

from collections import deque




class   UpperCamelCase			:

    def __init__(      self			:   Any				,     snake_case__			:   str				,     snake_case__			:   int				,     snake_case__			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      process_name  # process name
     SCREAMING_SNAKE_CASE		    =      arrival_time  # arrival time of the process
     # completion time of finished process or last interrupted time
     SCREAMING_SNAKE_CASE		    =      arrival_time
     SCREAMING_SNAKE_CASE		    =      burst_time  # remaining burst time
     SCREAMING_SNAKE_CASE		    =      0  # total time of the process wait in ready queue
     SCREAMING_SNAKE_CASE		    =      0  # time from arrival time to completion time




class   UpperCamelCase			:

    def __init__(      self			:   str				,     snake_case__			:   int				,     snake_case__			:   list[int]				,     snake_case__			:   deque[Process]				,     snake_case__			:   int				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      number_of_queues
     # time slice of queues that round robin algorithm applied
     SCREAMING_SNAKE_CASE		    =      time_slices
     # unfinished process is in this ready_queue
     SCREAMING_SNAKE_CASE		    =      queue
     # current time
     SCREAMING_SNAKE_CASE		    =      current_time
     # finished process is in this sequence queue
     SCREAMING_SNAKE_CASE		    =      deque()

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      []
     for i in range(len(self.finish_queue     )     ):
      sequence.append(self.finish_queue[i].process_name     )
     return sequence

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   list[Process]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      []
     for i in range(len(snake_case__     )     ):
      waiting_times.append(queue[i].waiting_time     )
     return waiting_times

    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   list[Process]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      []
     for i in range(len(snake_case__     )     ):
      turnaround_times.append(queue[i].turnaround_time     )
     return turnaround_times

    def UpperCamelCase      (      self			:   Optional[int]				,     snake_case__			:   list[Process]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      []
     for i in range(len(snake_case__     )     ):
      completion_times.append(queue[i].stop_time     )
     return completion_times

    def UpperCamelCase      (      self			:   Any				,     snake_case__			:   deque[Process]     ):

     """simple docstring"""






     return [q.burst_time for q in queue]

    def UpperCamelCase      (      self			:   str				,     snake_case__			:   Process     ):

     """simple docstring"""






     process.waiting_time += self.current_time - process.stop_time
     return process.waiting_time

    def UpperCamelCase      (      self			:   List[Any]				,     snake_case__			:   deque[Process]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      deque()  # sequence deque of finished process
     while len(snake_case__     ) != 0:
      SCREAMING_SNAKE_CASE		    =      ready_queue.popleft()  # current process

      # if process's arrival time is later than current time, update current time
      if self.current_time < cp.arrival_time:
       self.current_time += cp.arrival_time

      # update waiting time of current process
      self.update_waiting_time(snake_case__     )
      # update current time
      self.current_time += cp.burst_time
      # finish the process and set the process's burst-time 0
      SCREAMING_SNAKE_CASE		    =      0
      # set the process's turnaround time because it is finished
      SCREAMING_SNAKE_CASE		    =      self.current_time - cp.arrival_time
      # set the completion time
      SCREAMING_SNAKE_CASE		    =      self.current_time
      # add the process to queue that has finished queue
      finished.append(snake_case__     )

     self.finish_queue.extend(snake_case__     )  # add finished process to finish queue
     # FCFS will finish all remaining processes
     return finished

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   deque[Process]				,     snake_case__			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      deque()  # sequence deque of terminated process
     # just for 1 cycle and unfinished processes will go back to queue
     for _ in range(len(snake_case__     )     ):
      SCREAMING_SNAKE_CASE		    =      ready_queue.popleft()  # current process

      # if process's arrival time is later than current time, update current time
      if self.current_time < cp.arrival_time:
       self.current_time += cp.arrival_time

      # update waiting time of unfinished processes
      self.update_waiting_time(snake_case__     )
      # if the burst time of process is bigger than time-slice
      if cp.burst_time > time_slice:
       # use CPU for only time-slice
       self.current_time += time_slice
       # update remaining burst time
       cp.burst_time -= time_slice
       # update end point time
       SCREAMING_SNAKE_CASE		    =      self.current_time
       # locate the process behind the queue because it is not finished
       ready_queue.append(snake_case__     )
      else:
       # use CPU for remaining burst time
       self.current_time += cp.burst_time
       # set burst time 0 because the process is finished
       SCREAMING_SNAKE_CASE		    =      0
       # set the finish time
       SCREAMING_SNAKE_CASE		    =      self.current_time
       # update the process' turnaround time because it is finished
       SCREAMING_SNAKE_CASE		    =      self.current_time - cp.arrival_time
       # add the process to queue that has finished queue
       finished.append(snake_case__     )

     self.finish_queue.extend(snake_case__     )  # add finished process to finish queue
     # return finished processes queue and remaining processes queue
     return finished, ready_queue




    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     for i in range(self.number_of_queues - 1     ):
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.round_robin(
          self.ready_queue				,     self.time_slices[i]     )
     #  the last queue has first_come_first_served algorithm
     self.first_come_first_served(self.ready_queue     )

     return self.finish_queue


if __name__ == "__main__":
      import doctest

      a_     :    List[Any]     		=   Process("P1", 0, 53)
      a_     :    Any     		=   Process("P2", 0, 17)
      a_     :    Dict     		=   Process("P3", 0, 68)
      a_     :    Tuple     		=   Process("P4", 0, 24)
      a_     :    List[Any]     		=   3
      a_     :    int     		=   [17, 25]
      a_     :    Tuple     		=   deque([Pa, Pa, Pa, Pa])

      if len(time_slices) != number_of_queues - 1:
            raise SystemExit(0)

      doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})

      a_     :    str     		=   Process("P1", 0, 53)
      a_     :    Dict     		=   Process("P2", 0, 17)
      a_     :    List[Any]     		=   Process("P3", 0, 68)
      a_     :    Any     		=   Process("P4", 0, 24)
      a_     :    Dict     		=   3
      a_     :    List[Any]     		=   [17, 25]
      a_     :    Optional[Any]     		=   deque([Pa, Pa, Pa, Pa])
      a_     :    Optional[int]     		=   MLFQ(number_of_queues, time_slices, queue, 0)
      a_     :    List[str]     		=   mlfq.multi_level_feedback_queue()

      # print total waiting times of processes(P1, P2, P3, P4)
      print(
          F"""waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"""
      )
      # print completion times of processes(P1, P2, P3, P4)
      print(
          F"""completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"""
      )
      # print total turnaround times of processes(P1, P2, P3, P4)
      print(
          F"""turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"""
      )
      # print sequence of finished processes
      print(
          F"""sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}"""
      )




import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
      from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Dict=None		,				_UpperCamelCase		:	Dict=None		,				_UpperCamelCase		:	int=None		,				_UpperCamelCase		:	Optional[Any]=None		,				_UpperCamelCase		:	List[Any]=None		,				)		->				Tuple:
 '''simple docstring'''


 if attention_mask is None:
  SCREAMING_SNAKE_CASE		    =      input_ids.ne(config.pad_token_id						)
 if decoder_attention_mask is None:
  SCREAMING_SNAKE_CASE		    =      decoder_input_ids.ne(config.pad_token_id						)
 if head_mask is None:
  SCREAMING_SNAKE_CASE		    =      torch.ones(config.encoder_layers		,				config.encoder_attention_heads		,				device=_UpperCamelCase						)
 if decoder_head_mask is None:
  SCREAMING_SNAKE_CASE		    =      torch.ones(config.decoder_layers		,				config.decoder_attention_heads		,				device=_UpperCamelCase						)
 if cross_attn_head_mask is None:
  SCREAMING_SNAKE_CASE		    =      torch.ones(config.decoder_layers		,				config.decoder_attention_heads		,				device=_UpperCamelCase						)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }




class   UpperCamelCase			:

    def __init__(      self			:   Optional[Any]				,     snake_case__			:   List[str]				,     snake_case__			:   Any=1_3				,     snake_case__			:   List[str]=7				,     snake_case__			:   Optional[int]=True				,     snake_case__			:   Tuple=False				,     snake_case__			:   Optional[int]=9_9				,     snake_case__			:   List[str]=1_6				,     snake_case__			:   int=2				,     snake_case__			:   Optional[int]=4				,     snake_case__			:   str=4				,     snake_case__			:   Dict="relu"				,     snake_case__			:   Tuple=0.1				,     snake_case__			:   Union[str, Any]=0.1				,     snake_case__			:   Any=0.0				,     snake_case__			:   Dict=0.0				,     snake_case__			:   Optional[Any]=2_0				,     snake_case__			:   int=2				,     snake_case__			:   Optional[Any]=1				,     snake_case__			:   Any=0				,     ):

     """simple docstring"""






     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_act
     SCREAMING_SNAKE_CASE		    =      hidden_dropout_prob
     SCREAMING_SNAKE_CASE		    =      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE		    =      encoder_layerdrop
     SCREAMING_SNAKE_CASE		    =      decoder_layerdrop
     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 UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size     )
     SCREAMING_SNAKE_CASE		    =      self.eos_token_id  # Eos Token
     SCREAMING_SNAKE_CASE		    =      ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size     )

     # we need to clamp the input ids here to avoid having pad token in between
     # this is because for M2M100 the position_ids are prepared such that
     # all pad tokens have pos id = 2 and rest are between 2..seq_length
     # and the seq_length here is seq_length - num_pad_tokens
     # but when using past, there is no way of knowing if the past input ids had
     # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
     # position_ids being off by num_pad_tokens in past input
     SCREAMING_SNAKE_CASE		    =      input_ids.clamp(self.pad_token_id + 1     )
     SCREAMING_SNAKE_CASE		    =      decoder_input_ids.clamp(self.pad_token_id + 1     )

     SCREAMING_SNAKE_CASE		    =      self.get_config()
     SCREAMING_SNAKE_CASE		    =      prepare_mam_aaa_inputs_dict(snake_case__				,     snake_case__				,     snake_case__     )
     return config, inputs_dict

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     return MaMaaaConfig(
         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				,     encoder_layerdrop=self.encoder_layerdrop				,     decoder_layerdrop=self.decoder_layerdrop				,     max_position_embeddings=self.max_position_embeddings				,     eos_token_id=self.eos_token_id				,     bos_token_id=self.bos_token_id				,     pad_token_id=self.pad_token_id				,     )

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.prepare_config_and_inputs()
     return config, inputs_dict

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[str]				,     snake_case__			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaModel(config=snake_case__     ).get_decoder().to(snake_case__     ).eval()
     SCREAMING_SNAKE_CASE		    =      inputs_dict['input_ids']
     SCREAMING_SNAKE_CASE		    =      inputs_dict['attention_mask']
     SCREAMING_SNAKE_CASE		    =      inputs_dict['head_mask']

     # first forward pass
     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__				,     head_mask=snake_case__				,     use_cache=snake_case__     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      outputs.to_tuple()

     # create hypothetical multiple next token and extent to next_input_ids
     SCREAMING_SNAKE_CASE		    =      ids_tensor((self.batch_size, 3)				,     config.vocab_size     )
     SCREAMING_SNAKE_CASE		    =      ids_tensor((self.batch_size, 3)				,     2     )

     # append to next input_ids and
     SCREAMING_SNAKE_CASE		    =      torch.cat([input_ids, next_tokens]				,     dim=-1     )
     SCREAMING_SNAKE_CASE		    =      torch.cat([attention_mask, next_attn_mask]				,     dim=-1     )

     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__     )['last_hidden_state']
     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__				,     past_key_values=snake_case__     )[
         'last_hidden_state'
     ]

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

     self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

     # test that outputs are equal for slice
     self.parent.assertTrue(torch.allclose(snake_case__				,     snake_case__				,     atol=1E-2     )     )




    def UpperCamelCase      (      self			:   List[str]				,     snake_case__			:   Any				,     snake_case__			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaModel(config=snake_case__     ).to(snake_case__     ).eval()
     SCREAMING_SNAKE_CASE		    =      model(**snake_case__     )

     SCREAMING_SNAKE_CASE		    =      outputs.encoder_last_hidden_state
     SCREAMING_SNAKE_CASE		    =      outputs.last_hidden_state

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE		    =      model.get_encoder()
      encoder.save_pretrained(snake_case__     )
      SCREAMING_SNAKE_CASE		    =      MaMaaaEncoder.from_pretrained(snake_case__     ).to(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      encoder(inputs_dict['input_ids']				,     attention_mask=inputs_dict['attention_mask']     )[
         0
     ]

     self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3     )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE		    =      model.get_decoder()
      decoder.save_pretrained(snake_case__     )
      SCREAMING_SNAKE_CASE		    =      MaMaaaDecoder.from_pretrained(snake_case__     ).to(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      decoder(
         input_ids=inputs_dict['decoder_input_ids']				,     attention_mask=inputs_dict['decoder_attention_mask']				,     encoder_hidden_states=snake_case__				,     encoder_attention_mask=inputs_dict['attention_mask']				,     )[0]

     self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3     )




@require_torch
class   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =(
        (
            MaMaaaModel,
            MaMaaaForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    __UpperCamelCase    =(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
    __UpperCamelCase    =(
        {
            "conversational": MaMaaaForConditionalGeneration,
            "feature-extraction": MaMaaaModel,
            "summarization": MaMaaaForConditionalGeneration,
            "text2text-generation": MaMaaaForConditionalGeneration,
            "translation": MaMaaaForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    __UpperCamelCase    =True
    __UpperCamelCase    =True
    __UpperCamelCase    =False
    __UpperCamelCase    =False

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   Any				,     snake_case__			:   Tuple				,     snake_case__			:   List[str]				,     snake_case__			:   Optional[int]				,     snake_case__			:   Optional[Any]     ):

     """simple docstring"""






     if pipeline_test_casse_name == "TranslationPipelineTests":
      # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
      # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
      return True

     return False

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaModelTester(self     )
     SCREAMING_SNAKE_CASE		    =      ConfigTester(self				,     config_class=snake_case__     )

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE		    =      model_class(snake_case__     )

      with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(snake_case__     )
       SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      model_class.from_pretrained(snake_case__				,     output_loading_info=snake_case__     )
      self.assertEqual(info['missing_keys']				,     []     )

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case__     )

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_encoder_decoder_model_standalone(*snake_case__     )

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
      SCREAMING_SNAKE_CASE		    =      model_class(snake_case__     )
      model.to(snake_case__     )
      model.eval()

      SCREAMING_SNAKE_CASE		    =      copy.deepcopy(self._prepare_for_class(snake_case__				,     snake_case__     )     )

      if not self.is_encoder_decoder:
       SCREAMING_SNAKE_CASE		    =      inputs['input_ids']
       del inputs["input_ids"]
      else:
       SCREAMING_SNAKE_CASE		    =      inputs['input_ids']
       SCREAMING_SNAKE_CASE		    =      inputs.get('decoder_input_ids'				,     snake_case__     )
       del inputs["input_ids"]
       inputs.pop('decoder_input_ids'				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      model.get_input_embeddings()
      if not self.is_encoder_decoder:
       SCREAMING_SNAKE_CASE		    =      wte(snake_case__     )
      else:
       SCREAMING_SNAKE_CASE		    =      wte(snake_case__     )
       SCREAMING_SNAKE_CASE		    =      wte(snake_case__     )

      with torch.no_grad():
       model(**snake_case__     )[0]




    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE		    =      input_dict['input_ids']
     SCREAMING_SNAKE_CASE		    =      input_ids.ne(1     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      MaMaaaForConditionalGeneration(snake_case__     ).eval().to(snake_case__     )
     if torch_device == "cuda":
      model.half()
     model.generate(snake_case__				,     attention_mask=snake_case__     )
     model.generate(num_beams=4				,     do_sample=snake_case__				,     early_stopping=snake_case__				,     num_return_sequences=3     )


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple						)		->				Dict:
 '''simple docstring'''


 return torch.tensor(_UpperCamelCase		,				dtype=torch.long		,				device=_UpperCamelCase						)


a_     :    Optional[int]     		=   1e-4




@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class   UpperCamelCase			(  unittest.TestCase ):

    @cached_property
    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'     )

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaModel.from_pretrained('facebook/m2m100_418M'     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]     )
     SCREAMING_SNAKE_CASE		    =      _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]     )
     SCREAMING_SNAKE_CASE		    =      prepare_mam_aaa_inputs_dict(model.config				,     snake_case__				,     snake_case__     )
     with torch.no_grad():
      SCREAMING_SNAKE_CASE		    =      model(**snake_case__     )[0]
     SCREAMING_SNAKE_CASE		    =      torch.Size((1, 1_1, 1_0_2_4)     )
     self.assertEqual(output.shape				,     snake_case__     )
     # change to expected output here
     SCREAMING_SNAKE_CASE		    =      torch.tensor(
         [[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]]				,     device=snake_case__     )
     self.assertTrue(torch.allclose(output[:, :3, :3]				,     snake_case__				,     atol=snake_case__     )     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'     ).to(snake_case__     )

     # change to intended input
     SCREAMING_SNAKE_CASE		    =      _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]     )
     SCREAMING_SNAKE_CASE		    =      _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]     )
     SCREAMING_SNAKE_CASE		    =      prepare_mam_aaa_inputs_dict(model.config				,     snake_case__				,     snake_case__     )
     with torch.no_grad():
      SCREAMING_SNAKE_CASE		    =      model(**snake_case__     )[0]
     SCREAMING_SNAKE_CASE		    =      torch.Size((1, 1_1, model.config.vocab_size)     )
     self.assertEqual(output.shape				,     snake_case__     )
     # change to expected output here
     SCREAMING_SNAKE_CASE		    =      torch.tensor(
         [[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]]				,     device=snake_case__     )
     self.assertTrue(torch.allclose(output[:, :3, :3]				,     snake_case__				,     atol=snake_case__     )     )




    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'				,     src_lang='fr'				,     tgt_lang='en'     )

     SCREAMING_SNAKE_CASE		    =      [
         'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
         'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
         'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
         ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
         ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
     ]

     # The below article tests that we don't add any hypotheses outside of the top n_beams
     SCREAMING_SNAKE_CASE		    =      tokenizer(snake_case__				,     padding=snake_case__				,     return_tensors='pt'     )

     SCREAMING_SNAKE_CASE		    =      model.generate(
         input_ids=dct['input_ids'].to(snake_case__     )				,     attention_mask=dct['attention_mask'].to(snake_case__     )				,     num_beams=5				,     forced_bos_token_id=tokenizer.get_lang_id('en'     )				,     )

     SCREAMING_SNAKE_CASE		    =      [
         'The NSA case highlights the total absence of intelligence debate',
         'I think there are two levels of response from the French government.',
         'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
         ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
         ' communications in France.',
     ]

     SCREAMING_SNAKE_CASE		    =      tokenizer.batch_decode(
         hypotheses_batch.tolist()				,     clean_up_tokenization_spaces=snake_case__				,     skip_special_tokens=snake_case__     )
     assert generated == expected_en

