
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						tempfile.mkdtemp()

							UpperCamelCase_:							List[Any]	=						[
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """[PAD]""",
							    """[MASK]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							UpperCamelCase_:							Any	=						os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["""vocab_file"""]      )
							with open(self.vocab_file						,	"""w"""						,	encoding="""utf-8"""      ) as vocab_writer:
											vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]      )      )

							UpperCamelCase_:							Any	=						{
							    """do_resize""": True,
							    """size""": 20,
							    """do_center_crop""": True,
							    """crop_size""": 18,
							    """do_normalize""": True,
							    """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
							    """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
							}
							UpperCamelCase_:							Tuple	=						os.path.join(self.tmpdirname						,	snake_case_      )
							with open(self.image_processor_file						,	"""w"""						,	encoding="""utf-8"""      ) as fp:
											json.dump(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       List[Any]						,	**snake_case_ :       Optional[Any]      ):
							return BertTokenizer.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Any						,	**snake_case_ :       List[Any]      ):
							return BertTokenizerFast.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple						,	**snake_case_ :       Any      ):
							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Optional[int]	=						[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta      )]
							UpperCamelCase_:							Tuple	=						[Image.fromarray(np.moveaxis(snake_case_						,	0						,	-1      )      ) for x in image_inputs]
							return image_inputs

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()
							UpperCamelCase_:							Tuple	=						self.get_rust_tokenizer()
							UpperCamelCase_:							Any	=						self.get_image_processor()

							UpperCamelCase_:							Union[str, Any]	=						AlignProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )
							processor_slow.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Optional[Any]	=						AlignProcessor.from_pretrained(self.tmpdirname						,	use_fast=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						AlignProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )
							processor_fast.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Any	=						AlignProcessor.from_pretrained(self.tmpdirname      )

							self.assertEqual(processor_slow.tokenizer.get_vocab()						,	tokenizer_slow.get_vocab()      )
							self.assertEqual(processor_fast.tokenizer.get_vocab()						,	tokenizer_fast.get_vocab()      )
							self.assertEqual(tokenizer_slow.get_vocab()						,	tokenizer_fast.get_vocab()      )
							self.assertIsInstance(processor_slow.tokenizer						,	snake_case_      )
							self.assertIsInstance(processor_fast.tokenizer						,	snake_case_      )

							self.assertEqual(processor_slow.image_processor.to_json_string()						,	image_processor.to_json_string()      )
							self.assertEqual(processor_fast.image_processor.to_json_string()						,	image_processor.to_json_string()      )
							self.assertIsInstance(processor_slow.image_processor						,	snake_case_      )
							self.assertIsInstance(processor_fast.image_processor						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Tuple	=						AlignProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCamelCase_:							int	=						self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
							UpperCamelCase_:							int	=						self.get_image_processor(do_normalize=snake_case_						,	padding_value=1.0      )

							UpperCamelCase_:							Dict	=						AlignProcessor.from_pretrained(
							    self.tmpdirname						,	bos_token="""(BOS)"""						,	eos_token="""(EOS)"""						,	do_normalize=snake_case_						,	padding_value=1.0      )

							self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab()      )
							self.assertIsInstance(processor.tokenizer						,	snake_case_      )

							self.assertEqual(processor.image_processor.to_json_string()						,	image_processor_add_kwargs.to_json_string()      )
							self.assertIsInstance(processor.image_processor						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Optional[int]	=						self.get_image_processor()
							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer()

							UpperCamelCase_:							Tuple	=						AlignProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							str	=						self.prepare_image_inputs()

							UpperCamelCase_:							int	=						image_processor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							List[Any]	=						processor(images=snake_case_						,	return_tensors="""np"""      )

							for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()						,	input_processor[key].sum()						,	delta=1e-2      )

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							str	=						self.get_image_processor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						AlignProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							int	=						"""lower newer"""

							UpperCamelCase_:							Optional[int]	=						processor(text=snake_case_      )

							UpperCamelCase_:							Any	=						tokenizer(snake_case_						,	padding="""max_length"""						,	max_length=64      )

							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]						,	encoded_processor[key]      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						self.get_image_processor()
							UpperCamelCase_:							List[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Any	=						AlignProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							Tuple	=						"""lower newer"""
							UpperCamelCase_:							Optional[int]	=						self.prepare_image_inputs()

							UpperCamelCase_:							Union[str, Any]	=						processor(text=snake_case_						,	images=snake_case_      )

							self.assertListEqual(list(inputs.keys()      )						,	["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]      )

							# test if it raises when no input is passed
							with pytest.raises(snake_case_      ):
											processor()

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Dict	=						self.get_image_processor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							Any	=						AlignProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							int	=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase_:							Dict	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							Tuple	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							int	=						self.get_image_processor()
							UpperCamelCase_:							int	=						self.get_tokenizer()

							UpperCamelCase_:							Any	=						AlignProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							List[Any]	=						"""lower newer"""
							UpperCamelCase_:							Dict	=						self.prepare_image_inputs()

							UpperCamelCase_:							Optional[Any]	=						processor(text=snake_case_						,	images=snake_case_      )

							self.assertListEqual(list(inputs.keys()      )						,	processor.model_input_names      )








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      )






