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
_SCREAMING_SNAKE_CASE:    str     						=							{
    '''translation''': ['''bleu'''],
    '''summarization''': ROUGE_KEYS,
}






def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





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






def  UpperCAmelCase__(    ):



			"""simple docstring"""





			_snake_case														=   sys.argv[0]

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

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

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

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

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

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

			_snake_case														=   sorted(lowerCamelCase_				,						key=operator.itemgetter(*task_score_names[task]	)				,						reverse=lowerCamelCase_	)
			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]	)	)

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

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ViltImageProcessor

class 						_SCREAMING_SNAKE_CASE				(  unittest.TestCase				):






		"""simple docstring"""







		def __init__(      self     :					Optional[int]	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=7	,       __SCREAMING_SNAKE_CASE     :					Dict=3	,       __SCREAMING_SNAKE_CASE     :					Any=18	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=30	,       __SCREAMING_SNAKE_CASE     :					int=400	,       __SCREAMING_SNAKE_CASE     :					int=True	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=None	,       __SCREAMING_SNAKE_CASE     :					Any=2	,       __SCREAMING_SNAKE_CASE     :					Any=True	,       __SCREAMING_SNAKE_CASE     :					int=[0.5, 0.5, 0.5]	,       __SCREAMING_SNAKE_CASE     :					List[str]=[0.5, 0.5, 0.5]	,       ):






					"""simple docstring"""
					_snake_case														=   size if size is not None else {"shortest_edge": 30}
					_snake_case														=   parent
					_snake_case														=   batch_size
					_snake_case														=   num_channels
					_snake_case														=   image_size
					_snake_case														=   min_resolution
					_snake_case														=   max_resolution
					_snake_case														=   do_resize
					_snake_case														=   size
					_snake_case														=   size_divisor
					_snake_case														=   do_normalize
					_snake_case														=   image_mean
					_snake_case														=   image_std



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "size_divisor": self.size_divisor,
					}







		def 					_a					(      self     :					Any	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					List[Any]=False ):






					"""simple docstring"""
					if not batched:
								_snake_case														=   self.size["shortest_edge"]
								_snake_case														=   image_inputs[0]
								if isinstance(__SCREAMING_SNAKE_CASE	,       Image.Image ):
											_snake_case							,			_snake_case														=   image.size
								else:
											_snake_case							,			_snake_case														=   image.shape[1], image.shape[2]
								_snake_case														=   size / min(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
								if h < w:
											_snake_case							,			_snake_case														=   size, scale * w
								else:
											_snake_case							,			_snake_case														=   scale * h, size

								_snake_case														=   int((1_333 / 800) * size )
								if max(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) > max_size:
											_snake_case														=   max_size / max(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
											_snake_case														=   newh * scale
											_snake_case														=   neww * scale

								_snake_case							,			_snake_case														=   int(newh + 0.5 ), int(neww + 0.5 )
								_snake_case							,			_snake_case														=   (
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

					else:
								_snake_case														=   []
								for image in image_inputs:
											_snake_case							,			_snake_case														=   self.get_expected_values([image] )
											expected_values.append((expected_height, expected_width) )
								_snake_case														=   max(__SCREAMING_SNAKE_CASE	,       key=lambda __SCREAMING_SNAKE_CASE : item[0] )[0]
								_snake_case														=   max(__SCREAMING_SNAKE_CASE	,       key=lambda __SCREAMING_SNAKE_CASE : item[1] )[1]

					return expected_height, expected_width

@require_torch
@require_vision
class 						_SCREAMING_SNAKE_CASE				(  _lowercase   ,  unittest.TestCase				):






		"""simple docstring"""







		A_    			=   ViltImageProcessor if is_vision_available() else None



		def 					_a					(      self     :					Tuple ):






					"""simple docstring"""
					_snake_case														=   ViltImageProcessingTester(self )



		@property
		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					return self.image_processor_tester.prepare_image_processor_dict()



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "image_mean" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "image_std" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "do_normalize" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "do_resize" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "size" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "size_divisor" ) )



		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case														=   self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size	,       {"shortest_edge": 30} )

					_snake_case														=   self.image_processing_class.from_dict(self.image_processor_dict	,       size=42 )
					self.assertEqual(image_processor.size	,       {"shortest_edge": 42} )



		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					pass



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       Image.Image )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE	,       numpify=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       np.ndarray )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )







		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE	,       torchify=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       torch.Tensor )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )

