
import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
lowercase__				:				int						=      {
    "translation": ["bleu"],
    "summarization": ROUGE_KEYS,
}




def 						A_			(				snake_case				: Dict			)   ->     Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      search.split()
						__UpperCamelCase  	=      dict((g.split('''='''			) for g in groups)			)
						__UpperCamelCase  	=      list(entries.keys()			)
						__UpperCamelCase  	=      [[f"--{k} {v}" for v in vs.split(''':'''			)] for k, vs in entries.items()]
						__UpperCamelCase  	=      [list(snake_case			) for x in itertools.product(*snake_case			)]
						return matrix, entry_names




def 						A_			(				)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      sys.argv[0]

						__UpperCamelCase  	=      argparse.ArgumentParser(
						    usage=(
						        '''\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'''
						        ''' refer to `run_eval.py -h` for the complete list.'''
						    )			)
						parser.add_argument(
						    '''--search'''			,   type=snake_case			,   required=snake_case			,   help='''param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'''			,   )
						parser.add_argument(
						    '''--bs'''			,   type=snake_case			,   default=8			,   required=snake_case			,   help='''initial batch size (may get reduced if it\'s too big)'''			)
						parser.add_argument('''--task'''			,   type=snake_case			,   help='''used for task_specific_params + metrics'''			)
						parser.add_argument(
						    '''--info'''			,   nargs='''?'''			,   type=snake_case			,   const=datetime_now()			,   help=(
						        '''add custom notes to be printed before the results table. If no value is passed, the current datetime'''
						        ''' string will be used.'''
						    )			,   )
						__UpperCamelCase	,				__UpperCamelCase  	=      parser.parse_known_args()
						# we share some of the args
						args_main.extend(['''--task''', args.task]			)
						__UpperCamelCase  	=      [prog] + args_main

						# to support variations like translation_en_to_de"
						__UpperCamelCase  	=      '''translation''' if '''translation''' in args.task else '''summarization'''

						__UpperCamelCase	,				__UpperCamelCase  	=      parse_search_arg(args.search			)
						__UpperCamelCase  	=      task_score_names[task]  # score cols first
						__UpperCamelCase  	=      {col: len(str(snake_case			)			) for col in col_names}
						__UpperCamelCase  	=      []
						for r in matrix:
												__UpperCamelCase  	=      dict((x.replace('''--'''			,   ''''''			).split() for x in r)			)
												__UpperCamelCase  	=      ''' '''.join(snake_case			).split()
												args_exp.extend(['''--bs''', str(args.bs			)]			)  # in case we need to reduce its size due to CUDA OOM
												__UpperCamelCase  	=      args_normal + args_exp

												# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

												__UpperCamelCase  	=      run_generate(verbose=snake_case			)
												# make sure scores are first in the table
												__UpperCamelCase  	=      OrderedDict()
												for score in task_score_names[task]:
																		__UpperCamelCase  	=      scores[score]
												result.update(snake_case			)
												results.append(snake_case			)

												# find widest entries
												for k, v in result.items():
																		__UpperCamelCase  	=      len(str(snake_case			)			)
																		if l > col_widths[k]:
																								__UpperCamelCase  	=      l

						__UpperCamelCase  	=      sorted(snake_case			,   key=operator.itemgetter(*task_score_names[task]			)			,   reverse=snake_case			)
						print(''' | '''.join([f"{col:{col_widths[col]}}" for col in col_names]			)			)
						print(''' | '''.join([f"{'-'*col_widths[col]}" for col in col_names]			)			)
						for row in results_sorted:
												print(''' | '''.join([f"{row[col]:{col_widths[col]}}" for col in col_names]			)			)

						__UpperCamelCase  	=      results_sorted[0]
						for score in task_score_names[task]:
												del best[score]
						__UpperCamelCase  	=      [f"--{k} {v}" for k, v in best.items()]
						__UpperCamelCase  	=      ['''--bs''', str(args.bs			)]
						if args.info:
												print(f"\nInfo: {args.info}"			)
						print('''\nBest score args:'''			)
						print(''' '''.join(args_main + best_args + dyn_args			)			)

						return results_sorted


if __name__ == "__main__":
				# Usage:
				# [normal-run_eval_search.py cmd plus] \
				# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
				#
				# Example:
				# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
				# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
				# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
				# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
				run_search()












import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    MarkupLMForQuestionAnswering,
				    MarkupLMForSequenceClassification,
				    MarkupLMForTokenClassification,
				    MarkupLMModel,
				)

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer


class 				__a				:





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__=13			,     UpperCamelCase__=7			,     UpperCamelCase__=True			,     UpperCamelCase__=True			,     UpperCamelCase__=True			,     UpperCamelCase__=True			,     UpperCamelCase__=99			,     UpperCamelCase__=32			,     UpperCamelCase__=5			,     UpperCamelCase__=4			,     UpperCamelCase__=37			,     UpperCamelCase__="gelu"			,     UpperCamelCase__=0.1			,     UpperCamelCase__=0.1			,     UpperCamelCase__=512			,     UpperCamelCase__=16			,     UpperCamelCase__=2			,     UpperCamelCase__=0.02			,     UpperCamelCase__=3			,     UpperCamelCase__=None			,     UpperCamelCase__=20			,     UpperCamelCase__=30			,     UpperCamelCase__=2			,     UpperCamelCase__=2			,     UpperCamelCase__=10			,     ):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= parent
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= batch_size
						SCREAMING_SNAKE_CASE_ :						int    				= seq_length
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= is_training
						SCREAMING_SNAKE_CASE_ :						Dict    				= use_input_mask
						SCREAMING_SNAKE_CASE_ :						Dict    				= use_token_type_ids
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= use_labels
						SCREAMING_SNAKE_CASE_ :						int    				= vocab_size
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= hidden_size
						SCREAMING_SNAKE_CASE_ :						Any    				= num_hidden_layers
						SCREAMING_SNAKE_CASE_ :						int    				= num_attention_heads
						SCREAMING_SNAKE_CASE_ :						Any    				= intermediate_size
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= hidden_act
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= hidden_dropout_prob
						SCREAMING_SNAKE_CASE_ :						List[str]    				= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= max_position_embeddings
						SCREAMING_SNAKE_CASE_ :						Tuple    				= type_vocab_size
						SCREAMING_SNAKE_CASE_ :						str    				= type_sequence_label_size
						SCREAMING_SNAKE_CASE_ :						Dict    				= initializer_range
						SCREAMING_SNAKE_CASE_ :						int    				= num_labels
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= scope
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= max_xpath_tag_unit_embeddings
						SCREAMING_SNAKE_CASE_ :						Tuple    				= max_xpath_subs_unit_embeddings
						SCREAMING_SNAKE_CASE_ :						Dict    				= tag_pad_id
						SCREAMING_SNAKE_CASE_ :						str    				= subs_pad_id
						SCREAMING_SNAKE_CASE_ :						Tuple    				= max_depth


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= ids_tensor(
						    [self.batch_size, self.seq_length, self.max_depth]			,     self.max_xpath_tag_unit_embeddings							)

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= ids_tensor(
						    [self.batch_size, self.seq_length, self.max_depth]			,     self.max_xpath_subs_unit_embeddings							)

						SCREAMING_SNAKE_CASE_ :						Any    				= None
						if self.use_input_mask:
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= ids_tensor([self.batch_size, self.seq_length]			,     vocab_size=2							)

						SCREAMING_SNAKE_CASE_ :						Dict    				= None
						if self.use_token_type_ids:
										SCREAMING_SNAKE_CASE_ :						int    				= ids_tensor([self.batch_size, self.seq_length]			,     self.type_vocab_size							)

						SCREAMING_SNAKE_CASE_ :						int    				= None
						SCREAMING_SNAKE_CASE_ :						Tuple    				= None
						if self.use_labels:
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= ids_tensor([self.batch_size]			,     self.type_sequence_label_size							)
										SCREAMING_SNAKE_CASE_ :						Any    				= ids_tensor([self.batch_size, self.seq_length]			,     self.num_labels							)

						SCREAMING_SNAKE_CASE_ :						Tuple    				= self.get_config()

						return (
						    config,
						    input_ids,
						    xpath_tags_seq,
						    xpath_subs_seq,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						)


		def 	__snake_case      (    self							):
						return MarkupLMConfig(
						    vocab_size=self.vocab_size			,     hidden_size=self.hidden_size			,     num_hidden_layers=self.num_hidden_layers			,     num_attention_heads=self.num_attention_heads			,     intermediate_size=self.intermediate_size			,     hidden_act=self.hidden_act			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     max_position_embeddings=self.max_position_embeddings			,     type_vocab_size=self.type_vocab_size			,     initializer_range=self.initializer_range			,     max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings			,     max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings			,     tag_pad_id=self.tag_pad_id			,     subs_pad_id=self.subs_pad_id			,     max_depth=self.max_depth			,     )


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     ):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= MarkupLMModel(config=UpperCamelCase__							)
						model.to(UpperCamelCase__							)
						model.eval()
						print('Configs:'			,     model.config.tag_pad_id			,     model.config.subs_pad_id							)
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= model(UpperCamelCase__			,     attention_mask=UpperCamelCase__			,     token_type_ids=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						List[str]    				= model(UpperCamelCase__			,     token_type_ids=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						int    				= model(UpperCamelCase__							)
						self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)							)
						self.parent.assertEqual(result.pooler_output.shape			,     (self.batch_size, self.hidden_size)							)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     ):
						SCREAMING_SNAKE_CASE_ :						Dict    				= self.num_labels
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= MarkupLMForSequenceClassification(UpperCamelCase__							)
						model.to(UpperCamelCase__							)
						model.eval()
						SCREAMING_SNAKE_CASE_ :						str    				= model(
						    UpperCamelCase__			,     xpath_tags_seq=UpperCamelCase__			,     xpath_subs_seq=UpperCamelCase__			,     attention_mask=UpperCamelCase__			,     token_type_ids=UpperCamelCase__			,     labels=UpperCamelCase__			,     )
						self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)							)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     ):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.num_labels
						SCREAMING_SNAKE_CASE_ :						List[str]    				= MarkupLMForTokenClassification(config=UpperCamelCase__							)
						model.to(UpperCamelCase__							)
						model.eval()
						SCREAMING_SNAKE_CASE_ :						str    				= model(
						    UpperCamelCase__			,     xpath_tags_seq=UpperCamelCase__			,     xpath_subs_seq=UpperCamelCase__			,     attention_mask=UpperCamelCase__			,     token_type_ids=UpperCamelCase__			,     labels=UpperCamelCase__			,     )
						self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.num_labels)							)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     ):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= MarkupLMForQuestionAnswering(config=UpperCamelCase__							)
						model.to(UpperCamelCase__							)
						model.eval()
						SCREAMING_SNAKE_CASE_ :						int    				= model(
						    UpperCamelCase__			,     xpath_tags_seq=UpperCamelCase__			,     xpath_subs_seq=UpperCamelCase__			,     attention_mask=UpperCamelCase__			,     token_type_ids=UpperCamelCase__			,     start_positions=UpperCamelCase__			,     end_positions=UpperCamelCase__			,     )
						self.parent.assertEqual(result.start_logits.shape			,     (self.batch_size, self.seq_length)							)
						self.parent.assertEqual(result.end_logits.shape			,     (self.batch_size, self.seq_length)							)






		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Any    				= self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							
						) :						int    				= config_and_inputs
						SCREAMING_SNAKE_CASE_ :						Any    				= {
						    'input_ids': input_ids,
						    'xpath_tags_seq': xpath_tags_seq,
						    'xpath_subs_seq': xpath_subs_seq,
						    'token_type_ids': token_type_ids,
						    'attention_mask': input_mask,
						}
						return config, inputs_dict






@require_torch
class 				__a				(	__A			,						__A			,						unittest.TestCase    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Dict					     =    (
		    (
		        MarkupLMModel,
		        MarkupLMForSequenceClassification,
		        MarkupLMForTokenClassification,
		        MarkupLMForQuestionAnswering,
		    )
		    if is_torch_available()
		    else None
		)
		UpperCAmelCase__   :					Union[str, Any]					     =    (
		    {
		        """feature-extraction""": MarkupLMModel,
		        """question-answering""": MarkupLMForQuestionAnswering,
		        """text-classification""": MarkupLMForSequenceClassification,
		        """token-classification""": MarkupLMForTokenClassification,
		        """zero-shot""": MarkupLMForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
						# (batch of pretokenized examples).
						return True


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						int    				= MarkupLMModelTester(self							)
						SCREAMING_SNAKE_CASE_ :						Any    				= ConfigTester(self			,     config_class=UpperCamelCase__			,     hidden_size=37							)


		def 	__snake_case      (    self							):
						self.config_tester.run_common_tests()


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase__							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Any    				= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__							)






		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__							)






def        _lowerCamelCase(      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						int    				= '\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    '

				return html_string


@require_torch
class 				__a				(	unittest.TestCase    ):





		'''simple docstring'''



		@cached_property
		def 	__snake_case      (    self							):
						# TODO use from_pretrained here
						SCREAMING_SNAKE_CASE_ :						List[str]    				= MarkupLMFeatureExtractor()
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= MarkupLMTokenizer.from_pretrained('microsoft/markuplm-base'							)

						return MarkupLMProcessor(UpperCamelCase__			,     UpperCamelCase__							)






		@slow
		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Tuple    				= MarkupLMModel.from_pretrained('microsoft/markuplm-base'							).to(UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Tuple    				= self.default_processor

						SCREAMING_SNAKE_CASE_ :						int    				= processor(prepare_html_string()			,     return_tensors='pt'							)
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= inputs.to(UpperCamelCase__							)

						# forward pass
						with torch.no_grad():
										SCREAMING_SNAKE_CASE_ :						str    				= model(**UpperCamelCase__							)

						# verify the last hidden states
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= torch.Size([1, 14, 768]							)
						self.assertEqual(outputs.last_hidden_state.shape			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Tuple    				= torch.tensor(
						    [[0.06_75, -0.00_52, 0.50_01], [-0.22_81, 0.08_02, 0.21_92], [-0.05_83, -0.33_11, 0.11_85]]							).to(UpperCamelCase__							)

						self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]			,     UpperCamelCase__			,     atol=1E-4							)							)