

import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
		from transformers import MarkupLMFeatureExtractor


class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			parent







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return {}







def a():





	'''simple docstring'''
	snake_case_   		=			'<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR="FFFFFF">\n    <HR>\n    <a href="http://google.com">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style="color:#0000FF">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'

	snake_case_   		=			'\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

	return [html_string_a, html_string_a]






@require_bsa
class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	MarkupLMFeatureExtractor if is_bsa_available() else None


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			MarkupLMFeatureExtractionTester(self				)


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.feature_extract_tester.prepare_feat_extract_dict()







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feature_extraction_class()

				# Test not batched input
				snake_case_   		=			get_html_strings()[0]
				snake_case_   		=			feature_extractor(__UpperCamelCase				)

				# fmt: off
				snake_case_   		=			[['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
				snake_case_   		=			[['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
				# fmt: on

				self.assertEqual(encoding.nodes			,					__UpperCamelCase				)
				self.assertEqual(encoding.xpaths			,					__UpperCamelCase				)

				# Test batched
				snake_case_   		=			get_html_strings()
				snake_case_   		=			feature_extractor(__UpperCamelCase				)

				# fmt: off
				snake_case_   		=			expected_nodes + [['My First Heading', 'My first paragraph.']]
				snake_case_   		=			expected_xpaths + [['/html/body/h1', '/html/body/p']]

				self.assertEqual(len(encoding.nodes				)			,					2				)
				self.assertEqual(len(encoding.xpaths				)			,					2				)

				self.assertEqual(encoding.nodes			,					__UpperCamelCase				)
				self.assertEqual(encoding.xpaths			,					__UpperCamelCase				)



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									=  logging.getLogger()

A									=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = "pytorch"				):

				"""simple docstring"""


				snake_case_   		=			self.get_auto_remove_tmp_dir()
				snake_case_   		=			os.path.join(__UpperCamelCase			,					'output'				)
				snake_case_   		=			os.path.join(__UpperCamelCase			,					'data'				)
				self._create_dummy_data(data_dir=__UpperCamelCase				)

				snake_case_   		=			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'				)

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

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


			@require_torch_gpu
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			@require_torch_multi_gpu
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			@require_torch_gpu
			@require_ray
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			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				):

				"""simple docstring"""


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