowerCAmelCase__   ):
									run_squad.main()
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   get_results(lowerCAmelCase__   )
									self.assertGreaterEqual(result["f1"],30   )
									self.assertGreaterEqual(result["exact"],30   )

					def     __UpperCamelCase       (   self				:	Tuple   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   self.get_auto_remove_tmp_dir()
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   F'\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

							with patch.object(lowerCAmelCase__,"argv",lowerCAmelCase__   ):
									run_swag.main()
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   get_results(lowerCAmelCase__   )
									self.assertGreaterEqual(result["val_accuracy"],0.8   )

					@slow
					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   self.get_auto_remove_tmp_dir()
							SCREAMING_SNAKE_CASE_  :		Any    				=   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            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

							with patch.object(lowerCAmelCase__,"argv",lowerCAmelCase__   ):
									run_summarization.main()
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   get_results(lowerCAmelCase__   )
									self.assertGreaterEqual(result["rouge1"],10   )
									self.assertGreaterEqual(result["rouge2"],2   )
									self.assertGreaterEqual(result["rougeL"],7   )
									self.assertGreaterEqual(result["rougeLsum"],7   )

					@slow
					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   self.get_auto_remove_tmp_dir()
							SCREAMING_SNAKE_CASE_  :		str    				=   F'\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        '.split()

							with patch.object(lowerCAmelCase__,"argv",lowerCAmelCase__   ):
									run_translation.main()
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   get_results(lowerCAmelCase__   )
									self.assertGreaterEqual(result["bleu"],30   )






					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   self.get_auto_remove_tmp_dir()
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   F'\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            '.split()

							with patch.object(lowerCAmelCase__,"argv",lowerCAmelCase__   ):
									run_image_classification.main()
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   get_results(lowerCAmelCase__   )
									self.assertGreaterEqual(result["accuracy"],0.7   )







import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvBertConfig.from_json_file(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   ConvBertModel(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Dict    				=   load_tf_weights_in_convbert(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		model.save_pretrained(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   TFConvBertModel.from_pretrained(lowerCAmelCase       ,			from_pt=lowerCAmelCase     )
		tf_model.save_pretrained(lowerCAmelCase     )


if __name__ == "__main__":
		__lowerCamelCase :     Optional[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(
		    '''--convbert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained ConvBERT 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 :     Optional[int]          =  parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
