"""simple docstring"""






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
						import torch
@is_pipeline_test
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
					snake_case__				    = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	Any			):
									UpperCAmelCase__             =							TextaTextGenerationPipeline(model=lowerCamelCase__    ,tokenizer=lowerCamelCase__			)
									return generator, ["Something to write", "Something else"]







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	Optional[Any]			):
									UpperCAmelCase__             =							generator('Something there'			)
									self.assertEqual(lowerCamelCase__    ,[{'generated_text': ANY(lowerCamelCase__			)}]			)
									# These are encoder decoder, they don't just append to incoming string
									self.assertFalse(outputs[0]['generated_text'].startswith('Something there'			)			)

									UpperCAmelCase__             =							generator(['This is great !', 'Something else']    ,num_return_sequences=2    ,do_sample=lowerCamelCase__			)
									self.assertEqual(
									    lowerCamelCase__    ,[
									        [{'generated_text': ANY(lowerCamelCase__			)}, {'generated_text': ANY(lowerCamelCase__			)}],
									        [{'generated_text': ANY(lowerCamelCase__			)}, {'generated_text': ANY(lowerCamelCase__			)}],
									    ]    ,)

									UpperCAmelCase__             =							generator(
									    ['This is great !', 'Something else']    ,num_return_sequences=2    ,batch_size=2    ,do_sample=lowerCamelCase__			)
									self.assertEqual(
									    lowerCamelCase__    ,[
									        [{'generated_text': ANY(lowerCamelCase__			)}, {'generated_text': ANY(lowerCamelCase__			)}],
									        [{'generated_text': ANY(lowerCamelCase__			)}, {'generated_text': ANY(lowerCamelCase__			)}],
									    ]    ,)

									with self.assertRaises(lowerCamelCase__			):
													generator(4			)







					@require_torch
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							pipeline('text2text-generation'    ,model='patrickvonplaten/t5-tiny-random'    ,framework='pt'			)
									# do_sample=False necessary for reproducibility
									UpperCAmelCase__             =							generator('Something there'    ,do_sample=lowerCamelCase__			)
									self.assertEqual(lowerCamelCase__    ,[{'generated_text': ''}]			)

									UpperCAmelCase__             =							3
									UpperCAmelCase__             =							generator(
									    'Something there'    ,num_return_sequences=lowerCamelCase__    ,num_beams=lowerCamelCase__    ,)
									UpperCAmelCase__             =							[
									    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
									    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
									    {'generated_text': ''},
									]
									self.assertEqual(lowerCamelCase__    ,lowerCamelCase__			)

									UpperCAmelCase__             =							generator('This is a test'    ,do_sample=lowerCamelCase__    ,num_return_sequences=2    ,return_tensors=lowerCamelCase__			)
									self.assertEqual(
									    lowerCamelCase__    ,[
									        {'generated_token_ids': ANY(torch.Tensor			)},
									        {'generated_token_ids': ANY(torch.Tensor			)},
									    ]    ,)
									UpperCAmelCase__             =							generator.model.config.eos_token_id
									UpperCAmelCase__             =							'<pad>'
									UpperCAmelCase__             =							generator(
									    ['This is a test', 'This is a second test']    ,do_sample=lowerCamelCase__    ,num_return_sequences=2    ,batch_size=2    ,return_tensors=lowerCamelCase__    ,)
									self.assertEqual(
									    lowerCamelCase__    ,[
									        [
									            {'generated_token_ids': ANY(torch.Tensor			)},
									            {'generated_token_ids': ANY(torch.Tensor			)},
									        ],
									        [
									            {'generated_token_ids': ANY(torch.Tensor			)},
									            {'generated_token_ids': ANY(torch.Tensor			)},
									        ],
									    ]    ,)







					@require_tf
					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							pipeline('text2text-generation'    ,model='patrickvonplaten/t5-tiny-random'    ,framework='tf'			)
									# do_sample=False necessary for reproducibility
									UpperCAmelCase__             =							generator('Something there'    ,do_sample=lowerCamelCase__			)
									self.assertEqual(lowerCamelCase__    ,[{'generated_text': ''}]			)




"""simple docstring"""






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

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__  :  Optional[Any]						     =	logging.getLogger()



def   a_						(					):
				UpperCAmelCase__             =							argparse.ArgumentParser()
				parser.add_argument('-f'       )
				UpperCAmelCase__             =							parser.parse_args()
				return args.f







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							logging.StreamHandler(sys.stdout			)
									logger.addHandler(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							get_gpu_count()

									if n_gpu > 1:
													pass
													# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
													# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
													# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
													# cmd = [sys.executable] + distributed_args + args
													# execute_subprocess_async(cmd, env=self.get_env())
													# XXX: test the results - need to save them first into .json file
									else:
													args.insert(0    ,'run_glue_deebert.py'			)
													with patch.object(lowerCamelCase__    ,'argv'    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							run_glue_deebert.main()
																	for value in result.values():
																					self.assertGreaterEqual(lowerCamelCase__    ,0.6_6_6			)







					@slow
					@require_torch_non_multi_gpu
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)



