



'''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 			_lowercase							(     __UpperCAmelCase			):





	"""simple docstring"""




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.config_class(**self.inputs_dict	)
						self.parent.assertTrue(hasattr(lowercase_	, "hidden_sizes"	)	)
						self.parent.assertTrue(hasattr(lowercase_	, "num_encoder_blocks"	)	)




class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_=13	, lowerCamelCase_=64	, lowerCamelCase_=3	, lowerCamelCase_=4	, lowerCamelCase_=[2, 2, 2, 2]	, lowerCamelCase_=[8, 4, 2, 1]	, lowerCamelCase_=[16, 32, 64, 128]	, lowerCamelCase_=[1, 4, 8, 16]	, lowerCamelCase_=False	, lowerCamelCase_=True	, lowerCamelCase_="gelu"	, lowerCamelCase_=0.1	, lowerCamelCase_=0.02	, lowerCamelCase_=3	, lowerCamelCase_=None	, ):
						"""simple docstring"""

						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   UpperCamelCase_ (self	):
						"""simple docstring"""

						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   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     PoolFormerModel(config=lowercase_	)
						model.to(lowercase_	)
						model.eval()
						a        =     model(lowercase_	)
						a        =     self.image_size // 32.0
						self.parent.assertEqual(
						    result.last_hidden_state.shape	, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.prepare_config_and_inputs()
						a        =     config_and_inputs
						a        =     {'''pixel_values''': pixel_values}
						return config, inputs_dict




@require_torch
class 			_lowercase							(     __UpperCAmelCase,   __UpperCAmelCase,   unittest.TestCase			):





	"""simple docstring"""
	__A					=							(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
	__A					=							(
	    {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__A					=							False
	__A					=							False
	__A					=							False
	__A					=							False
	__A					=							False




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     PoolFormerModelTester(self	)
						a        =     PoolFormerConfigTester(self	, config_class=lowercase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						self.config_tester.run_common_tests()




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase_	)




	@unittest.skip("PoolFormer does not use inputs_embeds"	)
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						pass




	@unittest.skip("PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"	)
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						pass




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						def check_hidden_states_output(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
											a        =     model_class(lowercase_	)
											model.to(lowercase_	)
											model.eval()

											with torch.no_grad():
																a        =     model(**self._prepare_for_class(lowercase_	, lowercase_	)	)

											a        =     outputs.hidden_states

											a        =     self.model_tester.num_encoder_blocks
											self.assertEqual(len(lowercase_	)	, lowercase_	)

											# 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        =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											a        =     True
											check_hidden_states_output(lowercase_	, lowercase_	, lowercase_	)

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

											check_hidden_states_output(lowercase_	, lowercase_	, lowercase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						if not self.model_tester.is_training:
											return

						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(lowercase_	):
																continue
											a        =     model_class(lowercase_	)
											model.to(lowercase_	)
											model.train()
											a        =     self._prepare_for_class(lowercase_	, lowercase_	, return_labels=lowercase_	)
											a        =     model(**lowercase_	).loss
											loss.backward()




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											a        =     model_class(lowercase_	)
											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]	, lowercase_	)




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a        =     PoolFormerModel.from_pretrained(lowercase_	)
											self.assertIsNotNone(lowercase_	)






def 					a(   )	-> Any:
					"""simple docstring"""





					a        =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
					return image







@require_torch
class 			_lowercase							(     unittest.TestCase			):





	"""simple docstring"""




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     PoolFormerImageProcessor()
						a        =     PoolFormerForImageClassification.from_pretrained("sail/poolformer_s12"	).to(lowercase_	)

						a        =     image_processor(images=prepare_img()	, return_tensors="pt"	).to(lowercase_	)

						# forward pass
						with torch.no_grad():
											a        =     model(**lowercase_	)

						# verify the logits
						a        =     torch.Size((1, 1000)	)
						self.assertEqual(outputs.logits.shape	, lowercase_	)

						a        =     torch.tensor([-0.6113, 0.1685, -0.0492]	).to(lowercase_	)
						self.assertTrue(torch.allclose(outputs.logits[0, :3]	, lowercase_	, atol=1E-4	)	)





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls




	@slow
	@require_torch_gpu
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.get_auto_remove_tmp_dir()
						a        =     F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						a        =     get_gpu_count()
						a        =     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
						a        =     [sys.executable] + distributed_args + args
						execute_subprocess_async(lowerCamelCase_	, env=self.get_env()	)

						a        =     os.path.join(lowerCamelCase_	, "test_bleu.json"	)
						a        =     load_json(lowerCamelCase_	)
						# print(metrics)
						self.assertGreaterEqual(metrics["bleu"]	, 25	)
