






"""simple docstring"""







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_keras_nlp_available():
	from transformers.models.gpta import TFGPTaTokenizer


__a       :	Dict             = ['gpt2']
__a       :	Dict             = 'gpt2'

if is_tf_available():







	class        _SCREAMING_SNAKE_CASE						(  tf.Module							):





								"""simple docstring"""





								def __init__(   self:     Optional[Any]				,		__A:     Any    ):

													'''simple docstring'''

													super().__init__()
													a__          =  tokenizer
													a__          =  AutoConfig.from_pretrained(__A    )
													a__          =  TFGPTaLMHeadModel.from_config(__A    )





								@tf.function(input_signature=(tf.TensorSpec((None,)				,		tf.string				,		name='''text'''    ),)    )
								def  lowercase		(   self:     Tuple				,		__A:     List[str]    ):

													'''simple docstring'''

													a__          =  self.tokenizer(__A    )
													a__          =  tokenized['''input_ids'''].to_tensor()

													a__          =  tf.cast(input_ids_dense > 0				,		tf.intaa    )
													# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

													a__          =  self.model(input_ids=__A				,		attention_mask=__A    )['''logits''']

													return outputs







@require_tf
@require_keras_nlp
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												super().setUp()

												a__          =  [GPTaTokenizer.from_pretrained(__A    ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
												a__          =  [TFGPTaTokenizer.from_pretrained(__A    ) for checkpoint in TOKENIZER_CHECKPOINTS]
												assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

												a__          =  [
												    '''This is a straightforward English test sentence.''',
												    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
												    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
												    '''And some much more rare Chinese: 齉 堃 齉堃''',
												    '''Je vais aussi écrire en français pour tester les accents''',
												    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
												]
												a__          =  list(zip(self.test_sentences				,		self.test_sentences[::-1]    )    )




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												for tokenizer, tf_tokenizer in zip(self.tokenizers				,		self.tf_tokenizers    ):
																	for test_inputs in self.test_sentences:
																						a__          =  tokenizer([test_inputs]				,		return_tensors='''tf'''    )
																						a__          =  tf_tokenizer([test_inputs]    )

																						for key in python_outputs.keys():
																											# convert them to numpy to avoid messing with ragged tensors
																											a__          =  python_outputs[key].numpy()
																											a__          =  tf_outputs[key].numpy()

																											self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape    )    )
																											self.assertTrue(tf.reduce_all(tf.cast(__A				,		tf.intaa    ) == tf_outputs_values    )    )




							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												for tf_tokenizer in self.tf_tokenizers:
																	a__          =  tf.function(__A    )
																	for test_inputs in self.test_sentences:
																						a__          =  tf.constant(__A    )
																						a__          =  compiled_tokenizer(__A    )
																						a__          =  tf_tokenizer(__A    )

																						for key in eager_outputs.keys():
																											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )




							@slow
							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												for tf_tokenizer in self.tf_tokenizers:
																	a__          =  ModelToSave(tokenizer=__A    )
																	a__          =  tf.convert_to_tensor([self.test_sentences[0]]    )
																	a__          =  model.serving(__A    )  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																						a__          =  Path(__A    ) / '''saved.model'''
																						tf.saved_model.save(__A				,		__A				,		signatures={'''serving_default''': model.serving}    )
																						a__          =  tf.saved_model.load(__A    )
																	a__          =  loaded_model.signatures['''serving_default'''](__A    )['''output_0''']
																	# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																	self.assertTrue(tf.reduce_all(out == loaded_output    )    )




							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												for tf_tokenizer in self.tf_tokenizers:
																	a__          =  tf.convert_to_tensor([self.test_sentences[0]]    )
																	a__          =  tf_tokenizer(__A    )  # Build model with some sample inputs

																	a__          =  tf_tokenizer.get_config()
																	a__          =  TFGPTaTokenizer.from_config(__A    )
																	a__          =  model_from_config(__A    )

																	for key in from_config_output.keys():
																						self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]    )    )





							@slow
							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												for tf_tokenizer in self.tf_tokenizers:
																	# for the test to run
																	a__          =  123123

																	for max_length in [3, 5, 1024]:
																						a__          =  tf.convert_to_tensor([self.test_sentences[0]]    )
																						a__          =  tf_tokenizer(__A				,		max_length=__A    )

																						a__          =  out['''input_ids'''].numpy().shape[1]

																						assert out_length == max_length










"""simple docstring"""







import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__a       :	Optional[int]             = logging.getLogger()

__a       :	Any             = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							def  lowercase		(   self:     List[Any]				,		__A:     List[Any]    ):

												'''simple docstring'''

												os.makedirs(__A				,		exist_ok=__A    )
												a__          =  {'''source''': '''What is love ?''', '''target''': '''life'''}
												a__          =  {'''train''': 12, '''val''': 2, '''test''': 2}
												for split in ["train", "test", "val"]:
																	for field in ["source", "target"]:
																						a__          =  '''\n'''.join([contents[field]] * n_lines[split]    )
																						with open(os.path.join(__A				,		F'{split}.{field}'    )				,		'''w'''    ) as f:
																											f.write(__A    )




							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     str = "pytorch"    ):

												'''simple docstring'''

												a__          =  self.get_auto_remove_tmp_dir()
												a__          =  os.path.join(__A				,		'''output'''    )
												a__          =  os.path.join(__A				,		'''data'''    )
												self._create_dummy_data(data_dir=__A    )

												a__          =  F'\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            '.split()

												if gpus > 0:
																	testargs.append(F'--gpus={gpus}'    )
																	if is_apex_available():
																						testargs.append('''--fp16'''    )
												else:
																	testargs.append('''--gpus=0'''    )
																	testargs.append('''--distributed_backend=ddp_cpu'''    )
																	testargs.append('''--num_processes=2'''    )

												a__          =  [sys.executable, str(Path(finetune_rag.__file__    ).resolve()    )] + testargs
												execute_subprocess_async(__A				,		env=self.get_env()    )

												a__          =  os.path.join(__A				,		'''metrics.json'''    )
												with open(__A    ) as f:
																	a__          =  json.load(__A    )
												return result




							@require_torch_gpu
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )




							@require_torch_multi_gpu
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=2    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )




							@require_torch_gpu
							@require_ray
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1				,		distributed_retriever='''ray'''    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )





							@require_torch_multi_gpu
							@require_ray
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1				,		distributed_retriever='''ray'''    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )


