



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
A_    :					Dict             ={
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}




def   lowerCamelCase_       (					UpperCAmelCase__      ):



						"""simple docstring"""




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




def   lowerCamelCase_       (					):



						"""simple docstring"""




						a_					=		sys.argv[0]

						a_					=		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=UpperCAmelCase__       ,    required=UpperCAmelCase__       ,    help="""param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""""       ,    )
						parser.add_argument(
						    """--bs"""       ,    type=UpperCAmelCase__       ,    default=8       ,    required=UpperCAmelCase__       ,    help="""initial batch size (may get reduced if it\'s too big)"""      )
						parser.add_argument("""--task"""       ,    type=UpperCAmelCase__       ,    help="""used for task_specific_params + metrics"""      )
						parser.add_argument(
						    """--info"""       ,    nargs="""?"""       ,    type=UpperCAmelCase__       ,    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."""
						    )       ,    )
						a_			,   a_					=		parser.parse_known_args()
						# we share some of the args
						args_main.extend(["""--task""", args.task]      )
						a_					=		[prog] + args_main

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

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

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

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

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

						a_					=		sorted(UpperCAmelCase__       ,    key=operator.itemgetter(*task_score_names[task]      )       ,    reverse=UpperCAmelCase__      )
						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]      )      )

						a_					=		results_sorted[0]
						for score in task_score_names[task]:
												del best[score]
						a_					=		[F"--{k} {v}" for k, v in best.items()]
						a_					=		["""--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()



from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
	import torch





class        lowercase_				( UpperCamelCase__):






							"""simple docstring"""







							snake_case_        =		['''image_processor''', '''tokenizer''']
							snake_case_        =		'''OneFormerImageProcessor'''
							snake_case_        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


							def __init__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = 77			,    _UpperCAmelCase = 77			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if image_processor is None:
																			raise ValueError("""You need to specify an `image_processor`."""		)
													if tokenizer is None:
																			raise ValueError("""You need to specify a `tokenizer`."""		)

													a_					=		max_seq_length
													a_					=		task_seq_length

													super().__init__(_UpperCAmelCase			,    _UpperCAmelCase		)


							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=77		):







													"""simple docstring"""


													if text_list is None:
																			raise ValueError("""tokens cannot be None."""		)

													a_					=		self.tokenizer(_UpperCAmelCase			,    padding="""max_length"""			,    max_length=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)

													a_			,   a_					=		tokens["""attention_mask"""], tokens["""input_ids"""]

													a_					=		[]
													for attn_mask, input_id in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
																			token_inputs.append(token.unsqueeze(0		)		)

													a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
													return token_inputs


							def __call__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if task_inputs is None:
																			raise ValueError("""You have to specify the task_input. Found None."""		)
													elif images is None:
																			raise ValueError("""You have to specify the image. Found None."""		)

													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

													a_					=		self.image_processor(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		[task_inputs]

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
																			a_					=		[]
																			for task in task_inputs:
																									a_					=		f"the task is {task}"
																									task_token_inputs.append(_UpperCAmelCase		)
																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
													else:
																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)

													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
																			a_					=		encoded_inputs.text_inputs

																			a_					=		[]
																			for texts in texts_list:
																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
																									text_inputs.append(text_input_list.unsqueeze(0		)		)

																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)

													return encoded_inputs


							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if task_inputs is None:
																			raise ValueError("""You have to specify the task_input. Found None."""		)
													elif images is None:
																			raise ValueError("""You have to specify the image. Found None."""		)

													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

													a_					=		self.image_processor.encode_inputs(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		[task_inputs]

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
																			a_					=		[]
																			for task in task_inputs:
																									a_					=		f"the task is {task}"
																									task_token_inputs.append(_UpperCAmelCase		)
																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
													else:
																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)

													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
																			a_					=		encoded_inputs.text_inputs

																			a_					=		[]
																			for texts in texts_list:
																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
																									text_inputs.append(text_input_list.unsqueeze(0		)		)

																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)

													return encoded_inputs


							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)


							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)






							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)