



import inspect
import unittest

from transformers import ViTMSNConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTMSNForImageClassification, ViTMSNModel
				from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_=13		,       lowerCamelCase_=30		,       lowerCamelCase_=2		,       lowerCamelCase_=3		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=32		,       lowerCamelCase_=5		,       lowerCamelCase_=4		,       lowerCamelCase_=37		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=0.1		,       lowerCamelCase_=0.1		,       lowerCamelCase_=10		,       lowerCamelCase_=0.02		,       lowerCamelCase_=None		,       ):
						"""simple docstring"""

						a					     =   parent
						a					     =   batch_size
						a					     =   image_size
						a					     =   patch_size
						a					     =   num_channels
						a					     =   is_training
						a					     =   use_labels
						a					     =   hidden_size
						a					     =   num_hidden_layers
						a					     =   num_attention_heads
						a					     =   intermediate_size
						a					     =   hidden_act
						a					     =   hidden_dropout_prob
						a					     =   attention_probs_dropout_prob
						a					     =   type_sequence_label_size
						a					     =   initializer_range
						a					     =   scope

						# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						a					     =   (image_size // patch_size) ** 2
						a					     =   num_patches + 1




	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.type_sequence_label_size		)

						a					     =   self.get_config()

						return config, pixel_values, labels




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

						return ViTMSNConfig(
						    image_size=self.image_size		,       patch_size=self.patch_size		,       num_channels=self.num_channels		,       hidden_size=self.hidden_size		,       num_hidden_layers=self.num_hidden_layers		,       num_attention_heads=self.num_attention_heads		,       intermediate_size=self.intermediate_size		,       hidden_act=self.hidden_act		,       hidden_dropout_prob=self.hidden_dropout_prob		,       attention_probs_dropout_prob=self.attention_probs_dropout_prob		,       initializer_range=self.initializer_range		,       )




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

						a					     =   ViTMSNModel(config=lowerCamelCase_		)
						model.to(lowerCamelCase_		)
						model.eval()
						a					     =   model(lowerCamelCase_		)
						self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, self.seq_length, self.hidden_size)		)




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

						a					     =   self.type_sequence_label_size
						a					     =   ViTMSNForImageClassification(lowerCamelCase_		)
						model.to(lowerCamelCase_		)
						model.eval()
						a					     =   model(lowerCamelCase_		,       labels=lowerCamelCase_		)
						print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"		)
						print("Labels: {labels}"		)
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.type_sequence_label_size)		)

						# test greyscale images
						a					     =   1
						a					     =   ViTMSNForImageClassification(lowerCamelCase_		)
						model.to(lowerCamelCase_		)
						model.eval()

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




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

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




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





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

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




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

						a					     =   ViTMSNModelTester(self		)
						a					     =   ConfigTester(self		,       config_class=lowerCamelCase_		,       has_text_modality=lowerCamelCase_		,       hidden_size=37		)




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

						self.config_tester.run_common_tests()




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

						pass




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

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											a					     =   model_class(lowerCamelCase_		)
											self.assertIsInstance(model.get_input_embeddings()		,       (nn.Module)		)
											a					     =   model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowerCamelCase_		,       nn.Linear		)		)




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

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()

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




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

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




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

						a					     =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_		)




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

						for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a					     =   ViTMSNModel.from_pretrained(lowerCamelCase_		)
											self.assertIsNotNone(lowerCamelCase_		)






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





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







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





	"""simple docstring"""




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

						return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"		) if is_vision_available() else None




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

						torch.manual_seed(2		)
						a					     =   ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"		).to(lowerCamelCase_		)

						a					     =   self.default_image_processor
						a					     =   prepare_img()
						a					     =   image_processor(images=lowerCamelCase_		,       return_tensors="pt"		).to(lowerCamelCase_		)

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

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

						a					     =   torch.tensor([-0.0803, -0.4454, -0.2375]		).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_		)
