



'''simple docstring'''

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
							import jax

							from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class       __magic_name__		(       unittest.TestCase  ):







							def __init__(				self   :			List[str]      ,_UpperCAmelCase   :			Optional[int]      ,_UpperCAmelCase   :			List[str]=13      ,_UpperCAmelCase   :			Any=30      ,_UpperCAmelCase   :			Optional[int]=2      ,_UpperCAmelCase   :			int=3      ,_UpperCAmelCase   :			List[Any]=True      ,_UpperCAmelCase   :			List[Any]=True      ,_UpperCAmelCase   :			Optional[int]=32      ,_UpperCAmelCase   :			Any=5      ,_UpperCAmelCase   :			Dict=4      ,_UpperCAmelCase   :			List[str]=37      ,_UpperCAmelCase   :			Optional[int]="gelu"      ,_UpperCAmelCase   :			List[str]=0.1      ,_UpperCAmelCase   :			List[Any]=0.1      ,_UpperCAmelCase   :			List[Any]=10      ,_UpperCAmelCase   :			List[str]=0.02      ,):
									_a	: Tuple								=				parent
									_a	: Optional[Any]								=				batch_size
									_a	: Tuple								=				image_size
									_a	: Union[str, Any]								=				patch_size
									_a	: Any								=				num_channels
									_a	: List[Any]								=				is_training
									_a	: Optional[Any]								=				use_labels
									_a	: Dict								=				hidden_size
									_a	: Optional[Any]								=				num_hidden_layers
									_a	: Any								=				num_attention_heads
									_a	: int								=				intermediate_size
									_a	: List[str]								=				hidden_act
									_a	: Union[str, Any]								=				hidden_dropout_prob
									_a	: List[Any]								=				attention_probs_dropout_prob
									_a	: Any								=				type_sequence_label_size
									_a	: Optional[int]								=				initializer_range

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







							def 				__lowercase     (				self   :			Any						):
									_a	: int								=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

									_a	: Union[str, Any]								=				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=_UpperCAmelCase      ,initializer_range=self.initializer_range      ,)

									return config, pixel_values







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			str      ,_UpperCAmelCase   :			Any						):
									_a	: int								=				FlaxViTModel(config=_UpperCAmelCase						)
									_a	: str								=				model(_UpperCAmelCase						)
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									_a	: Any								=				(self.image_size, self.image_size)
									_a	: Dict								=				(self.patch_size, self.patch_size)
									_a	: Any								=				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, num_patches + 1, self.hidden_size)						)







							def 				__lowercase     (				self   :			int      ,_UpperCAmelCase   :			Optional[Any]      ,_UpperCAmelCase   :			int						):
									_a	: Optional[int]								=				self.type_sequence_label_size
									_a	: int								=				FlaxViTForImageClassification(config=_UpperCAmelCase						)
									_a	: Tuple								=				model(_UpperCAmelCase						)
									self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)						)

									# test greyscale images
									_a	: Dict								=				1
									_a	: List[str]								=				FlaxViTForImageClassification(_UpperCAmelCase						)

									_a	: List[Any]								=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
									_a	: Any								=				model(_UpperCAmelCase						)







							def 				__lowercase     (				self   :			List[str]						):
									_a	: int								=				self.prepare_config_and_inputs()
									(
									    (
									    _a
									)			,   (
									    _a
									)			,   
									)	: List[str]								=				config_and_inputs
									_a	: str								=				{'pixel_values': pixel_values}
									return config, inputs_dict

@require_flax
class       __magic_name__		(       _UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Any  							=   (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: List[str]								=				FlaxViTModelTester(self						)
									_a	: Optional[Any]								=				ConfigTester(self      ,config_class=_UpperCAmelCase      ,has_text_modality=_UpperCAmelCase      ,hidden_size=37						)







							def 				__lowercase     (				self   :			int						):
									self.config_tester.run_common_tests()







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[Any]								=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase						)







							def 				__lowercase     (				self   :			str						):
									_a	: Dict								=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)







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

									for model_class in self.all_model_classes:
											_a	: int								=				model_class(_UpperCAmelCase						)
											_a	: List[str]								=				inspect.signature(model.__call__						)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a	: Tuple								=				[*signature.parameters.keys()]

											_a	: Tuple								=				['pixel_values']
											self.assertListEqual(arg_names[:1]      ,_UpperCAmelCase						)







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

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

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

													with self.subTest('JIT Enabled'						):
															_a	: List[Any]								=				model_jitted(**_UpperCAmelCase						).to_tuple()

													with self.subTest('JIT Disabled'						):
															with jax.disable_jit():
																	_a	: Union[str, Any]								=				model_jitted(**_UpperCAmelCase						).to_tuple()

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







							@slow
							def 				__lowercase     (				self   :			List[str]						):
									for model_class_name in self.all_model_classes:
											_a	: Tuple								=				model_class_name.from_pretrained('google/vit-base-patch16-224'						)
											_a	: Optional[int]								=				model(np.ones((1, 3, 224, 224)						)						)
											self.assertIsNotNone(_UpperCAmelCase						)









'''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       __magic_name__		:
							lowerCAmelCase			:       Optional[Any]  							=   MBartConfig
							lowerCAmelCase			:       str  							=   {}
							lowerCAmelCase			:       Dict  							=   'gelu'







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







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: str								=				ids_tensor([self.batch_size, self.seq_length - 1]      ,self.vocab_size						)
									_a	: List[str]								=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)      ,1						)
									_a	: str								=				tf.concat([input_ids, eos_tensor]      ,axis=1						)

									_a	: Dict								=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size						)

									_a	: List[str]								=				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	: Optional[int]								=				prepare_mbart_inputs_dict(_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)
									return config, inputs_dict







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			Optional[int]						):
									_a	: str								=				TFMBartModel(config=_UpperCAmelCase						).get_decoder()
									_a	: List[Any]								=				inputs_dict['input_ids']

									_a	: Optional[Any]								=				input_ids[:1, :]
									_a	: Tuple								=				inputs_dict['attention_mask'][:1, :]
									_a	: List[Any]								=				inputs_dict['head_mask']
									_a	: str								=				1

									# first forward pass
									_a	: Union[str, Any]								=				model(_UpperCAmelCase      ,attention_mask=_UpperCAmelCase      ,head_mask=_UpperCAmelCase      ,use_cache=_UpperCAmelCase						)

									_a			,   _a	: Optional[int]								=				outputs.to_tuple()
									_a	: Union[str, Any]								=				past_key_values[1]



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     )     -> Union[str, Any]:
		if attention_mask is None:
				_a	: List[Any]								=				tf.cast(tf.math.not_equal(lowerCAmelCase_		,     config.pad_token_id    )		,     tf.inta    )
		if decoder_attention_mask is None:
				_a	: 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	: int								=				tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
		if decoder_head_mask is None:
				_a	: Any								=				tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
		if cross_attn_head_mask is None:
				_a	: int								=				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       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       List[str]  							=   (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							lowerCAmelCase			:       Optional[Any]  							=   (TFMBartForConditionalGeneration,) if is_tf_available() else ()
							lowerCAmelCase			:       Dict  							=   (
							    {
							        'conversational': TFMBartForConditionalGeneration,
							        'feature-extraction': TFMBartModel,
							        'summarization': TFMBartForConditionalGeneration,
							        'text2text-generation': TFMBartForConditionalGeneration,
							        'translation': TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCAmelCase			:       Dict  							=   True
							lowerCAmelCase			:       List[str]  							=   False
							lowerCAmelCase			:       str  							=   False







							def 				__lowercase     (				self   :			int      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			str      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			int						):
									if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
											# Exception encountered when calling layer '...'
											return True

									return False







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[int]								=				TFMBartModelTester(self						)
									_a	: Dict								=				ConfigTester(self      ,config_class=_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[int]						):
									self.config_tester.run_common_tests()







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: List[Any]								=				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       __magic_name__		(       unittest.TestCase  ):
							lowerCAmelCase			:       List[Any]  							=   [
							    ' UN Chief Says There Is No Military Solution in Syria',
							]
							lowerCAmelCase			:       Dict  							=   [
							    'Şeful ONU declară că nu există o soluţie militară în Siria',
							]
							lowerCAmelCase			:       Tuple  							=   'facebook/mbart-large-en-ro'







							@cached_property
							def 				__lowercase     (				self   :			Optional[int]						):
									return AutoTokenizer.from_pretrained(self.model_name						)







							@cached_property
							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Any								=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
									return model







							def 				__lowercase     (				self   :			Optional[int]      ,**_UpperCAmelCase   :			Dict						):
									_a	: List[Any]								=				self.translate_src_text(**_UpperCAmelCase						)
									self.assertListEqual(self.expected_text      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Tuple      ,**_UpperCAmelCase   :			str						):
									_a	: List[Any]								=				self.tokenizer(self.src_text      ,**_UpperCAmelCase      ,return_tensors='tf'						)
									_a	: Any								=				self.model.generate(
									    model_inputs.input_ids      ,attention_mask=model_inputs.attention_mask      ,num_beams=2						)
									_a	: Tuple								=				self.tokenizer.batch_decode(_UpperCAmelCase      ,skip_special_tokens=_UpperCAmelCase						)
									return generated_words







							@slow
							def 				__lowercase     (				self   :			Tuple						):
									self._assert_generated_batch_equal_expected()




