


import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
				import jax
				import jax.numpy as jnp

				from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor







class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__=3       ,		lowerCAmelCase__=3_2       ,		lowerCAmelCase__=3       ,		lowerCAmelCase__=1_0       ,		lowerCAmelCase__=[1_0, 2_0, 3_0, 4_0]       ,		lowerCAmelCase__=[1, 1, 2, 1]       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__="relu"       ,		lowerCAmelCase__=3       ,		lowerCAmelCase__=None       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= parent
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= batch_size
										SCREAMING_SNAKE_CASE_						:			Any       						= image_size
										SCREAMING_SNAKE_CASE_						:			List[str]       						= num_channels
										SCREAMING_SNAKE_CASE_						:			int       						= embeddings_size
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= hidden_sizes
										SCREAMING_SNAKE_CASE_						:			Any       						= depths
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= is_training
										SCREAMING_SNAKE_CASE_						:			str       						= use_labels
										SCREAMING_SNAKE_CASE_						:			str       						= hidden_act
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= num_labels
										SCREAMING_SNAKE_CASE_						:			str       						= scope
										SCREAMING_SNAKE_CASE_						:			Tuple       						= len(lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.get_config()

										return config, pixel_values






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return ResNetConfig(
										    num_channels=self.num_channels       ,		embeddings_size=self.embeddings_size       ,		hidden_sizes=self.hidden_sizes       ,		depths=self.depths       ,		hidden_act=self.hidden_act       ,		num_labels=self.num_labels       ,		image_size=self.image_size       ,		)






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= FlaxResNetModel(config=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model(lowerCAmelCase__    )

										# Output shape (b, c, h, w)
										self.parent.assertEqual(
										    result.last_hidden_state.shape       ,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)       ,		)






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.num_labels
										SCREAMING_SNAKE_CASE_						:			Any       						= FlaxResNetForImageClassification(config=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Any       						= model(lowerCAmelCase__    )
										self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[str]       						= config_and_inputs
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= {'pixel_values': pixel_values}
										return config, inputs_dict







@require_flax
class      __lowercase  (__SCREAMING_SNAKE_CASE     ,    unittest.TestCase   ):





							"""simple docstring"""







							_UpperCAmelCase     =		(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		False






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Dict       						= FlaxResNetModelTester(self    )
										SCREAMING_SNAKE_CASE_						:			Any       						= ConfigTester(self       ,		config_class=lowerCAmelCase__       ,		has_text_modality=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__    )






							@unittest.skip(reason='ResNet does not use inputs_embeds'    )
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							@unittest.skip(reason='ResNet does not support input and output embeddings'    )
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Dict       						= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model_class(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			str       						= inspect.signature(model.__call__    )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													SCREAMING_SNAKE_CASE_						:			int       						= [*signature.parameters.keys()]

													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= ['pixel_values']
													self.assertListEqual(arg_names[:1]       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model_class(lowerCAmelCase__    )

													SCREAMING_SNAKE_CASE_						:			Dict       						= model(**self._prepare_for_class(lowerCAmelCase__       ,		lowerCAmelCase__    )    )

													SCREAMING_SNAKE_CASE_						:			List[Any]       						= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													SCREAMING_SNAKE_CASE_						:			Dict       						= self.model_tester.num_stages
													self.assertEqual(len(lowerCAmelCase__    )       ,		expected_num_stages + 1    )






							@unittest.skip(reason='ResNet does not use feedforward chunking'    )
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[str]       						= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__    ):
																SCREAMING_SNAKE_CASE_						:			List[Any]       						= self._prepare_for_class(lowerCAmelCase__       ,		lowerCAmelCase__    )
																SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model_class(lowerCAmelCase__    )

																@jax.jit
																def model_jitted(lowerCAmelCase__       ,		**lowerCAmelCase__    ):
																			return model(pixel_values=lowerCAmelCase__       ,		**lowerCAmelCase__    )

																with self.subTest('JIT Enabled'    ):
																			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= model_jitted(**lowerCAmelCase__    ).to_tuple()

																with self.subTest('JIT Disabled'    ):
																			with jax.disable_jit():
																						SCREAMING_SNAKE_CASE_						:			int       						= model_jitted(**lowerCAmelCase__    ).to_tuple()

																self.assertEqual(len(lowerCAmelCase__    )       ,		len(lowerCAmelCase__    )    )
																for jitted_output, output in zip(lowerCAmelCase__       ,		lowerCAmelCase__    ):
																			self.assertEqual(jitted_output.shape       ,		output.shape    )







def 		a__       (							):
			SCREAMING_SNAKE_CASE_						:			Any       						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
			return image







@require_flax
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							@cached_property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return AutoImageProcessor.from_pretrained('microsoft/resnet-50'    ) if is_vision_available() else None






							@slow
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Dict       						= FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'    )

										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= self.default_image_processor
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= prepare_img()
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= image_processor(images=lowerCAmelCase__       ,		return_tensors='np'    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(**lowerCAmelCase__    )

										# verify the logits
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= (1, 1_0_0_0)
										self.assertEqual(outputs.logits.shape       ,		lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= jnp.array([-11.1_069, -9.7_877, -8.3_777]    )

										self.assertTrue(jnp.allclose(outputs.logits[0, :3]       ,		lowerCAmelCase__       ,		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  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							@classmethod
							def  UpperCamelCase__			(	cls    ):





										"""simple docstring"""




										return cls






							@slow
							@require_torch_gpu
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE_						:			Tuple       						= 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
										SCREAMING_SNAKE_CASE_						:			Dict       						= get_gpu_count()
										SCREAMING_SNAKE_CASE_						:			int       						= F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= [sys.executable] + distributed_args + args
										execute_subprocess_async(lowerCAmelCase__       ,		env=self.get_env()    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= os.path.join(lowerCAmelCase__       ,		'test_bleu.json'    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= load_json(lowerCAmelCase__    )
										# print(metrics)
										self.assertGreaterEqual(metrics['bleu']       ,		2_5    )





