






"""simple docstring"""





from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel
@require_tf
class  __lowerCamelCase     :
   a   :					List[str]			    =MarianConfig
   a   :					Tuple			    ={}
   a   :					Tuple			    ="""gelu"""

   def __init__(       self			,					snake_case_			,					snake_case_=13			,					snake_case_=7			,					snake_case_=True			,					snake_case_=False			,					snake_case_=99			,					snake_case_=32			,					snake_case_=2			,					snake_case_=4			,					snake_case_=37			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=20			,					snake_case_=2			,					snake_case_=1			,					snake_case_=0			,					)		->							Union[str, Any]:
       UpperCamelCase__								=     parent
       UpperCamelCase__								=     batch_size
       UpperCamelCase__								=     seq_length
       UpperCamelCase__								=     is_training
       UpperCamelCase__								=     use_labels
       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     intermediate_size

       UpperCamelCase__								=     hidden_dropout_prob
       UpperCamelCase__								=     attention_probs_dropout_prob
       UpperCamelCase__								=     max_position_embeddings
       UpperCamelCase__								=     eos_token_id
       UpperCamelCase__								=     pad_token_id
       UpperCamelCase__								=     bos_token_id

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       UpperCamelCase__								=     ids_tensor([self.batch_size, self.seq_length - 1]			,					self.vocab_size   )
       UpperCamelCase__								=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )			,					1   )
       UpperCamelCase__								=     tf.concat([input_ids, eos_tensor]			,					axis=1   )

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

       UpperCamelCase__								=     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			,					)
       UpperCamelCase__								=     prepare_marian_inputs_dict(snake_case_			,					snake_case_			,					snake_case_   )
       return config, inputs_dict







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							Optional[Any]:
       UpperCamelCase__								=     TFMarianModel(config=snake_case_   ).get_decoder()
       UpperCamelCase__								=     inputs_dict['input_ids']

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

       # first forward pass
       UpperCamelCase__								=     model(snake_case_			,					attention_mask=snake_case_			,					head_mask=snake_case_			,					use_cache=snake_case_   )

       UpperCamelCase__							,    UpperCamelCase__								=     outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       UpperCamelCase__								=     ids_tensor((self.batch_size, 3)			,					config.vocab_size   )
       UpperCamelCase__								=     tf.cast(ids_tensor((self.batch_size, 3)			,					2   )			,					tf.inta   )

       # append to next input_ids and
       UpperCamelCase__								=     tf.concat([input_ids, next_tokens]			,					axis=-1   )
       UpperCamelCase__								=     tf.concat([attention_mask, next_attn_mask]			,					axis=-1   )

       UpperCamelCase__								=     model(snake_case_			,					attention_mask=snake_case_   )[0]
       UpperCamelCase__								=     model(snake_case_			,					attention_mask=snake_case_			,					past_key_values=snake_case_   )[0]

       self.parent.assertEqual(next_tokens.shape[1]			,					output_from_past.shape[1]   )

       # select random slice
       UpperCamelCase__								=     int(ids_tensor((1,)			,					output_from_past.shape[-1]   )   )
       UpperCamelCase__								=     output_from_no_past[:, -3:, random_slice_idx]
       UpperCamelCase__								=     output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(snake_case_			,					snake_case_			,					rtol=1E-3   )







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE=None			,						SCREAMING_SNAKE_CASE=None			,						SCREAMING_SNAKE_CASE=None			,						SCREAMING_SNAKE_CASE=None			,						SCREAMING_SNAKE_CASE=None			,						)       ->						Optional[int]:
    """simple docstring"""

    if attention_mask is None:
        UpperCamelCase__								=     tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE			,						config.pad_token_id       )			,						tf.inta       )
    if decoder_attention_mask is None:
        UpperCamelCase__								=     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:
        UpperCamelCase__								=     tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
    if decoder_head_mask is None:
        UpperCamelCase__								=     tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
    if cross_attn_head_mask is None:
        UpperCamelCase__								=     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     (	_a				,						_a				,						unittest.TestCase	):
   a   :					Any			    =(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
   a   :					int			    =(TFMarianMTModel,) if is_tf_available() else ()
   a   :					Tuple			    =(
       {
           """conversational""": TFMarianMTModel,
           """feature-extraction""": TFMarianModel,
           """summarization""": TFMarianMTModel,
           """text2text-generation""": TFMarianMTModel,
           """translation""": TFMarianMTModel,
       }
       if is_tf_available()
       else {}
   )
   a   :					List[str]			    =True
   a   :					int			    =False
   a   :					Tuple			    =False

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       UpperCamelCase__								=     TFMarianModelTester(self   )
       UpperCamelCase__								=     ConfigTester(self			,					config_class=snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       self.config_tester.run_common_tests()







   def     SCREAMING_SNAKE_CASE__			(       self   )		->							str:
       UpperCamelCase__								=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*snake_case_   )







@require_tf
class  __lowerCamelCase     (	unittest.TestCase	):
   a   :					List[Any]			    =1_0_0_0  # show more chars for failing integration tests

   @classmethod
   def     SCREAMING_SNAKE_CASE__			(       cls   )		->							None:
       UpperCamelCase__								=     F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
       return cls

   @cached_property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							MarianTokenizer:
       return AutoTokenizer.from_pretrained(self.model_name   )

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       return self.tokenizer.eos_token_id

   @cached_property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[str]:
       warnings.simplefilter('error'   )
       UpperCamelCase__								=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
       assert isinstance(snake_case_			,					snake_case_   )
       UpperCamelCase__								=     model.config
       self.assertListEqual(c.bad_words_ids			,					[[c.pad_token_id]]   )
       self.assertEqual(c.max_length			,					512   )
       self.assertEqual(c.decoder_start_token_id			,					c.pad_token_id   )
       return model

   def     SCREAMING_SNAKE_CASE__			(       self			,					**snake_case_   )		->							Dict:
       UpperCamelCase__								=     self.translate_src_text(**snake_case_   )
       self.assertListEqual(self.expected_text			,					snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					**snake_case_   )		->							Any:
       UpperCamelCase__								=     self.tokenizer(self.src_text			,					**snake_case_			,					padding=snake_case_			,					return_tensors='tf'   )
       UpperCamelCase__								=     self.model.generate(
           model_inputs.input_ids			,					attention_mask=model_inputs.attention_mask			,					num_beams=2			,					max_length=128   )
       UpperCamelCase__								=     self.tokenizer.batch_decode(generated_ids.numpy()			,					skip_special_tokens=snake_case_   )
       return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class  __lowerCamelCase     (	_a	):
   a   :					Any			    ="""mt"""
   a   :					Tuple			    ="""en"""
   a   :					Dict			    =["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
   a   :					Optional[Any]			    =["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]

   @unittest.skip('Skipping until #12647 is resolved.'   )
   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[str]:
       self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    ="""en"""
   a   :					Union[str, Any]			    ="""zh"""
   a   :					Optional[int]			    =["""My name is Wolfgang and I live in Berlin"""]
   a   :					Union[str, Any]			    =["""我叫沃尔夫冈 我住在柏林"""]

   @unittest.skip('Skipping until #12647 is resolved.'   )
   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Any:
       self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class  __lowerCamelCase     (	_a	):
   a   :					Dict			    ="""en"""
   a   :					List[str]			    ="""ROMANCE"""
   a   :					Any			    =[
       """>>fr<< Don't spend so much time watching TV.""",
       """>>pt<< Your message has been sent.""",
       """>>es<< He's two years older than me.""",
   ]
   a   :					Dict			    =[
       """Ne passez pas autant de temps à regarder la télé.""",
       """A sua mensagem foi enviada.""",
       """Es dos años más viejo que yo.""",
   ]

   @unittest.skip('Skipping until #12647 is resolved.'   )
   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[str]:
       self._assert_generated_batch_equal_expected()







   @unittest.skip('Skipping until #12647 is resolved.'   )
   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[Any]:
       UpperCamelCase__								=     TranslationPipeline(self.model			,					self.tokenizer			,					framework='tf'   )
       UpperCamelCase__								=     pipeline(self.src_text   )
       self.assertEqual(self.expected_text			,					[x['translation_text'] for x in output]   )












"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__       :			str=						[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    return [int(SCREAMING_SNAKE_CASE       ) for c in F'{ruleset:08}'[:8]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    UpperCamelCase__								=     len(cells[0]       )  # 31
    UpperCamelCase__								=     []
    for i in range(SCREAMING_SNAKE_CASE       ):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        UpperCamelCase__								=     0 if i == 0 else cells[time][i - 1]
        UpperCamelCase__								=     0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        UpperCamelCase__								=     7 - int(F'{left_neighbor}{cells[time][i]}{right_neighbor}'			,						2       )
        next_generation.append(rule[situation]       )
    return next_generation







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Image.Image:
    """simple docstring"""

    UpperCamelCase__								=     Image.new('RGB'			,						(len(cells[0]       ), len(SCREAMING_SNAKE_CASE       ))       )
    UpperCamelCase__								=     img.load()
    # Generates image
    for w in range(img.width       ):
        for h in range(img.height       ):
            UpperCamelCase__								=     2_55 - int(2_55 * cells[h][w]       )
            UpperCamelCase__								=     (color, color, color)
    return img


if __name__ == "__main__":
  A__       :			Optional[int]=						bin(int(input("""Rule:\n""").strip()))[2:]
  A__       :			str=						format_ruleset(int(rule_num))
  for time in range(16):
    CELLS.append(new_generation(CELLS, rule, time))
  A__       :			Tuple=						generate_image(CELLS)
  # Uncomment to save the image
  # img.save(f"rule_{rule_num}.png")
  img.show()




