

import inspect
import unittest

from transformers import RegNetConfig, 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.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor





class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			def __init__(     self :				List[Any]							,   _UpperCamelCase :				int							,   _UpperCamelCase :				Union[str, Any]=3							,   _UpperCamelCase :				List[Any]=3_2							,   _UpperCamelCase :				List[Any]=3							,   _UpperCamelCase :				List[str]=1_0							,   _UpperCamelCase :				List[str]=[1_0, 2_0, 3_0, 4_0]							,   _UpperCamelCase :				Union[str, Any]=[1, 1, 2, 1]							,   _UpperCamelCase :				int=True							,   _UpperCamelCase :				Union[str, Any]=True							,   _UpperCamelCase :				Union[str, Any]="relu"							,   _UpperCamelCase :				List[str]=3							,   _UpperCamelCase :				Optional[Any]=None							,   )      ->List[Any]:
									snake_case_   =	parent
									snake_case_   =	batch_size
									snake_case_   =	image_size
									snake_case_   =	num_channels
									snake_case_   =	embeddings_size
									snake_case_   =	hidden_sizes
									snake_case_   =	depths
									snake_case_   =	is_training
									snake_case_   =	use_labels
									snake_case_   =	hidden_act
									snake_case_   =	num_labels
									snake_case_   =	scope
									snake_case_   =	len(lowerCamelCase_							)







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

									snake_case_   =	self.get_config()

									return config, pixel_values







			def 				snake_case__(     self :				List[str]							)      ->int:
									return RegNetConfig(
									    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 				snake_case__(     self :				Dict							,   _UpperCamelCase :				str							,   _UpperCamelCase :				List[str]							)      ->List[str]:
									snake_case_   =	FlaxRegNetModel(config=lowerCamelCase_							)
									snake_case_   =	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 				snake_case__(     self :				int							,   _UpperCamelCase :				List[Any]							,   _UpperCamelCase :				Optional[Any]							)      ->Tuple:
									snake_case_   =	self.num_labels
									snake_case_   =	FlaxRegNetForImageClassification(config=lowerCamelCase_							)
									snake_case_   =	model(lowerCamelCase_							)
									self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_labels)							)







			def 				snake_case__(     self :				List[Any]							)      ->int:
									snake_case_   =	self.prepare_config_and_inputs()
									snake_case_,    snake_case_   =	config_and_inputs
									snake_case_   =	{'''pixel_values''': pixel_values}
									return config, inputs_dict






@require_flax
class 	snake_case_   (		__A	,       unittest.TestCase					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Tuple									=				(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

			SCREAMING_SNAKE_CASE     :   Tuple									=				False
			SCREAMING_SNAKE_CASE     :   Tuple									=				False
			SCREAMING_SNAKE_CASE     :   Dict									=				False







			def 				snake_case__(     self :				Optional[Any]							)      ->Tuple:
									snake_case_   =	FlaxRegNetModelTester(self							)
									snake_case_   =	ConfigTester(self							,   config_class=lowerCamelCase_							,   has_text_modality=lowerCamelCase_							)







			def 				snake_case__(     self :				Union[str, Any]							)      ->Optional[Any]:
									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 				snake_case__(     self :				Optional[Any]							)      ->List[str]:
									return







			def 				snake_case__(     self :				Optional[int]							)      ->int:
									snake_case_   =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase_							)







			def 				snake_case__(     self :				Dict							)      ->Optional[int]:
									snake_case_   =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_							)







			@unittest.skip(reason='''RegNet does not use inputs_embeds'''							)
			def 				snake_case__(     self :				int							)      ->List[str]:
									pass







			@unittest.skip(reason='''RegNet does not support input and output embeddings'''							)
			def 				snake_case__(     self :				Tuple							)      ->Tuple:
									pass







			def 				snake_case__(     self :				int							)      ->Optional[int]:
									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case_   =	['''pixel_values''']
															self.assertListEqual(arg_names[:1]							,   lowerCamelCase_							)







			def 				snake_case__(     self :				List[str]							)      ->Dict:

									def check_hidden_states_output(_UpperCamelCase :				int							,   _UpperCamelCase :				Dict							,   _UpperCamelCase :				int							):
															snake_case_   =	model_class(lowerCamelCase_							)

															snake_case_   =	model(**self._prepare_for_class(lowerCamelCase_							,   lowerCamelCase_							)							)

															snake_case_   =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															snake_case_   =	self.model_tester.num_stages
															self.assertEqual(len(lowerCamelCase_							)							,   expected_num_stages + 1							)

									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															snake_case_   =	True
															check_hidden_states_output(lowerCamelCase_							,   lowerCamelCase_							,   lowerCamelCase_							)

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

															check_hidden_states_output(lowerCamelCase_							,   lowerCamelCase_							,   lowerCamelCase_							)







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

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__							):
																					snake_case_   =	self._prepare_for_class(lowerCamelCase_							,   lowerCamelCase_							)
																					snake_case_   =	model_class(lowerCamelCase_							)

																					@jax.jit
																					def model_jitted(_UpperCamelCase :				List[Any]							,   **_UpperCamelCase :				Tuple							):
																											return model(pixel_values=lowerCamelCase_							,   **lowerCamelCase_							)

																					with self.subTest('''JIT Enabled'''							):
																											snake_case_   =	model_jitted(**lowerCamelCase_							).to_tuple()

																					with self.subTest('''JIT Disabled'''							):
																											with jax.disable_jit():
																																	snake_case_   =	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   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
						return image





@require_flax
class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			@cached_property
			def 				snake_case__(     self :				Union[str, Any]							)      ->Tuple:
									return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''							) if is_vision_available() else None







			@slow
			def 				snake_case__(     self :				Dict							)      ->Tuple:
									snake_case_   =	FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''							)

									snake_case_   =	self.default_image_processor
									snake_case_   =	prepare_img()
									snake_case_   =	image_processor(images=lowerCamelCase_							,   return_tensors='''np'''							)

									snake_case_   =	model(**lowerCamelCase_							)

									# verify the logits
									snake_case_   =	(1, 1_0_0_0)
									self.assertEqual(outputs.logits.shape							,   lowerCamelCase_							)

									snake_case_   =	jnp.array([-0.4180, -1.5051, -3.4836]							)

									self.assertTrue(jnp.allclose(outputs.logits[0, :3]							,   lowerCamelCase_							,   atol=1e-4							)							)




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowercase: Union[str, Any]      			=		get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
				_lowercase: List[str]      			=		json.load(f)



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




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

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




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

						a					     =   FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_		).to(lowerCamelCase_		)
						if torch_device == "cuda":
											model.half()
						return model




	@parameterized.expand(
	    [
	        ["en-ru", 26.0],
	        ["ru-en", 22.0],
	        ["en-de", 22.0],
	        ["de-en", 29.0],
	    ]		)
	@slow
	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   F'''facebook/wmt19-{pair}'''
						a					     =   self.get_tokenizer(lowerCamelCase_		)
						a					     =   self.get_model(lowerCamelCase_		)

						a					     =   bleu_data[pair]["src"]
						a					     =   bleu_data[pair]["tgt"]

						a					     =   tokenizer(lowerCamelCase_		,       return_tensors="pt"		,       truncation=lowerCamelCase_		,       padding="longest"		).to(lowerCamelCase_		)
						a					     =   model.generate(
						    input_ids=batch.input_ids		,       num_beams=8		,       )
						a					     =   tokenizer.batch_decode(
						    lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       clean_up_tokenization_spaces=lowerCamelCase_		)
						a					     =   calculate_bleu(lowerCamelCase_		,       lowerCamelCase_		)
						print(lowerCamelCase_		)
						self.assertGreaterEqual(scores["bleu"]		,       lowerCamelCase_		)
