



import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

_lowercase     							=						logging.getLogger()






def      _A				()  ->List[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		argparse.ArgumentParser()
			parser.add_argument("""-f""" )
			lowerCamelCase__ :      Dict								=		parser.parse_args()
			return args.f






def      _A				(UpperCamelCase		:      Any )  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		{}
			lowerCamelCase__ :      Optional[int]								=		os.path.join(__UpperCamelCase      ,			"""all_results.json""" )
			if os.path.exists(__UpperCamelCase ):
						with open(__UpperCamelCase      ,			"""r""" ) as f:
									lowerCamelCase__ :      Optional[int]								=		json.load(__UpperCamelCase )
			else:
						raise ValueError(f"can't find {path}" )
			return results






def      _A				()  ->Dict:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		torch.cuda.is_available() and torch_device == '''cuda'''
			return is_using_cuda and is_apex_available()


_lowercase     							=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 				__A					( SCREAMING_SNAKE_CASE_      ):





			@classmethod
			def        _snake_case    (cls				):
						# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
						lowerCamelCase__ :      Any								=		tempfile.mkdtemp()
						lowerCamelCase__ :      List[Any]								=		os.path.join(cls.tmpdir							,    """default_config.yml"""				)
						write_basic_config(save_location=cls.configPath				)
						lowerCamelCase__ :      int								=		['''accelerate''', '''launch''', '''--config_file''', cls.configPath]





			@classmethod
			def        _snake_case    (cls				):
						shutil.rmtree(cls.tmpdir				)





			@mock.patch.dict(os.environ							,    {"""WANDB_MODE""": """offline"""}				)
			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		self.get_auto_remove_tmp_dir()
						lowerCamelCase__ :      Optional[int]								=		f"\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.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            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

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

						run_command(self._launch_args + testargs				)
						lowerCamelCase__ :      Tuple								=		get_results(UpperCamelCase__				)
						self.assertGreaterEqual(result["""eval_accuracy"""]							,    0.75				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """epoch_0"""				)				)				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """glue_no_trainer"""				)				)				)





			@mock.patch.dict(os.environ							,    {"""WANDB_MODE""": """offline"""}				)
			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		self.get_auto_remove_tmp_dir()
						lowerCamelCase__ :      int								=		f"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

						if torch.cuda.device_count() > 1:
									# Skipping because there are not enough batches to train the model + would need a drop_last to work.
									return

						run_command(self._launch_args + testargs				)
						lowerCamelCase__ :      Optional[Any]								=		get_results(UpperCamelCase__				)
						self.assertLess(result["""perplexity"""]							,    100				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """epoch_0"""				)				)				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """clm_no_trainer"""				)				)				)





			@mock.patch.dict(os.environ							,    {"""WANDB_MODE""": """offline"""}				)
			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		self.get_auto_remove_tmp_dir()
						lowerCamelCase__ :      str								=		f"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.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            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

						run_command(self._launch_args + testargs				)
						lowerCamelCase__ :      Optional[int]								=		get_results(UpperCamelCase__				)
						self.assertLess(result["""perplexity"""]							,    42				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """epoch_0"""				)				)				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """mlm_no_trainer"""				)				)				)





			@mock.patch.dict(os.environ							,    {"""WANDB_MODE""": """offline"""}				)
			def        _snake_case    (self				):
						# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
						lowerCamelCase__ :      Optional[int]								=		7 if get_gpu_count() > 1 else 2

						lowerCamelCase__ :      Dict								=		self.get_auto_remove_tmp_dir()
						lowerCamelCase__ :      Dict								=		f"\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.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            --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            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

						run_command(self._launch_args + testargs				)
						lowerCamelCase__ :      str								=		get_results(UpperCamelCase__				)
						self.assertGreaterEqual(result["""eval_accuracy"""]							,    0.75				)
						self.assertLess(result["""train_loss"""]							,    0.5				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """epoch_0"""				)				)				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """ner_no_trainer"""				)				)				)





			@unittest.skip(reason="""Fix me @muellerzr"""				)
			@mock.patch.dict(os.environ							,    {"""WANDB_MODE""": """offline"""}				)
			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		self.get_auto_remove_tmp_dir()
						lowerCamelCase__ :      Any								=		f"\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.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            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

						run_command(self._launch_args + testargs				)
						lowerCamelCase__ :      Tuple								=		get_results(UpperCamelCase__				)
						# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
						self.assertGreaterEqual(result["""eval_f1"""]							,    28				)
						self.assertGreaterEqual(result["""eval_exact"""]							,    28				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """epoch_0"""				)				)				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """qa_no_trainer"""				)				)				)





			@mock.patch.dict(os.environ							,    {"""WANDB_MODE""": """offline"""}				)
			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[int]								=		self.get_auto_remove_tmp_dir()
						lowerCamelCase__ :      Dict								=		f"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.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            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

						run_command(self._launch_args + testargs				)
						lowerCamelCase__ :      List[Any]								=		get_results(UpperCamelCase__				)
						self.assertGreaterEqual(result["""eval_accuracy"""]							,    0.8				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """swag_no_trainer"""				)				)				)





			@slow
			@mock.patch.dict(os.environ							,    {"""WANDB_MODE""": """offline"""}				)
			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		self.get_auto_remove_tmp_dir()
						lowerCamelCase__ :      Tuple								=		f"\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.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            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

						run_command(self._launch_args + testargs				)
						lowerCamelCase__ :      Tuple								=		get_results(UpperCamelCase__				)
						self.assertGreaterEqual(result["""eval_rouge1"""]							,    10				)
						self.assertGreaterEqual(result["""eval_rouge2"""]							,    2				)
						self.assertGreaterEqual(result["""eval_rougeL"""]							,    7				)
						self.assertGreaterEqual(result["""eval_rougeLsum"""]							,    7				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """epoch_0"""				)				)				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """summarization_no_trainer"""				)				)				)





			@slow
			@mock.patch.dict(os.environ							,    {"""WANDB_MODE""": """offline"""}				)
			def        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		self.get_auto_remove_tmp_dir()
						lowerCamelCase__ :      Optional[int]								=		f"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/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            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\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            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

						run_command(self._launch_args + testargs				)
						lowerCamelCase__ :      Optional[int]								=		get_results(UpperCamelCase__				)
						self.assertGreaterEqual(result["""eval_bleu"""]							,    30				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """epoch_0"""				)				)				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """translation_no_trainer"""				)				)				)





			@slow
			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		logging.StreamHandler(sys.stdout				)
						logger.addHandler(UpperCamelCase__				)

						lowerCamelCase__ :      Optional[int]								=		self.get_auto_remove_tmp_dir()
						lowerCamelCase__ :      List[Any]								=		f"\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        ".split()

						run_command(self._launch_args + testargs				)
						lowerCamelCase__ :      Dict								=		get_results(UpperCamelCase__				)
						self.assertGreaterEqual(result["""eval_overall_accuracy"""]							,    0.10				)





			@mock.patch.dict(os.environ							,    {"""WANDB_MODE""": """offline"""}				)
			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[Any]								=		self.get_auto_remove_tmp_dir()
						lowerCamelCase__ :      List[Any]								=		f"\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        ".split()

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

						run_command(self._launch_args + testargs				)
						lowerCamelCase__ :      Dict								=		get_results(UpperCamelCase__				)
						# The base model scores a 25%
						self.assertGreaterEqual(result["""eval_accuracy"""]							,    0.6				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """step_1"""				)				)				)
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__							,    """image_classification_no_trainer"""				)				)				)






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Optional[Any] )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		TaConfig.from_json_file(UpperCamelCase )
			print(f"Building PyTorch model from configuration: {config}" )
			lowerCamelCase__ :      Optional[int]								=		TaForConditionalGeneration(UpperCamelCase )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )

			# Save pytorch-model
			print(f"Save PyTorch model to {pytorch_dump_path}" )
			model.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
				_lowercase     							=						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(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				_lowercase     							=						parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



