"""simple docstring"""




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






def 	UpperCAmelCase       (						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 	UpperCAmelCase       (						):






							"""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()






"""simple docstring"""




import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
				import torch

				from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
				from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor
class      UpperCamelCase__(      __A			):





						def 					snake_case__						(							self  )       ->					int:
													A__						  =				self.config_class(**self.inputs_dict  )
													self.parent.assertTrue(hasattr(__UpperCAmelCase		,'hidden_sizes'  )  )
													self.parent.assertTrue(hasattr(__UpperCAmelCase		,'num_encoder_blocks'  )  )






class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase=13		,__UpperCAmelCase=64		,__UpperCAmelCase=3		,__UpperCAmelCase=4		,__UpperCAmelCase=[2, 2, 2, 2]		,__UpperCAmelCase=[8, 4, 2, 1]		,__UpperCAmelCase=[16, 32, 64, 1_28]		,__UpperCAmelCase=[1, 4, 8, 16]		,__UpperCAmelCase=False		,__UpperCAmelCase=True		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=3		,__UpperCAmelCase=None		,)       ->					Tuple:
													A__						  =				parent
													A__						  =				batch_size
													A__						  =				image_size
													A__						  =				num_channels
													A__						  =				num_encoder_blocks
													A__						  =				sr_ratios
													A__						  =				depths
													A__						  =				hidden_sizes
													A__						  =				downsampling_rates
													A__						  =				is_training
													A__						  =				use_labels
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				initializer_range
													A__						  =				num_labels
													A__						  =				scope





						def 					snake_case__						(							self  )       ->					Tuple:
													A__						  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

													A__						  =				None
													if self.use_labels:
																				A__						  =				ids_tensor([self.batch_size, self.image_size, self.image_size]		,self.num_labels  )

													A__						  =				PoolFormerConfig(
													    image_size=self.image_size		,num_channels=self.num_channels		,num_encoder_blocks=self.num_encoder_blocks		,depths=self.depths		,hidden_sizes=self.hidden_sizes		,hidden_act=self.hidden_act		,hidden_dropout_prob=self.hidden_dropout_prob		,initializer_range=self.initializer_range		,)

													return config, pixel_values, labels





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Union[str, Any]:
													A__						  =				PoolFormerModel(config=__UpperCAmelCase  )
													model.to(__UpperCAmelCase  )
													model.eval()
													A__						  =				model(__UpperCAmelCase  )
													A__						  =				A__						  =				self.image_size // 3_2.0
													self.parent.assertEqual(
													    result.last_hidden_state.shape		,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)  )





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				self.prepare_config_and_inputs()
													A__  ,	A__  ,	A__						  =				config_and_inputs
													A__						  =				{'pixel_values': pixel_values}
													return config, inputs_dict






@require_torch
class      UpperCamelCase__(      __A		,    __A		,    unittest.TestCase			):
						lowerCAmelCase__	:    Optional[int]    				=		(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
						lowerCAmelCase__	:    Optional[Any]    				=		(
						    {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase__	:    Any    				=		False
						lowerCAmelCase__	:    str    				=		False
						lowerCAmelCase__	:    Any    				=		False
						lowerCAmelCase__	:    str    				=		False
						lowerCAmelCase__	:    Optional[int]    				=		False





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				PoolFormerModelTester(self  )
													A__						  =				PoolFormerConfigTester(self		,config_class=__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					int:
													self.config_tester.run_common_tests()





						def 					snake_case__						(							self  )       ->					Tuple:
													A__						  =				self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCAmelCase  )





						@unittest.skip('PoolFormer does not use inputs_embeds'  )
						def 					snake_case__						(							self  )       ->					int:
													pass





						@unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'  )
						def 					snake_case__						(							self  )       ->					List[str]:
													pass





						def 					snake_case__						(							self  )       ->					List[Any]:
													def check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  ):
																				A__						  =				model_class(__UpperCAmelCase  )
																				model.to(__UpperCAmelCase  )
																				model.eval()

																				with torch.no_grad():
																											A__						  =				model(**self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase  )  )

																				A__						  =				outputs.hidden_states

																				A__						  =				self.model_tester.num_encoder_blocks
																				self.assertEqual(len(__UpperCAmelCase  )		,__UpperCAmelCase  )

																				# verify the first hidden states (first block)
																				self.assertListEqual(
																				    list(hidden_states[0].shape[-3:]  )		,[
																				        self.model_tester.hidden_sizes[0],
																				        self.model_tester.image_size // 4,
																				        self.model_tester.image_size // 4,
																				    ]		,)

													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__						  =				True
																				check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				A__						  =				True

																				check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													if not self.model_tester.is_training:
																				return

													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()
													A__						  =				True

													for model_class in self.all_model_classes:
																				if model_class in get_values(__UpperCAmelCase  ):
																											continue
																				A__						  =				model_class(__UpperCAmelCase  )
																				model.to(__UpperCAmelCase  )
																				model.train()
																				A__						  =				self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase		,return_labels=__UpperCAmelCase  )
																				A__						  =				model(**__UpperCAmelCase  ).loss
																				loss.backward()





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__						  =				model_class(__UpperCAmelCase  )
																				A__						  =				inspect.signature(model.forward  )
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				A__						  =				[*signature.parameters.keys()]

																				A__						  =				['pixel_values']
																				self.assertListEqual(arg_names[:1]		,__UpperCAmelCase  )





						@slow
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				A__						  =				PoolFormerModel.from_pretrained(__UpperCAmelCase  )
																				self.assertIsNotNone(__UpperCAmelCase  )






def 	UpperCAmelCase       (						):






							"""simple docstring"""





							A__						  =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							return image


@require_torch
class      UpperCamelCase__(      unittest.TestCase			):





						@slow
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__						  =				PoolFormerImageProcessor()
													A__						  =				PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'  ).to(__UpperCAmelCase  )

													A__						  =				image_processor(images=prepare_img()		,return_tensors='pt'  ).to(__UpperCAmelCase  )

													# forward pass
													with torch.no_grad():
																				A__						  =				model(**__UpperCAmelCase  )

													# verify the logits
													A__						  =				torch.Size((1, 10_00)  )
													self.assertEqual(outputs.logits.shape		,__UpperCAmelCase  )

													A__						  =				torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]  ).to(__UpperCAmelCase  )
													self.assertTrue(torch.allclose(outputs.logits[0, :3]		,__UpperCAmelCase		,atol=1e-4  )  )





