






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       __SCREAMING_SNAKE_CASE   :
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   MBartConfig
						_SCREAMING_SNAKE_CASE		:    int						=   {}
						_SCREAMING_SNAKE_CASE		:    str						=   '''gelu'''






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






												"""simple docstring"""
												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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												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(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)
												return config, inputs_dict







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFMBartModel(config=_UpperCamelCase				).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(_UpperCamelCase       ,  attention_mask=_UpperCamelCase       ,  head_mask=_UpperCamelCase       ,  use_cache=_UpperCamelCase				)

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







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Union[str, Any]=None      ,    UpperCamelCase_    :   Tuple=None      ,    UpperCamelCase_    :   List[str]=None      ,    UpperCamelCase_    :   Tuple=None      ,    UpperCamelCase_    :   Union[str, Any]=None      ,    )					->				Optional[Any]:

						"""simple docstring"""



						if attention_mask is None:
												lowerCAmelCase__        =  tf.cast(tf.math.not_equal(UpperCamelCase_      ,    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       __SCREAMING_SNAKE_CASE   (				__lowercase						,   __lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    Dict						=   (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
						_SCREAMING_SNAKE_CASE		:    List[str]						=   (TFMBartForConditionalGeneration,) if is_tf_available() else ()
						_SCREAMING_SNAKE_CASE		:    Dict						=   (
						    {
						        '''conversational''': TFMBartForConditionalGeneration,
						        '''feature-extraction''': TFMBartModel,
						        '''summarization''': TFMBartForConditionalGeneration,
						        '''text2text-generation''': TFMBartForConditionalGeneration,
						        '''translation''': TFMBartForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						_SCREAMING_SNAKE_CASE		:    Tuple						=   True
						_SCREAMING_SNAKE_CASE		:    Any						=   False
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   False






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






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

												return False






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFMBartModelTester(self				)
												lowerCAmelCase__        =  ConfigTester(self       ,  config_class=_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.config_tester.run_common_tests()







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  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       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    Dict						=   [
						    ''' UN Chief Says There Is No Military Solution in Syria''',
						]
						_SCREAMING_SNAKE_CASE		:    Any						=   [
						    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
						]
						_SCREAMING_SNAKE_CASE		:    int						=   '''facebook/mbart-large-en-ro'''






						@cached_property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return AutoTokenizer.from_pretrained(self.model_name				)






						@cached_property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
												return model






						def  UpperCamelCase__ (		self       ,  **_UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.translate_src_text(**_UpperCamelCase				)
												self.assertListEqual(self.expected_text       ,  _UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  **_UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.tokenizer(self.src_text       ,  **_UpperCamelCase       ,  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(_UpperCamelCase       ,  skip_special_tokens=_UpperCamelCase				)
												return generated_words







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self._assert_generated_batch_equal_expected()













import colorsys

from PIL import Image  # type: ignore







def 		_UpperCamelCase				(      UpperCamelCase_    :   float      ,    UpperCamelCase_    :   float      ,    UpperCamelCase_    :   int						)					->				float:

						"""simple docstring"""



						lowerCAmelCase__        =  x
						lowerCAmelCase__        =  y
						for step in range(UpperCamelCase_						):  # noqa: B007
												lowerCAmelCase__        =  a * a - b * b + x
												lowerCAmelCase__        =  2 * a * b + y
												lowerCAmelCase__        =  a_new

												# divergence happens for all complex number with an absolute value
												# greater than 4
												if a * a + b * b > 4:
																		break
						return step / (max_step - 1)







def 		_UpperCamelCase				(      UpperCamelCase_    :   float						)					->				tuple:

						"""simple docstring"""



						if distance == 1:
												return (0, 0, 0)
						else:
												return (255, 255, 255)







def 		_UpperCamelCase				(      UpperCamelCase_    :   float						)					->				tuple:

						"""simple docstring"""



						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 255						) for i in colorsys.hsv_to_rgb(UpperCamelCase_      ,    1      ,    1						)						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   int = 800      ,    UpperCamelCase_    :   int = 600      ,    UpperCamelCase_    :   float = -0.6      ,    UpperCamelCase_    :   float = 0      ,    UpperCamelCase_    :   float = 3.2      ,    UpperCamelCase_    :   int = 50      ,    UpperCamelCase_    :   bool = True      ,    )					->				Image.Image:

						"""simple docstring"""



						lowerCAmelCase__        =  Image.new('RGB'      ,    (image_width, image_height)						)
						lowerCAmelCase__        =  img.load()

						# loop through the image-coordinates
						for image_x in range(UpperCamelCase_						):
												for image_y in range(UpperCamelCase_						):
																		# determine the figure-coordinates based on the image-coordinates
																		lowerCAmelCase__        =  figure_width / image_width * image_height
																		lowerCAmelCase__        =  figure_center_x + (image_x / image_width - 0.5) * figure_width
																		lowerCAmelCase__        =  figure_center_y + (image_y / image_height - 0.5) * figure_height

																		lowerCAmelCase__        =  get_distance(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								lowerCAmelCase__        =  get_color_coded_rgb(UpperCamelCase_						)
																		else:
																								lowerCAmelCase__        =  get_black_and_white_rgb(UpperCamelCase_						)

						return img


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# colored version, full figure
					__snake_case      :      str             =       get_image()

					# uncomment for colored version, different section, zoomed in
					# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
					# figure_width = 0.8)

					# uncomment for black and white version, full figure
					# img = get_image(use_distance_color_coding = False)

					# uncomment to save the image
					# img.save("mandelbrot.png")

					img.show()





