



'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel







@require_tf
class      _lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE       :		Any		      = MBartConfig
	__SCREAMING_SNAKE_CASE       :		str		      = {}
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = "gelu"


	def __init__(self	,				lowercase	,				lowercase=13	,				lowercase=7	,				lowercase=True	,				lowercase=False	,				lowercase=99	,				lowercase=32	,				lowercase=2	,				lowercase=4	,				lowercase=37	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=20	,				lowercase=2	,				lowercase=1	,				lowercase=0	,				):
				A_     :						Any     =					parent
				A_     :						Union[str, Any]     =					batch_size
				A_     :						Dict     =					seq_length
				A_     :						List[Any]     =					is_training
				A_     :						Optional[Any]     =					use_labels
				A_     :						List[str]     =					vocab_size
				A_     :						int     =					hidden_size
				A_     :						Optional[int]     =					num_hidden_layers
				A_     :						Any     =					num_attention_heads
				A_     :						List[Any]     =					intermediate_size
				A_     :						Tuple     =					hidden_dropout_prob
				A_     :						str     =					attention_probs_dropout_prob
				A_     :						Optional[Any]     =					max_position_embeddings
				A_     :						Union[str, Any]     =					eos_token_id
				A_     :						Optional[Any]     =					pad_token_id
				A_     :						Union[str, Any]     =					bos_token_id


	def    _a     (self							):
				A_     :						str     =					ids_tensor([self.batch_size, self.seq_length - 1]	,				self.vocab_size							)
				A_     :						Tuple     =					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)	,				1							)
				A_     :						int     =					tf.concat([input_ids, eos_tensor]	,				axis=1							)

				A_     :						Any     =					ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size							)

				A_     :						Optional[Any]     =					self.config_cls(
				    vocab_size=self.vocab_size	,				d_model=self.hidden_size	,				encoder_layers=self.num_hidden_layers	,				decoder_layers=self.num_hidden_layers	,				encoder_attention_heads=self.num_attention_heads	,				decoder_attention_heads=self.num_attention_heads	,				encoder_ffn_dim=self.intermediate_size	,				decoder_ffn_dim=self.intermediate_size	,				dropout=self.hidden_dropout_prob	,				attention_dropout=self.attention_probs_dropout_prob	,				max_position_embeddings=self.max_position_embeddings	,				eos_token_ids=[2]	,				bos_token_id=self.bos_token_id	,				pad_token_id=self.pad_token_id	,				decoder_start_token_id=self.pad_token_id	,				**self.config_updates	,				)
				A_     :						Dict     =					prepare_mbart_inputs_dict(_UpperCAmelCase	,				_UpperCAmelCase	,				_UpperCAmelCase							)
				return config, inputs_dict


	def    _a     (self	,				lowercase	,				lowercase							):
				A_     :						Dict     =					TFMBartModel(config=_UpperCAmelCase							).get_decoder()
				A_     :						List[Any]     =					inputs_dict['''input_ids''']

				A_     :						Dict     =					input_ids[:1, :]
				A_     :						Optional[Any]     =					inputs_dict['''attention_mask'''][:1, :]
				A_     :						Dict     =					inputs_dict['''head_mask''']
				A_     :						Tuple     =					1

				# first forward pass
				A_     :						Optional[int]     =					model(_UpperCAmelCase	,				attention_mask=_UpperCAmelCase	,				head_mask=_UpperCAmelCase	,				use_cache=_UpperCAmelCase							)

				A_     :						Dict     =					outputs.to_tuple()
				A_     :						Any     =					past_key_values[1]






def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				lowerCamelCase__=None						,				):
			'''simple docstring'''





			if attention_mask is None:
						A_     :						List[str]     =					tf.cast(tf.math.not_equal(__UpperCAmelCase						,				config.pad_token_id   )						,				tf.inta   )
			if decoder_attention_mask is None:
						A_     :						Union[str, Any]     =					tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape						,				dtype=tf.inta   ),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,				config.pad_token_id   )						,				tf.inta   ),
						    ]						,				axis=-1						,				)
			if head_mask is None:
						A_     :						Optional[int]     =					tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						A_     :						int     =					tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						A_     :						List[str]     =					tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}







@require_tf
class      _lowerCAmelCase							(      _UpperCAmelCase				,      _UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		List[str]		      = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
	__SCREAMING_SNAKE_CASE       :		str		      = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
	__SCREAMING_SNAKE_CASE       :		Any		      = (
	    {
	        "conversational": TFMBartForConditionalGeneration,
	        "feature-extraction": TFMBartModel,
	        "summarization": TFMBartForConditionalGeneration,
	        "text2text-generation": TFMBartForConditionalGeneration,
	        "translation": TFMBartForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = True
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = False
	__SCREAMING_SNAKE_CASE       :		str		      = False


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase							):
				if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
							# Exception encountered when calling layer '...'
							return True

				return False


	def    _a     (self							):
				A_     :						Dict     =					TFMBartModelTester(self							)
				A_     :						List[Any]     =					ConfigTester(self	,				config_class=_UpperCAmelCase							)


	def    _a     (self							):
				self.config_tester.run_common_tests()


	def    _a     (self							):
				A_     :						Tuple     =					self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase							)







@require_sentencepiece
@require_tokenizers
@require_tf
class      _lowerCAmelCase							(      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Any		      = [
	    " UN Chief Says There Is No Military Solution in Syria",
	]
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = [
	    "Şeful ONU declară că nu există o soluţie militară în Siria",
	]
	__SCREAMING_SNAKE_CASE       :		List[str]		      = "facebook/mbart-large-en-ro"


	@cached_property
	def    _a     (self							):
				return AutoTokenizer.from_pretrained(self.model_name							)


	@cached_property
	def    _a     (self							):
				A_     :						int     =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
				return model


	def    _a     (self	,				**lowercase							):
				A_     :						List[Any]     =					self.translate_src_text(**_UpperCAmelCase							)
				self.assertListEqual(self.expected_text	,				_UpperCAmelCase							)


	def    _a     (self	,				**lowercase							):
				A_     :						Union[str, Any]     =					self.tokenizer(self.src_text	,				**_UpperCAmelCase	,				return_tensors="""tf"""							)
				A_     :						Dict     =					self.model.generate(
				    model_inputs.input_ids	,				attention_mask=model_inputs.attention_mask	,				num_beams=2							)
				A_     :						List[str]     =					self.tokenizer.batch_decode(_UpperCAmelCase	,				skip_special_tokens=_UpperCAmelCase							)
				return generated_words


	@slow
	def    _a     (self							):
				self._assert_generated_batch_equal_expected()




'''simple docstring'''




import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
						from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor







class      _lowerCAmelCase							:


	def __init__(self	,				lowercase	,				lowercase=13	,				lowercase=30	,				lowercase=2	,				lowercase=3	,				lowercase=True	,				lowercase=True	,				lowercase=32	,				lowercase=5	,				lowercase=4	,				lowercase=37	,				lowercase="gelu"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=10	,				lowercase=0.02	,				lowercase=None	,				lowercase=2	,				):
				A_     :						List[str]     =					parent
				A_     :						str     =					batch_size
				A_     :						Optional[Any]     =					image_size
				A_     :						List[str]     =					patch_size
				A_     :						List[str]     =					num_channels
				A_     :						List[str]     =					is_training
				A_     :						str     =					use_labels
				A_     :						List[str]     =					hidden_size
				A_     :						List[Any]     =					num_hidden_layers
				A_     :						Any     =					num_attention_heads
				A_     :						Any     =					intermediate_size
				A_     :						Optional[Any]     =					hidden_act
				A_     :						Optional[int]     =					hidden_dropout_prob
				A_     :						str     =					attention_probs_dropout_prob
				A_     :						Optional[int]     =					type_sequence_label_size
				A_     :						Any     =					initializer_range
				A_     :						int     =					scope
				A_     :						str     =					encoder_stride

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


	def    _a     (self							):
				A_     :						str     =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

				A_     :						Optional[Any]     =					None
				if self.use_labels:
							A_     :						str     =					ids_tensor([self.batch_size]	,				self.type_sequence_label_size							)

				A_     :						Union[str, Any]     =					self.get_config()

				return config, pixel_values, labels


	def    _a     (self							):
				return ViTConfig(
				    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	,				is_decoder=lowercase	,				initializer_range=self.initializer_range	,				encoder_stride=self.encoder_stride	,				)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						List[str]     =					ViTModel(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_     :						List[str]     =					model(lowercase							)
				self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						List[str]     =					ViTForMaskedImageModeling(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_     :						Tuple     =					model(lowercase							)
				self.parent.assertEqual(
				    result.reconstruction.shape	,				(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

				# test greyscale images
				A_     :						Union[str, Any]     =					1
				A_     :						Any     =					ViTForMaskedImageModeling(lowercase							)
				model.to(lowercase							)
				model.eval()

				A_     :						str     =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				A_     :						Optional[int]     =					model(lowercase							)
				self.parent.assertEqual(result.reconstruction.shape	,				(self.batch_size, 1, self.image_size, self.image_size)							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						Dict     =					self.type_sequence_label_size
				A_     :						str     =					ViTForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_     :						List[str]     =					model(lowercase	,				labels=lowercase							)
				self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.type_sequence_label_size)							)

				# test greyscale images
				A_     :						Any     =					1
				A_     :						str     =					ViTForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()

				A_     :						Optional[int]     =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				A_     :						Union[str, Any]     =					model(lowercase							)
				self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.type_sequence_label_size)							)


	def    _a     (self							):
				A_     :						str     =					self.prepare_config_and_inputs()
				(
				    (
				    A_
				),   (
				    A_
				),   (
				    A_
				),   
				)     :						Optional[int]     =					config_and_inputs
				A_     :						Tuple     =					{"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_torch
class      _lowerCAmelCase							(      __UpperCAmelCase				,      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Any		      = (
	    (
	        ViTModel,
	        ViTForImageClassification,
	        ViTForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = (
	    {'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
	    if is_torch_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = True

	__SCREAMING_SNAKE_CASE       :		Dict		      = False
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = False
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = False


	def    _a     (self							):
				A_     :						Any     =					ViTModelTester(self							)
				A_     :						str     =					ConfigTester(self	,				config_class=lowercase	,				has_text_modality=lowercase	,				hidden_size=37							)


	def    _a     (self							):
				self.config_tester.run_common_tests()


	@unittest.skip(reason="""ViT does not use inputs_embeds"""							)
	def    _a     (self							):
				pass


	def    _a     (self							):
				A_,   A_     :						Union[str, Any]     =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_     :						Union[str, Any]     =					model_class(lowercase							)
							self.assertIsInstance(model.get_input_embeddings()	,				(nn.Module)							)
							A_     :						Tuple     =					model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowercase	,				nn.Linear							)							)


	def    _a     (self							):
				A_,   A_     :						List[Any]     =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_     :						Optional[Any]     =					model_class(lowercase							)
							A_     :						Any     =					inspect.signature(model.forward							)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							A_     :						List[str]     =					[*signature.parameters.keys()]

							A_     :						Dict     =					["""pixel_values"""]
							self.assertListEqual(arg_names[:1]	,				lowercase							)


	def    _a     (self							):
				A_     :						Tuple     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase							)


	def    _a     (self							):
				A_     :						List[Any]     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*lowercase							)


	def    _a     (self							):
				A_     :						Dict     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase							)


	@slow
	def    _a     (self							):
				for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_     :						Dict     =					ViTModel.from_pretrained(lowercase							)
							self.assertIsNotNone(lowercase							)






def      a      (			):
			'''simple docstring'''





			A_     :						List[Any]     =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
			return image







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


	@cached_property
	def    _a     (self							):
				return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""							) if is_vision_available() else None


	@slow
	def    _a     (self							):
				A_     :						Optional[int]     =					ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""							).to(lowercase							)

				A_     :						List[str]     =					self.default_image_processor
				A_     :						Tuple     =					prepare_img()
				A_     :						int     =					image_processor(images=lowercase	,				return_tensors="""pt"""							).to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_     :						str     =					model(**lowercase							)

				# verify the logits
				A_     :						Dict     =					torch.Size((1, 1000)							)
				self.assertEqual(outputs.logits.shape	,				lowercase							)

				A_     :						str     =					torch.tensor([-0.27_44, 0.82_15, -0.08_36]							).to(lowercase							)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]	,				lowercase	,				atol=1E-4							)							)


	@slow
	def    _a     (self							):
				# ViT models have an `interpolate_pos_encoding` argument in their forward method,
				# allowing to interpolate the pre-trained position embeddings in order to use
				# the model on higher resolutions. The DINO model by Facebook AI leverages this
				# to visualize self-attention on higher resolution images.
				A_     :						Optional[int]     =					ViTModel.from_pretrained("""facebook/dino-vits8"""							).to(lowercase							)

				A_     :						List[Any]     =					ViTImageProcessor.from_pretrained("""facebook/dino-vits8"""	,				size=480							)
				A_     :						Dict     =					prepare_img()
				A_     :						str     =					image_processor(images=lowercase	,				return_tensors="""pt"""							)
				A_     :						int     =					inputs.pixel_values.to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_     :						int     =					model(lowercase	,				interpolate_pos_encoding=lowercase							)

				# verify the logits
				A_     :						int     =					torch.Size((1, 3601, 384)							)
				self.assertEqual(outputs.last_hidden_state.shape	,				lowercase							)

				A_     :						List[Any]     =					torch.tensor(
				    [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]							).to(lowercase							)

				self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,				lowercase	,				atol=1E-4							)							)


	@slow
	@require_accelerate
	@require_torch_gpu
	def    _a     (self							):
				A_     :						List[Any]     =					ViTModel.from_pretrained("""facebook/dino-vits8"""	,				torch_dtype=torch.floataa	,				device_map="""auto"""							)
				A_     :						int     =					self.default_image_processor

				A_     :						Any     =					prepare_img()
				A_     :						List[str]     =					image_processor(images=lowercase	,				return_tensors="""pt"""							)
				A_     :						Any     =					inputs.pixel_values.to(lowercase							)

				# forward pass to make sure inference works in fp16
				with torch.no_grad():
							A_     :						Optional[Any]     =					model(lowercase							)