




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_							:


 '''simple docstring'''







 lowercase_       					=					MarianConfig
 lowercase_       					=					{}
 lowercase_       					=					"gelu"



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

       SCREAMING_SNAKE_CASE_					=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE_					=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_					=    max_position_embeddings
       SCREAMING_SNAKE_CASE_					=    eos_token_id
       SCREAMING_SNAKE_CASE_					=    pad_token_id
       SCREAMING_SNAKE_CASE_					=    bos_token_id



 def    lowerCAmelCase_						(							self						:	str		):
       SCREAMING_SNAKE_CASE_					=    ids_tensor([self.batch_size, self.seq_length - 1]       ,       self.vocab_size		)
       SCREAMING_SNAKE_CASE_					=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)       ,       1		)
       SCREAMING_SNAKE_CASE_					=    tf.concat([input_ids, eos_tensor]       ,       axis=1		)

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

       SCREAMING_SNAKE_CASE_					=    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       ,       )
       SCREAMING_SNAKE_CASE_					=    prepare_marian_inputs_dict(_lowerCAmelCase       ,       _lowerCAmelCase       ,       _lowerCAmelCase		)
       return config, inputs_dict




 def    lowerCAmelCase_						(							self						:	Any       ,       _lowerCAmelCase						:	str       ,       _lowerCAmelCase						:	List[str]		):
       SCREAMING_SNAKE_CASE_					=    TFMarianModel(config=_lowerCAmelCase		).get_decoder()
       SCREAMING_SNAKE_CASE_					=    inputs_dict['input_ids']

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

       # first forward pass
       SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase       ,       attention_mask=_lowerCAmelCase       ,       head_mask=_lowerCAmelCase       ,       use_cache=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    outputs.to_tuple()

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

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

       SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase       ,       attention_mask=_lowerCAmelCase		)[0]
       SCREAMING_SNAKE_CASE_					=    model(_lowerCAmelCase       ,       attention_mask=_lowerCAmelCase       ,       past_key_values=_lowerCAmelCase		)[0]

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

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

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


def       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			Any    ,    __UpperCAmelCase		:			Union[str, Any]    ,    __UpperCAmelCase		:			int=None    ,    __UpperCAmelCase		:			Any=None    ,    __UpperCAmelCase		:			Union[str, Any]=None    ,    __UpperCAmelCase		:			Optional[Any]=None    ,    __UpperCAmelCase		:			Optional[Any]=None    ,    )      ->				List[Any]:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE_					=    tf.cast(tf.math.not_equal(__UpperCAmelCase    ,    config.pad_token_id							)    ,    tf.inta							)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE_					=    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:
            SCREAMING_SNAKE_CASE_					=    tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE_					=    tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE_					=    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_							(   _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     unittest.TestCase      ):


 '''simple docstring'''







 lowercase_       					=					(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
 lowercase_       					=					(TFMarianMTModel,) if is_tf_available() else ()
 lowercase_       					=					(
     {
         "conversational": TFMarianMTModel,
         "feature-extraction": TFMarianModel,
         "summarization": TFMarianMTModel,
         "text2text-generation": TFMarianMTModel,
         "translation": TFMarianMTModel,
     }
     if is_tf_available()
     else {}
 )
 lowercase_       					=					True
 lowercase_       					=					False
 lowercase_       					=					False



 def    lowerCAmelCase_						(							self						:	Optional[int]		):
       SCREAMING_SNAKE_CASE_					=    TFMarianModelTester(self		)
       SCREAMING_SNAKE_CASE_					=    ConfigTester(self       ,       config_class=_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	List[str]		):
       self.config_tester.run_common_tests()




 def    lowerCAmelCase_						(							self						:	Tuple		):
       SCREAMING_SNAKE_CASE_					=    self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase		)





@require_tf
class  lowerCamelCase_							(   unittest.TestCase      ):


 '''simple docstring'''







 lowercase_       					=					1_000  # show more chars for failing integration tests



 @classmethod
 def    lowerCAmelCase_						(							cls						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
       return cls



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



 @property
 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       return self.tokenizer.eos_token_id



 @cached_property
 def    lowerCAmelCase_						(							self						:	Tuple		):
       warnings.simplefilter('error'		)
       SCREAMING_SNAKE_CASE_					=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
       assert isinstance(_lowerCAmelCase       ,       _lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    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    lowerCAmelCase_						(							self						:	Optional[Any]       ,       **_lowerCAmelCase						:	List[str]		):
       SCREAMING_SNAKE_CASE_					=    self.translate_src_text(**_lowerCAmelCase		)
       self.assertListEqual(self.expected_text       ,       _lowerCAmelCase		)




 def    lowerCAmelCase_						(							self						:	Dict       ,       **_lowerCAmelCase						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    self.tokenizer(self.src_text       ,       **_lowerCAmelCase       ,       padding=_lowerCAmelCase       ,       return_tensors='tf'		)
       SCREAMING_SNAKE_CASE_					=    self.model.generate(
           model_inputs.input_ids       ,       attention_mask=model_inputs.attention_mask       ,       num_beams=2       ,       max_length=128		)
       SCREAMING_SNAKE_CASE_					=    self.tokenizer.batch_decode(generated_ids.numpy()       ,       skip_special_tokens=_lowerCAmelCase		)
       return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 lowercase_       					=					"mt"
 lowercase_       					=					"en"
 lowercase_       					=					["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
 lowercase_       					=					["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]



 @unittest.skip('Skipping until #12647 is resolved.'		)
 @slow
 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 lowercase_       					=					"en"
 lowercase_       					=					"zh"
 lowercase_       					=					["My name is Wolfgang and I live in Berlin"]
 lowercase_       					=					["我叫沃尔夫冈 我住在柏林"]



 @unittest.skip('Skipping until #12647 is resolved.'		)
 @slow
 def    lowerCAmelCase_						(							self						:	str		):
       self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 lowercase_       					=					"en"
 lowercase_       					=					"ROMANCE"
 lowercase_       					=					[
     ">>fr<< Don't spend so much time watching TV.",
     ">>pt<< Your message has been sent.",
     ">>es<< He's two years older than me.",
 ]
 lowercase_       					=					[
     "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    lowerCAmelCase_						(							self						:	Optional[Any]		):
       self._assert_generated_batch_equal_expected()




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





from __future__ import annotations

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				list[int]:
      SCREAMING_SNAKE_CASE_					=    [0] * len(__UpperCAmelCase							)
      for i in range(1    ,    len(__UpperCAmelCase							)							):
            SCREAMING_SNAKE_CASE_					=    duration_times[i - 1] + waiting_times[i - 1]
      return waiting_times

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]    ,    __UpperCAmelCase		:			list[int]							)      ->				list[int]:
      return [
          duration_time + waiting_times[i]
          for i, duration_time in enumerate(__UpperCAmelCase							)
      ]

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				float:
      return sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			list[int]							)      ->				float:
      return sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)


if __name__ == "__main__":
   # process id's
   lowerCamelCase__				:     Union[str, Any]			       =							[1, 2, 3]

   # ensure that we actually have processes
   if len(processes) == 0:
      print('Zero amount of processes')
      raise SystemExit(0)

   # duration time of all processes
   lowerCamelCase__				:     Optional[Any]			       =							[19, 8, 9]

   # ensure we can match each id to a duration time
   if len(duration_times) != len(processes):
      print('Unable to match all id\'s with their duration time')
      raise SystemExit(0)

   # get the waiting times and the turnaround times
   lowerCamelCase__				:     Dict			       =							calculate_waiting_times(duration_times)
   lowerCamelCase__				:     int			       =							calculate_turnaround_times(duration_times, waiting_times)

   # get the average times
   lowerCamelCase__				:     Any			       =							calculate_average_waiting_time(waiting_times)
   lowerCamelCase__				:     Tuple			       =							calculate_average_turnaround_time(turnaround_times)

   # print all the results
   print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
   for i, process in enumerate(processes):
      print(
          f'''{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'''
          f'''{turnaround_times[i]}'''
      )
   print(f'''Average waiting time = {average_waiting_time}''')
   print(f'''Average turn around time = {average_turnaround_time}''')