
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__     :
		a__       :						Optional[int]        =		MBartConfig
		a__       :						Tuple        =		{}
		a__       :						List[Any]        =		"""gelu"""






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






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

						__lowerCamelCase    					=	ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size  )

						__lowerCamelCase    					=	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							,			)
						__lowerCamelCase    					=	prepare_mbart_inputs_dict(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						return config, inputs_dict







		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							Optional[int]:
						__lowerCamelCase    					=	TFMBartModel(config=SCREAMING_SNAKE_CASE__  ).get_decoder()
						__lowerCamelCase    					=	inputs_dict['''input_ids''']

						__lowerCamelCase    					=	input_ids[:1, :]
						__lowerCamelCase    					=	inputs_dict['''attention_mask'''][:1, :]
						__lowerCamelCase    					=	inputs_dict['''head_mask''']
						__lowerCamelCase    					=	1

						# first forward pass
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			head_mask=SCREAMING_SNAKE_CASE__							,			use_cache=SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase					,    __lowerCamelCase    					=	outputs.to_tuple()
						__lowerCamelCase    					=	past_key_values[1]





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






		def  __A       (		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Dict  )     ->							Any:
						if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
										# Exception encountered when calling layer '...'
										return True

						return False






		def  __A       (		self		:							Any  )     ->							Optional[Any]:
						__lowerCamelCase    					=	TFMBartModelTester(self  )
						__lowerCamelCase    					=	ConfigTester(self							,			config_class=SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[Any]  )     ->							int:
						self.config_tester.run_common_tests()







		def  __A       (		self		:							Any  )     ->							Optional[int]:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__  )





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






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






		@cached_property
		def  __A       (		self		:							Optional[Any]  )     ->							Any:
						__lowerCamelCase    					=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
						return model






		def  __A       (		self		:							List[Any]							,			**SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	self.translate_src_text(**SCREAMING_SNAKE_CASE__  )
						self.assertListEqual(self.expected_text							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							List[str]							,			**SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	self.tokenizer(self.src_text							,			**SCREAMING_SNAKE_CASE__							,			return_tensors='''tf'''  )
						__lowerCamelCase    					=	self.model.generate(
						    model_inputs.input_ids							,			attention_mask=model_inputs.attention_mask							,			num_beams=2  )
						__lowerCamelCase    					=	self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							,			skip_special_tokens=SCREAMING_SNAKE_CASE__  )
						return generated_words







		@slow
		def  __A       (		self		:							Optional[Any]  )     ->							List[Any]:
						self._assert_generated_batch_equal_expected()





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class    lowerCAmelCase__     :






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Dict:
						if dst_width < 0 or dst_height < 0:
										raise ValueError('''Destination width/height should be > 0'''  )

						__lowerCamelCase    					=	img
						__lowerCamelCase    					=	img.shape[1]
						__lowerCamelCase    					=	img.shape[0]
						__lowerCamelCase    					=	dst_width
						__lowerCamelCase    					=	dst_height

						__lowerCamelCase    					=	self.src_w / self.dst_w
						__lowerCamelCase    					=	self.src_h / self.dst_h

						__lowerCamelCase    					=	__lowerCamelCase    					=	(
						    np.ones((self.dst_h, self.dst_w, 3)							,			np.uinta  ) * 2_55
						)






		def  __A       (		self		:							List[Any]  )     ->							str:
						for i in range(self.dst_h  ):
										for j in range(self.dst_w  ):
														__lowerCamelCase    					=	self.img[self.get_y(SCREAMING_SNAKE_CASE__  )][self.get_x(SCREAMING_SNAKE_CASE__  )]






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_x * x  )







		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_y * y  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__   :			List[str]												=    800, 600
					SCREAMING_SNAKE_CASE__   :			int												=    imread("image_data/lena.jpg", 1)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    NearestNeighbour(im, dst_w, dst_h)
					n.process()

					imshow(
					    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
					)
					waitKey(0)
					destroyAllWindows()



