
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class        _UpperCamelCase				(					_A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						Any					      = MvpTokenizer
			__UpperCamelCase      :						Union[str, Any]					      = MvpTokenizerFast
			__UpperCamelCase      :						str					      = True
			__UpperCamelCase      :						List[Any]					      = filter_roberta_detectors

			def       lowerCAmelCase__	(       self :       Tuple      ):
							super().setUp()
							UpperCamelCase_:							Union[str, Any]	=						[
							    """l""",
							    """o""",
							    """w""",
							    """e""",
							    """r""",
							    """s""",
							    """t""",
							    """i""",
							    """d""",
							    """n""",
							    """\u0120""",
							    """\u0120l""",
							    """\u0120n""",
							    """\u0120lo""",
							    """\u0120low""",
							    """er""",
							    """\u0120lowest""",
							    """\u0120newer""",
							    """\u0120wider""",
							    """<unk>""",
							]
							UpperCamelCase_:							Optional[Any]	=						dict(zip(snake_case_						,	range(len(snake_case_      )      )      )      )
							UpperCamelCase_:							int	=						["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
							UpperCamelCase_:							Tuple	=						{"""unk_token""": """<unk>"""}

							UpperCamelCase_:							int	=						os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["""vocab_file"""]      )
							UpperCamelCase_:							Optional[Any]	=						os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["""merges_file"""]      )
							with open(self.vocab_file						,	"""w"""						,	encoding="""utf-8"""      ) as fp:
											fp.write(json.dumps(snake_case_      ) + """\n"""      )
							with open(self.merges_file						,	"""w"""						,	encoding="""utf-8"""      ) as fp:
											fp.write("""\n""".join(snake_case_      )      )

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	**snake_case_ :       Optional[Any]      ):
							kwargs.update(self.special_tokens_map      )
							return self.tokenizer_class.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       List[Any]						,	**snake_case_ :       Optional[int]      ):
							kwargs.update(self.special_tokens_map      )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       Optional[int]      ):
							return "lower newer", "lower newer"

			@cached_property
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""      )

			@cached_property
			def       lowerCAmelCase__	(       self :       Dict      ):
							return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""      )

			@require_torch
			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Union[str, Any]	=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
							UpperCamelCase_:							Tuple	=						[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCamelCase_:							Optional[int]	=						tokenizer(snake_case_						,	max_length=len(snake_case_      )						,	padding=snake_case_						,	return_tensors="""pt"""      )
											self.assertIsInstance(snake_case_						,	snake_case_      )

											self.assertEqual((2, 9)						,	batch.input_ids.shape      )
											self.assertEqual((2, 9)						,	batch.attention_mask.shape      )
											UpperCamelCase_:							List[str]	=						batch.input_ids.tolist()[0]
											self.assertListEqual(snake_case_						,	snake_case_      )
											# Test that special tokens are reset

			@require_torch
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Any	=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCamelCase_:							Any	=						tokenizer(snake_case_						,	padding=snake_case_						,	return_tensors="""pt"""      )
											# check if input_ids are returned and no labels
											self.assertIn("""input_ids"""						,	snake_case_      )
											self.assertIn("""attention_mask"""						,	snake_case_      )
											self.assertNotIn("""labels"""						,	snake_case_      )
											self.assertNotIn("""decoder_attention_mask"""						,	snake_case_      )

			@require_torch
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Any	=						[
							    """Summary of the text.""",
							    """Another summary.""",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCamelCase_:							Union[str, Any]	=						tokenizer(text_target=snake_case_						,	max_length=32						,	padding="""max_length"""						,	return_tensors="""pt"""      )
											self.assertEqual(32						,	targets["""input_ids"""].shape[1]      )

			@require_torch
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCamelCase_:							str	=						tokenizer(
											    ["""I am a small frog""" * 1024, """I am a small frog"""]						,	padding=snake_case_						,	truncation=snake_case_						,	return_tensors="""pt"""      )
											self.assertIsInstance(snake_case_						,	snake_case_      )
											self.assertEqual(batch.input_ids.shape						,	(2, 1024)      )

			@require_torch
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Tuple	=						["""A long paragraph for summarization."""]
							UpperCamelCase_:							List[str]	=						[
							    """Summary of the text.""",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCamelCase_:							Tuple	=						tokenizer(snake_case_						,	text_target=snake_case_						,	return_tensors="""pt"""      )
											UpperCamelCase_:							int	=						inputs["""input_ids"""]
											UpperCamelCase_:							Union[str, Any]	=						inputs["""labels"""]
											self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
											self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
											self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
											self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							pass




			def       lowerCAmelCase__	(       self :       Dict      ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
															UpperCamelCase_:							Dict	=						self.rust_tokenizer_class.from_pretrained(snake_case_						,	**snake_case_      )
															UpperCamelCase_:							Tuple	=						self.tokenizer_class.from_pretrained(snake_case_						,	**snake_case_      )
															UpperCamelCase_:							List[str]	=						"""A, <mask> AllenNLP sentence."""
															UpperCamelCase_:							str	=						tokenizer_r.encode_plus(snake_case_						,	add_special_tokens=snake_case_						,	return_token_type_ids=snake_case_      )
															UpperCamelCase_:							Dict	=						tokenizer_p.encode_plus(snake_case_						,	add_special_tokens=snake_case_						,	return_token_type_ids=snake_case_      )

															# token_type_ids should put 0 everywhere
															self.assertEqual(sum(tokens_r["""token_type_ids"""]      )						,	sum(tokens_p["""token_type_ids"""]      )      )

															# attention_mask should put 1 everywhere, so sum over length should be 1
															self.assertEqual(
															    sum(tokens_r["""attention_mask"""]      ) / len(tokens_r["""attention_mask"""]      )						,	sum(tokens_p["""attention_mask"""]      ) / len(tokens_p["""attention_mask"""]      )						,	)

															UpperCamelCase_:							List[str]	=						tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]      )
															UpperCamelCase_:							int	=						tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]      )

															# Rust correctly handles the space before the mask while python doesnt
															self.assertSequenceEqual(tokens_p["""input_ids"""]						,	[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )
															self.assertSequenceEqual(tokens_r["""input_ids"""]						,	[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )

															self.assertSequenceEqual(
															    snake_case_						,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]      )
															self.assertSequenceEqual(
															    snake_case_						,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]      )








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)
lowerCamelCase_						: Union[str, Any]         =  logging.getLogger()

lowerCamelCase_						: List[str]         =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Dict      ):
							os.makedirs(snake_case_						,	exist_ok=snake_case_      )
							UpperCamelCase_:							int	=						{"""source""": """What is love ?""", """target""": """life"""}
							UpperCamelCase_:							Tuple	=						{"""train""": 12, """val""": 2, """test""": 2}
							for split in ["train", "test", "val"]:
											for field in ["source", "target"]:
															UpperCamelCase_:							Tuple	=						"""\n""".join([contents[field]] * n_lines[split]      )
															with open(os.path.join(snake_case_						,	f'''{split}.{field}'''      )						,	"""w"""      ) as f:
																			f.write(snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       int						,	snake_case_ :       str = "pytorch"      ):
							UpperCamelCase_:							Optional[Any]	=						self.get_auto_remove_tmp_dir()
							UpperCamelCase_:							Dict	=						os.path.join(snake_case_						,	"""output"""      )
							UpperCamelCase_:							Any	=						os.path.join(snake_case_						,	"""data"""      )
							self._create_dummy_data(data_dir=snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						f'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.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"""      )

							UpperCamelCase_:							Optional[Any]	=						[sys.executable, str(Path(finetune_rag.__file__      ).resolve()      )] + testargs
							execute_subprocess_async(snake_case_						,	env=self.get_env()      )

							UpperCamelCase_:							Optional[int]	=						os.path.join(snake_case_						,	"""metrics.json"""      )
							with open(snake_case_      ) as f:
											UpperCamelCase_:							Any	=						json.load(snake_case_      )
							return result

			@require_torch_gpu
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							List[str]	=						self._run_finetune(gpus=1      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )

			@require_torch_multi_gpu
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						self._run_finetune(gpus=2      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )

			@require_torch_gpu
			@require_ray
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							List[Any]	=						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       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							List[Any]	=						self._run_finetune(gpus=1						,	distributed_retriever="""ray"""      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )






