
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     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	):
 if attention_mask is None:
  lowerCamelCase		:	Optional[Any]							=      input_ids.ne(config.pad_token_id							)
 if decoder_attention_mask is None:
  lowerCamelCase		:	Optional[int]							=      decoder_input_ids.ne(config.pad_token_id							)
 if head_mask is None:
  lowerCamelCase		:	List[str]							=      torch.ones(config.encoder_layers,	config.encoder_attention_heads,	device=lowerCamelCase							)
 if decoder_head_mask is None:
  lowerCamelCase		:	int							=      torch.ones(config.decoder_layers,	config.decoder_attention_heads,	device=lowerCamelCase							)
 if cross_attn_head_mask is None:
  lowerCamelCase		:	Dict							=      torch.ones(config.decoder_layers,	config.decoder_attention_heads,	device=lowerCamelCase							)
 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 							A__		:
      def __init__(      self    ,  __magic_name__    ,  __magic_name__=1_3    ,  __magic_name__=7    ,  __magic_name__=True    ,  __magic_name__=False    ,  __magic_name__=9_9    ,  __magic_name__=1_6    ,  __magic_name__=2    ,  __magic_name__=4    ,  __magic_name__=4    ,  __magic_name__="relu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=2_0    ,  __magic_name__=2    ,  __magic_name__=1    ,  __magic_name__=0    ,  ):
       lowerCamelCase		:	List[Any]							=      parent
       lowerCamelCase		:	Dict							=      batch_size
       lowerCamelCase		:	str							=      seq_length
       lowerCamelCase		:	List[str]							=      is_training
       lowerCamelCase		:	Dict							=      use_labels
       lowerCamelCase		:	Optional[Any]							=      vocab_size
       lowerCamelCase		:	Any							=      hidden_size
       lowerCamelCase		:	Optional[Any]							=      num_hidden_layers
       lowerCamelCase		:	List[str]							=      num_attention_heads
       lowerCamelCase		:	Tuple							=      intermediate_size
       lowerCamelCase		:	Dict							=      hidden_act
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Union[str, Any]							=      attention_probs_dropout_prob
       lowerCamelCase		:	List[str]							=      encoder_layerdrop
       lowerCamelCase		:	Any							=      decoder_layerdrop
       lowerCamelCase		:	Tuple							=      max_position_embeddings
       lowerCamelCase		:	Tuple							=      eos_token_id
       lowerCamelCase		:	List[Any]							=      pad_token_id
       lowerCamelCase		:	Optional[Any]							=      bos_token_id
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size      )
       lowerCamelCase		:	List[Any]							=      self.eos_token_id  # Eos Token
       lowerCamelCase		:	str							=      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
       lowerCamelCase		:	List[Any]							=      input_ids.clamp(self.pad_token_id + 1      )
       lowerCamelCase		:	int							=      decoder_input_ids.clamp(self.pad_token_id + 1      )

       lowerCamelCase		:	Tuple							=      self.get_config()
       lowerCamelCase		:	Optional[Any]							=      prepare_mam_aaa_inputs_dict(__magic_name__    ,  __magic_name__    ,  __magic_name__      )
       return config, inputs_dict
      def 						UpperCamelCase__							(      self      ):
       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      ):
       lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      self.prepare_config_and_inputs()
       return config, inputs_dict
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Optional[int]							=      MaMaaaModel(config=__magic_name__      ).get_decoder().to(__magic_name__      ).eval()
       lowerCamelCase		:	Optional[int]							=      inputs_dict["""input_ids"""]
       lowerCamelCase		:	Dict							=      inputs_dict["""attention_mask"""]
       lowerCamelCase		:	List[Any]							=      inputs_dict["""head_mask"""]

       # first forward pass
       lowerCamelCase		:	int							=      model(__magic_name__    ,  attention_mask=__magic_name__    ,  head_mask=__magic_name__    ,  use_cache=__magic_name__      )

       lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       lowerCamelCase		:	Tuple							=      ids_tensor((self.batch_size, 3)    ,  config.vocab_size      )
       lowerCamelCase		:	Optional[int]							=      ids_tensor((self.batch_size, 3)    ,  2      )

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

       lowerCamelCase		:	str							=      model(__magic_name__    ,  attention_mask=__magic_name__      )["""last_hidden_state"""]
       lowerCamelCase		:	Optional[int]							=      model(__magic_name__    ,  attention_mask=__magic_name__    ,  past_key_values=__magic_name__      )[
           """last_hidden_state"""
       ]

       # select random slice
       lowerCamelCase		:	int							=      ids_tensor((1,)    ,  output_from_past.shape[-1]      ).item()
       lowerCamelCase		:	Optional[int]							=      output_from_no_past[:, -3:, random_slice_idx].detach()
       lowerCamelCase		:	Tuple							=      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(__magic_name__    ,  __magic_name__    ,  atol=1e-2      )      )






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	List[str]							=      MaMaaaModel(config=__magic_name__      ).to(__magic_name__      ).eval()
       lowerCamelCase		:	Dict							=      model(**__magic_name__      )

       lowerCamelCase		:	Optional[int]							=      outputs.encoder_last_hidden_state
       lowerCamelCase		:	List[Any]							=      outputs.last_hidden_state

       with tempfile.TemporaryDirectory() as tmpdirname:
        lowerCamelCase		:	Tuple							=      model.get_encoder()
        encoder.save_pretrained(__magic_name__      )
        lowerCamelCase		:	Optional[Any]							=      MaMaaaEncoder.from_pretrained(__magic_name__      ).to(__magic_name__      )

       lowerCamelCase		:	Union[str, Any]							=      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:
        lowerCamelCase		:	int							=      model.get_decoder()
        decoder.save_pretrained(__magic_name__      )
        lowerCamelCase		:	List[Any]							=      MaMaaaDecoder.from_pretrained(__magic_name__      ).to(__magic_name__      )

       lowerCamelCase		:	Dict							=      decoder(
           input_ids=inputs_dict["""decoder_input_ids"""]    ,  attention_mask=inputs_dict["""decoder_attention_mask"""]    ,  encoder_hidden_states=__magic_name__    ,  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 							A__		(	__SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE  ,   unittest.TestCase):
      _UpperCAmelCase				:    Optional[int]           =			(
          (
              MaMaaaModel,
              MaMaaaForConditionalGeneration,
          )
          if is_torch_available()
          else ()
      )
      _UpperCAmelCase				:    Tuple           =			(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
      _UpperCAmelCase				:    str           =			(
          {
              """conversational""": MaMaaaForConditionalGeneration,
              """feature-extraction""": MaMaaaModel,
              """summarization""": MaMaaaForConditionalGeneration,
              """text2text-generation""": MaMaaaForConditionalGeneration,
              """translation""": MaMaaaForConditionalGeneration,
          }
          if is_torch_available()
          else {}
      )
      _UpperCAmelCase				:    List[str]           =			True
      _UpperCAmelCase				:    Any           =			True
      _UpperCAmelCase				:    Optional[Any]           =			False
      _UpperCAmelCase				:    Optional[int]           =			False
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       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      ):
       lowerCamelCase		:	str							=      MaMaaaModelTester(self      )
       lowerCamelCase		:	Dict							=      ConfigTester(self    ,  config_class=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       self.config_tester.run_common_tests()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase		:	Tuple							=      self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
        lowerCamelCase		:	List[str]							=      model_class(__magic_name__      )

        with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(__magic_name__      )
         lowerCamelCase			,    lowerCamelCase		:	int							=      model_class.from_pretrained(__magic_name__    ,  output_loading_info=__magic_name__      )
        self.assertEqual(info["""missing_keys"""]    ,  []      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_encoder_decoder_model_standalone(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
        lowerCamelCase		:	Optional[Any]							=      model_class(__magic_name__      )
        model.to(__magic_name__      )
        model.eval()

        lowerCamelCase		:	List[Any]							=      copy.deepcopy(self._prepare_for_class(__magic_name__    ,  __magic_name__      )      )

        if not self.is_encoder_decoder:
         lowerCamelCase		:	Optional[int]							=      inputs["""input_ids"""]
         del inputs["input_ids"]
        else:
         lowerCamelCase		:	List[Any]							=      inputs["""input_ids"""]
         lowerCamelCase		:	Any							=      inputs.get("""decoder_input_ids"""    ,  __magic_name__      )
         del inputs["input_ids"]
         inputs.pop("""decoder_input_ids"""    ,  __magic_name__      )

        lowerCamelCase		:	Any							=      model.get_input_embeddings()
        if not self.is_encoder_decoder:
         lowerCamelCase		:	Union[str, Any]							=      wte(__magic_name__      )
        else:
         lowerCamelCase		:	Any							=      wte(__magic_name__      )
         lowerCamelCase		:	Dict							=      wte(__magic_name__      )

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






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase		:	Tuple							=      self.model_tester.prepare_config_and_inputs()
       lowerCamelCase		:	Any							=      input_dict["""input_ids"""]
       lowerCamelCase		:	Union[str, Any]							=      input_ids.ne(1      ).to(__magic_name__      )
       lowerCamelCase		:	Dict							=      MaMaaaForConditionalGeneration(__magic_name__      ).eval().to(__magic_name__      )
       if torch_device == "cuda":
        model.half()
       model.generate(__magic_name__    ,  attention_mask=__magic_name__      )
       model.generate(num_beams=4    ,  do_sample=__magic_name__    ,  early_stopping=__magic_name__    ,  num_return_sequences=3      )





def     _a					(						lowerCamelCase							):
 return torch.tensor(lowerCamelCase,	dtype=torch.long,	device=lowerCamelCase							)


_lowerCamelCase					   =1E-4





@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 							A__		(	unittest.TestCase):
      @cached_property
      def 						UpperCamelCase__							(      self      ):
       return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""      ).to(__magic_name__      )
       lowerCamelCase		:	int							=      _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]]      )
       lowerCamelCase		:	Optional[Any]							=      _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]]      )
       lowerCamelCase		:	str							=      prepare_mam_aaa_inputs_dict(model.config    ,  __magic_name__    ,  __magic_name__      )
       with torch.no_grad():
        lowerCamelCase		:	List[Any]							=      model(**__magic_name__      )[0]
       lowerCamelCase		:	Any							=      torch.Size((1, 1_1, 1_0_2_4)      )
       self.assertEqual(output.shape    ,  __magic_name__      )
       # change to expected output here
       lowerCamelCase		:	Union[str, Any]							=      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=__magic_name__      )
       self.assertTrue(torch.allclose(output[:, :3, :3]    ,  __magic_name__    ,  atol=__magic_name__      )      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""      ).to(__magic_name__      )

       # change to intended input
       lowerCamelCase		:	int							=      _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]]      )
       lowerCamelCase		:	Optional[Any]							=      _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]]      )
       lowerCamelCase		:	Dict							=      prepare_mam_aaa_inputs_dict(model.config    ,  __magic_name__    ,  __magic_name__      )
       with torch.no_grad():
        lowerCamelCase		:	Union[str, Any]							=      model(**__magic_name__      )[0]
       lowerCamelCase		:	List[Any]							=      torch.Size((1, 1_1, model.config.vocab_size)      )
       self.assertEqual(output.shape    ,  __magic_name__      )
       # change to expected output here
       lowerCamelCase		:	Optional[Any]							=      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=__magic_name__      )
       self.assertTrue(torch.allclose(output[:, :3, :3]    ,  __magic_name__    ,  atol=__magic_name__      )      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""      ).to(__magic_name__      )
       lowerCamelCase		:	Dict							=      MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""    ,  src_lang="""fr"""    ,  tgt_lang="""en"""      )

       lowerCamelCase		:	List[Any]							=      [
           """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
       lowerCamelCase		:	List[str]							=      tokenizer(__magic_name__    ,  padding=__magic_name__    ,  return_tensors="""pt"""      )

       lowerCamelCase		:	List[str]							=      model.generate(
           input_ids=dct["""input_ids"""].to(__magic_name__      )    ,  attention_mask=dct["""attention_mask"""].to(__magic_name__      )    ,  num_beams=5    ,  forced_bos_token_id=tokenizer.get_lang_id("""en"""      )    ,  )

       lowerCamelCase		:	Union[str, Any]							=      [
           """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.""",
       ]

       lowerCamelCase		:	Optional[int]							=      tokenizer.batch_decode(
           hypotheses_batch.tolist()    ,  clean_up_tokenization_spaces=__magic_name__    ,  skip_special_tokens=__magic_name__      )
       assert generated == expected_en






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    List[Any]           =			"""gpt_neo"""
      _UpperCAmelCase				:    Union[str, Any]           =			["""past_key_values"""]
      _UpperCAmelCase				:    List[Any]           =			{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
      def __init__(      self    ,  __magic_name__=5_0_2_5_7    ,  __magic_name__=2_0_4_8    ,  __magic_name__=2_0_4_8    ,  __magic_name__=2_4    ,  __magic_name__=[[["global", "local"], 1_2]]    ,  __magic_name__=1_6    ,  __magic_name__=None    ,  __magic_name__=2_5_6    ,  __magic_name__="gelu_new"    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.1    ,  __magic_name__=1e-5    ,  __magic_name__=0.02    ,  __magic_name__=True    ,  __magic_name__=5_0_2_5_6    ,  __magic_name__=5_0_2_5_6    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	List[Any]							=      vocab_size
       lowerCamelCase		:	str							=      max_position_embeddings
       lowerCamelCase		:	str							=      hidden_size
       lowerCamelCase		:	Optional[int]							=      num_layers
       lowerCamelCase		:	str							=      num_heads
       lowerCamelCase		:	Optional[Any]							=      intermediate_size
       lowerCamelCase		:	List[Any]							=      window_size
       lowerCamelCase		:	int							=      activation_function
       lowerCamelCase		:	Union[str, Any]							=      resid_dropout
       lowerCamelCase		:	List[Any]							=      embed_dropout
       lowerCamelCase		:	List[str]							=      attention_dropout
       lowerCamelCase		:	Dict							=      classifier_dropout
       lowerCamelCase		:	Any							=      layer_norm_epsilon
       lowerCamelCase		:	Dict							=      initializer_range
       lowerCamelCase		:	Dict							=      use_cache

       lowerCamelCase		:	Optional[Any]							=      bos_token_id
       lowerCamelCase		:	int							=      eos_token_id

       lowerCamelCase		:	List[Any]							=      attention_types
       lowerCamelCase		:	Optional[Any]							=      self.expand_attention_types_params(__magic_name__      )

       if len(self.attention_layers      ) != self.num_layers:
        raise ValueError(
            """Configuration for convolutional module is incorrect. """
            """It is required that `len(config.attention_layers)` == `config.num_layers` """
            F'''but is `len(config.attention_layers) = {len(self.attention_layers      )}`, '''
            F'''`config.num_layers = {self.num_layers}`. '''
            """`config.attention_layers` is prepared using `config.attention_types`. """
            """Please verify the value of `config.attention_types` argument."""      )

       super().__init__(bos_token_id=__magic_name__    ,  eos_token_id=__magic_name__    ,  **__magic_name__      )






      @staticmethod
      def 						UpperCamelCase__							(      __magic_name__      ):
       lowerCamelCase		:	Optional[int]							=      []
       for item in attention_types:
        for _ in range(item[1]      ):
         attentions.extend(item[0]      )
       return attentions





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 import torch

 lowerCamelCase		:	Any							=      input.size()
 lowerCamelCase		:	List[Any]							=      len(lowerCamelCase							)
 lowerCamelCase		:	Optional[Any]							=      shape[dimension]

 lowerCamelCase		:	Optional[int]							=      torch.arange(0,	lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	Dict							=      torch.div(sizedim - size,	lowerCamelCase,	rounding_mode="""floor"""							) + 1
 lowerCamelCase		:	int							=      torch.arange(lowerCamelCase							) + low_indices[:min_length][:, None]

 lowerCamelCase		:	str							=      [slice(lowerCamelCase							)] * rank
 lowerCamelCase		:	List[str]							=      indices
 lowerCamelCase		:	Dict							=      input[s]

 lowerCamelCase		:	Any							=      list(range(0,	rank + 1							)							)
 perm.append(perm.pop(dimension + 1							)							)

 return sliced.permute(lowerCamelCase							)





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 import torch

 lowerCamelCase		:	List[Any]							=      torch.arange(1,	lowerCamelCase							)
 lowerCamelCase		:	Optional[int]							=      torch.remainder(lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	List[Any]							=      remainders == 0
 lowerCamelCase		:	List[Any]							=      candidates[divisor_indices]
 lowerCamelCase		:	Optional[Any]							=      torch.max(lowerCamelCase							)
 return largest_divisor, torch.div(lowerCamelCase,	lowerCamelCase,	rounding_mode="""floor"""							)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
       if self.use_past:
        self.fill_with_past_key_values_(__magic_name__    ,  direction="""inputs"""      )
        lowerCamelCase		:	int							=      {0: """batch""", 1: """past_sequence + sequence"""}
       else:
        lowerCamelCase		:	Tuple							=      {0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def 						UpperCamelCase__							(      self      ):
       return self._config.num_heads
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = -1    ,  __magic_name__ = -1    ,  __magic_name__ = False    ,  __magic_name__ = None    ,  ):
       lowerCamelCase		:	Optional[int]							=      super(__magic_name__    ,  self      ).generate_dummy_inputs(
           __magic_name__    ,  batch_size=__magic_name__    ,  seq_length=__magic_name__    ,  is_pair=__magic_name__    ,  framework=__magic_name__      )

       # We need to order the input in the way they appears in the forward()
       lowerCamelCase		:	int							=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
        else:
         import torch

         lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         lowerCamelCase		:	Optional[int]							=      seqlen + 2
         lowerCamelCase		:	List[Any]							=      (
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         lowerCamelCase		:	str							=      [
             (torch.zeros(__magic_name__      ), torch.zeros(__magic_name__      )) for _ in range(self.num_layers      )
         ]

       lowerCamelCase		:	Tuple							=      common_inputs["""attention_mask"""]
       if self.use_past:
        lowerCamelCase		:	str							=      ordered_inputs["""attention_mask"""].dtype
        lowerCamelCase		:	Any							=      torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(__magic_name__    ,  __magic_name__    ,  dtype=__magic_name__      )]    ,  dim=1      )

       return ordered_inputs






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_3




