

import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
					import torch
					from torch import nn

					from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
					from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor





class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				Tuple							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Optional[int]=1_3							,   _UpperCamelCase :				str=3_2							,   _UpperCamelCase :				List[Any]=2							,   _UpperCamelCase :				Dict=3							,   _UpperCamelCase :				str=1_6							,   _UpperCamelCase :				Union[str, Any]=[1, 2, 1]							,   _UpperCamelCase :				int=[2, 2, 4]							,   _UpperCamelCase :				List[str]=2							,   _UpperCamelCase :				Optional[int]=2.0							,   _UpperCamelCase :				str=True							,   _UpperCamelCase :				str=0.0							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				List[str]=0.1							,   _UpperCamelCase :				Optional[Any]="gelu"							,   _UpperCamelCase :				List[Any]=False							,   _UpperCamelCase :				Tuple=True							,   _UpperCamelCase :				List[str]=0.02							,   _UpperCamelCase :				List[Any]=1e-5							,   _UpperCamelCase :				List[Any]=True							,   _UpperCamelCase :				List[str]=None							,   _UpperCamelCase :				Optional[Any]=True							,   _UpperCamelCase :				Dict=1_0							,   _UpperCamelCase :				Tuple=8							,   )      ->List[str]:
									snake_case_   =	parent
									snake_case_   =	batch_size
									snake_case_   =	image_size
									snake_case_   =	patch_size
									snake_case_   =	num_channels
									snake_case_   =	embed_dim
									snake_case_   =	depths
									snake_case_   =	num_heads
									snake_case_   =	window_size
									snake_case_   =	mlp_ratio
									snake_case_   =	qkv_bias
									snake_case_   =	hidden_dropout_prob
									snake_case_   =	attention_probs_dropout_prob
									snake_case_   =	drop_path_rate
									snake_case_   =	hidden_act
									snake_case_   =	use_absolute_embeddings
									snake_case_   =	patch_norm
									snake_case_   =	layer_norm_eps
									snake_case_   =	initializer_range
									snake_case_   =	is_training
									snake_case_   =	scope
									snake_case_   =	use_labels
									snake_case_   =	type_sequence_label_size
									snake_case_   =	encoder_stride







			def 				snake_case__(     self :				List[Any]							)      ->Optional[int]:
									snake_case_   =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

									snake_case_   =	None
									if self.use_labels:
															snake_case_   =	ids_tensor([self.batch_size]							,   self.type_sequence_label_size							)

									snake_case_   =	self.get_config()

									return config, pixel_values, labels







			def 				snake_case__(     self :				Dict							)      ->str:
									return SwinvaConfig(
									    image_size=self.image_size							,   patch_size=self.patch_size							,   num_channels=self.num_channels							,   embed_dim=self.embed_dim							,   depths=self.depths							,   num_heads=self.num_heads							,   window_size=self.window_size							,   mlp_ratio=self.mlp_ratio							,   qkv_bias=self.qkv_bias							,   hidden_dropout_prob=self.hidden_dropout_prob							,   attention_probs_dropout_prob=self.attention_probs_dropout_prob							,   drop_path_rate=self.drop_path_rate							,   hidden_act=self.hidden_act							,   use_absolute_embeddings=self.use_absolute_embeddings							,   path_norm=self.patch_norm							,   layer_norm_eps=self.layer_norm_eps							,   initializer_range=self.initializer_range							,   encoder_stride=self.encoder_stride							,   )







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				int							)      ->str:
									snake_case_   =	SwinvaModel(config=lowerCamelCase_							)
									model.to(lowerCamelCase_							)
									model.eval()
									snake_case_   =	model(lowerCamelCase_							)

									snake_case_   =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
									snake_case_   =	int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

									self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, expected_seq_len, expected_dim)							)







			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				int							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				int							)      ->Tuple:
									snake_case_   =	SwinvaForMaskedImageModeling(config=lowerCamelCase_							)
									model.to(lowerCamelCase_							)
									model.eval()
									snake_case_   =	model(lowerCamelCase_							)
									self.parent.assertEqual(
									    result.logits.shape							,   (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

									# test greyscale images
									snake_case_   =	1
									snake_case_   =	SwinvaForMaskedImageModeling(lowerCamelCase_							)
									model.to(lowerCamelCase_							)
									model.eval()

									snake_case_   =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
									snake_case_   =	model(lowerCamelCase_							)
									self.parent.assertEqual(result.logits.shape							,   (self.batch_size, 1, self.image_size, self.image_size)							)







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Optional[Any]							)      ->int:
									snake_case_   =	self.type_sequence_label_size
									snake_case_   =	SwinvaForImageClassification(lowerCamelCase_							)
									model.to(lowerCamelCase_							)
									model.eval()
									snake_case_   =	model(lowerCamelCase_							,   labels=lowerCamelCase_							)
									self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.type_sequence_label_size)							)







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






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







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				(
			    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
			)
			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				(
			    {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
			    if is_torch_available()
			    else {}
			)

			SCREAMING_SNAKE_CASE     :   int									=				False
			SCREAMING_SNAKE_CASE     :   List[str]									=				False
			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				False
			SCREAMING_SNAKE_CASE     :   Tuple									=				False







			def 				snake_case__(     self :				Dict							)      ->List[str]:
									snake_case_   =	SwinvaModelTester(self							)
									snake_case_   =	ConfigTester(self							,   config_class=lowerCamelCase_							,   embed_dim=3_7							)







			def 				snake_case__(     self :				str							)      ->Dict:
									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[int]							)      ->List[Any]:
									snake_case_   =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase_							)







			@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''							)
			def 				snake_case__(     self :				List[str]							)      ->Optional[Any]:
									pass







			@unittest.skip(reason='''Swinv2 does not use inputs_embeds'''							)
			def 				snake_case__(     self :				Any							)      ->Union[str, Any]:
									pass







			def 				snake_case__(     self :				Union[str, Any]							)      ->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_							)
															self.assertIsInstance(model.get_input_embeddings()							,   (nn.Module)							)
															snake_case_   =	model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(lowerCamelCase_							,   nn.Linear							)							)







			def 				snake_case__(     self :				Any							)      ->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.forward							)
															# 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 :				Dict							)      ->Dict:
									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()
									snake_case_   =	True

									for model_class in self.all_model_classes:
															snake_case_   =	True
															snake_case_   =	False
															snake_case_   =	True
															snake_case_   =	model_class(lowerCamelCase_							)
															model.to(lowerCamelCase_							)
															model.eval()
															with torch.no_grad():
																					snake_case_   =	model(**self._prepare_for_class(lowerCamelCase_							,   lowerCamelCase_							)							)
															snake_case_   =	outputs.attentions
															snake_case_   =	len(self.model_tester.depths							)
															self.assertEqual(len(lowerCamelCase_							)							,   lowerCamelCase_							)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															snake_case_   =	True
															snake_case_   =	config.window_size**2
															snake_case_   =	model_class(lowerCamelCase_							)
															model.to(lowerCamelCase_							)
															model.eval()
															with torch.no_grad():
																					snake_case_   =	model(**self._prepare_for_class(lowerCamelCase_							,   lowerCamelCase_							)							)
															snake_case_   =	outputs.attentions
															self.assertEqual(len(lowerCamelCase_							)							,   lowerCamelCase_							)

															self.assertListEqual(
															    list(attentions[0].shape[-3:]							)							,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,   )
															snake_case_   =	len(lowerCamelCase_							)

															# Check attention is always last and order is fine
															snake_case_   =	True
															snake_case_   =	True
															snake_case_   =	model_class(lowerCamelCase_							)
															model.to(lowerCamelCase_							)
															model.eval()
															with torch.no_grad():
																					snake_case_   =	model(**self._prepare_for_class(lowerCamelCase_							,   lowerCamelCase_							)							)

															if hasattr(self.model_tester							,   '''num_hidden_states_types'''							):
																					snake_case_   =	self.model_tester.num_hidden_states_types
															else:
																					# also another +1 for reshaped_hidden_states
																					snake_case_   =	2
															self.assertEqual(out_len + added_hidden_states							,   len(lowerCamelCase_							)							)

															snake_case_   =	outputs.attentions

															self.assertEqual(len(lowerCamelCase_							)							,   lowerCamelCase_							)

															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]							)							,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,   )







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				Dict							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Dict							,   _UpperCamelCase :				List[str]							)      ->Optional[Any]:
									snake_case_   =	model_class(lowerCamelCase_							)
									model.to(lowerCamelCase_							)
									model.eval()

									with torch.no_grad():
															snake_case_   =	model(**self._prepare_for_class(lowerCamelCase_							,   lowerCamelCase_							)							)

									snake_case_   =	outputs.hidden_states

									snake_case_   =	getattr(
									    self.model_tester							,   '''expected_num_hidden_layers'''							,   len(self.model_tester.depths							) + 1							)
									self.assertEqual(len(lowerCamelCase_							)							,   lowerCamelCase_							)

									# Swinv2 has a different seq_length
									snake_case_   =	(
									    config.patch_size
									    if isinstance(config.patch_size							,   collections.abc.Iterable							)
									    else (config.patch_size, config.patch_size)
									)

									snake_case_   =	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]							)							,   [num_patches, self.model_tester.embed_dim]							,   )

									snake_case_   =	outputs.reshaped_hidden_states
									self.assertEqual(len(lowerCamelCase_							)							,   lowerCamelCase_							)

									snake_case_,    snake_case_,    snake_case_,    snake_case_   =	reshaped_hidden_states[0].shape
									snake_case_   =	(
									    reshaped_hidden_states[0].view(lowerCamelCase_							,   lowerCamelCase_							,   height * width							).permute(0							,   2							,   1							)
									)
									self.assertListEqual(
									    list(reshaped_hidden_states.shape[-2:]							)							,   [num_patches, self.model_tester.embed_dim]							,   )







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

									snake_case_   =	(
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size							,   collections.abc.Iterable							)
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)

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

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

															self.check_hidden_states_output(lowerCamelCase_							,   lowerCamelCase_							,   lowerCamelCase_							,   lowerCamelCase_							)







			def 				snake_case__(     self :				str							)      ->str:
									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()
									snake_case_   =	3

									snake_case_   =	(
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size							,   collections.abc.Iterable							)
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)
									snake_case_   =	(
									    config.patch_size
									    if isinstance(config.patch_size							,   collections.abc.Iterable							)
									    else (config.patch_size, config.patch_size)
									)

									snake_case_   =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
									snake_case_   =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															snake_case_   =	True
															self.check_hidden_states_output(lowerCamelCase_							,   lowerCamelCase_							,   lowerCamelCase_							,   (padded_height, padded_width)							)







			def 				snake_case__(     self :				List[str]							)      ->Tuple:
									snake_case_   =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase_							)







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







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

									for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_   =	SwinvaModel.from_pretrained(lowerCamelCase_							)
															self.assertIsNotNone(lowerCamelCase_							)







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

									snake_case_   =	_config_zero_init(lowerCamelCase_							)
									for model_class in self.all_model_classes:
															snake_case_   =	model_class(config=lowerCamelCase_							)
															for name, param in model.named_parameters():
																					if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																											self.assertIn(
																											    ((param.data.mean() * 1e9).round() / 1e9).item()							,   [0.0, 1.0]							,   msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''							,   )






@require_vision
@require_torch
class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			@cached_property
			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									return (
									    AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''							)
									    if is_vision_available()
									    else None
									)







			@slow
			def 				snake_case__(     self :				Optional[Any]							)      ->Tuple:
									snake_case_   =	SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''							).to(
									    lowerCamelCase_							)
									snake_case_   =	self.default_image_processor

									snake_case_   =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
									snake_case_   =	image_processor(images=lowerCamelCase_							,   return_tensors='''pt'''							).to(lowerCamelCase_							)

									# forward pass
									with torch.no_grad():
															snake_case_   =	model(**lowerCamelCase_							)

									# verify the logits
									snake_case_   =	torch.Size((1, 1_0_0_0)							)
									self.assertEqual(outputs.logits.shape							,   lowerCamelCase_							)
									snake_case_   =	torch.tensor([-0.3947, -0.4306, 0.0026]							).to(lowerCamelCase_							)
									self.assertTrue(torch.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_		)
