
'''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__       :
					lowerCAmelCase_					       =					MBartConfig
					lowerCAmelCase_					       =					{}
					lowerCAmelCase_					       =					'''gelu'''






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=13    ,							__SCREAMING_SNAKE_CASE=7    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=99    ,							__SCREAMING_SNAKE_CASE=32    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=4    ,							__SCREAMING_SNAKE_CASE=37    ,							__SCREAMING_SNAKE_CASE=0.1    ,							__SCREAMING_SNAKE_CASE=0.1    ,							__SCREAMING_SNAKE_CASE=20    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=1    ,							__SCREAMING_SNAKE_CASE=0    ,							):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	parent
											lowercase_     :					List[str]													=	batch_size
											lowercase_     :					Any													=	seq_length
											lowercase_     :					Dict													=	is_training
											lowercase_     :					List[Any]													=	use_labels
											lowercase_     :					str													=	vocab_size
											lowercase_     :					Optional[Any]													=	hidden_size
											lowercase_     :					List[str]													=	num_hidden_layers
											lowercase_     :					Union[str, Any]													=	num_attention_heads
											lowercase_     :					Tuple													=	intermediate_size
											lowercase_     :					Union[str, Any]													=	hidden_dropout_prob
											lowercase_     :					Dict													=	attention_probs_dropout_prob
											lowercase_     :					List[str]													=	max_position_embeddings
											lowercase_     :					List[str]													=	eos_token_id
											lowercase_     :					Union[str, Any]													=	pad_token_id
											lowercase_     :					List[Any]													=	bos_token_id






					def      _snake_case   (       self		):


											"""simple docstring"""





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

											lowercase_     :					Union[str, Any]													=	ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size		)

											lowercase_     :					List[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    ,							)
											lowercase_     :					Any													=	prepare_mbart_inputs_dict(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
											return config, inputs_dict






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					int													=	TFMBartModel(config=__SCREAMING_SNAKE_CASE		).get_decoder()
											lowercase_     :					Optional[int]													=	inputs_dict['''input_ids''']

											lowercase_     :					str													=	input_ids[:1, :]
											lowercase_     :					Any													=	inputs_dict['''attention_mask'''][:1, :]
											lowercase_     :					List[str]													=	inputs_dict['''head_mask''']
											lowercase_     :					int													=	1

											# first forward pass
											lowercase_     :					Any													=	model(__SCREAMING_SNAKE_CASE    ,							attention_mask=__SCREAMING_SNAKE_CASE    ,							head_mask=__SCREAMING_SNAKE_CASE    ,							use_cache=__SCREAMING_SNAKE_CASE		)

											lowercase_						,			lowercase_     :					List[str]													=	outputs.to_tuple()
											lowercase_     :					int													=	past_key_values[1]







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[str]				,				__SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			Any=None				,				__SCREAMING_SNAKE_CASE :			List[str]=None				,				__SCREAMING_SNAKE_CASE :			List[Any]=None				,				__SCREAMING_SNAKE_CASE :			List[Any]=None				,				__SCREAMING_SNAKE_CASE :			Tuple=None				,				):



						"""simple docstring"""





						if attention_mask is None:
												lowercase_     :					List[str]													=	tf.cast(tf.math.not_equal(__SCREAMING_SNAKE_CASE				,				config.pad_token_id							)				,				tf.inta							)
						if decoder_attention_mask is None:
												lowercase_     :					Optional[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:
												lowercase_     :					Optional[Any]													=	tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
						if decoder_head_mask is None:
												lowercase_     :					List[str]													=	tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
						if cross_attn_head_mask is None:
												lowercase_     :					Tuple													=	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__       (  lowerCamelCase_   ,						lowerCamelCase_   ,						unittest.TestCase					):
					lowerCAmelCase_					       =					(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
					lowerCAmelCase_					       =					(TFMBartForConditionalGeneration,) if is_tf_available() else ()
					lowerCAmelCase_					       =					(
					    {
					        '''conversational''': TFMBartForConditionalGeneration,
					        '''feature-extraction''': TFMBartModel,
					        '''summarization''': TFMBartForConditionalGeneration,
					        '''text2text-generation''': TFMBartForConditionalGeneration,
					        '''translation''': TFMBartForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase_					       =					True
					lowerCAmelCase_					       =					False
					lowerCAmelCase_					       =					False






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																	# Exception encountered when calling layer '...'
																	return True

											return False






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	TFMBartModelTester(self		)
											lowercase_     :					Any													=	ConfigTester(self    ,							config_class=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.config_tester.run_common_tests()






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	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					):
					lowerCAmelCase_					       =					[
					    ''' UN Chief Says There Is No Military Solution in Syria''',
					]
					lowerCAmelCase_					       =					[
					    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
					]
					lowerCAmelCase_					       =					'''facebook/mbart-large-en-ro'''






					@cached_property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return AutoTokenizer.from_pretrained(self.model_name		)






					@cached_property
					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					str													=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
											return model






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.translate_src_text(**__SCREAMING_SNAKE_CASE		)
											self.assertListEqual(self.expected_text    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.tokenizer(self.src_text    ,							**__SCREAMING_SNAKE_CASE    ,							return_tensors='''tf'''		)
											lowercase_     :					Optional[int]													=	self.model.generate(
											    model_inputs.input_ids    ,							attention_mask=model_inputs.attention_mask    ,							num_beams=2		)
											lowercase_     :					Optional[int]													=	self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    ,							skip_special_tokens=__SCREAMING_SNAKE_CASE		)
											return generated_words






					@slow
					def      _snake_case   (       self		):


											"""simple docstring"""





											self._assert_generated_batch_equal_expected()





'''simple docstring'''







from PIL import Image
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Image				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	(259 * (level + 255)) / (255 * (259 - level))

						def contrast(__SCREAMING_SNAKE_CASE :			int							) -> int:
												return int(128 + factor * (c - 128)							)

						return img.point(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change contrast to 170
														_lowercase			:  Union[str, Any]   							=	change_contrast(img, 1_7_0)
														cont_img.save("image_data/lena_high_contrast.png", format="png")



