'''simple docstring'''






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  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				List[str]   ,    _lowercase    :				Tuple   ,    _lowercase    :				Any=None   ,    _lowercase    :				List[str]=None   ,    _lowercase    :				int=None   ,    _lowercase    :				str=None   ,    _lowercase    :				Any=None   ,    )							->	int:
  if attention_mask is None:
    __UpperCAmelCase:					List[str]		= input_ids.ne(config.pad_token_id     )
  if decoder_attention_mask is None:
    __UpperCAmelCase:					Optional[int]		= decoder_input_ids.ne(config.pad_token_id     )
  if head_mask is None:
    __UpperCAmelCase:					Optional[int]		= torch.ones(config.encoder_layers   ,    config.encoder_attention_heads   ,    device=_lowercase     )
  if decoder_head_mask is None:
    __UpperCAmelCase:					List[str]		= torch.ones(config.decoder_layers   ,    config.decoder_attention_heads   ,    device=_lowercase     )
  if cross_attn_head_mask is None:
    __UpperCAmelCase:					int		= torch.ones(config.decoder_layers   ,    config.decoder_attention_heads   ,    device=_lowercase     )
  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 :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=13   ,    snake_case_=7   ,    snake_case_=True   ,    snake_case_=False   ,    snake_case_=99   ,    snake_case_=16   ,    snake_case_=2   ,    snake_case_=4   ,    snake_case_=4   ,    snake_case_="relu"   ,    snake_case_=0.1   ,    snake_case_=0.1   ,    snake_case_=0.0   ,    snake_case_=0.0   ,    snake_case_=20   ,    snake_case_=2   ,    snake_case_=1   ,    snake_case_=0   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= parent
    __UpperCAmelCase:					List[str]		= batch_size
    __UpperCAmelCase:					str		= seq_length
    __UpperCAmelCase:					Union[str, Any]		= is_training
    __UpperCAmelCase:					Dict		= use_labels
    __UpperCAmelCase:					Any		= vocab_size
    __UpperCAmelCase:					Tuple		= hidden_size
    __UpperCAmelCase:					Optional[int]		= num_hidden_layers
    __UpperCAmelCase:					Optional[Any]		= num_attention_heads
    __UpperCAmelCase:					List[Any]		= intermediate_size
    __UpperCAmelCase:					List[str]		= hidden_act
    __UpperCAmelCase:					List[str]		= hidden_dropout_prob
    __UpperCAmelCase:					List[Any]		= attention_probs_dropout_prob
    __UpperCAmelCase:					Optional[Any]		= encoder_layerdrop
    __UpperCAmelCase:					List[str]		= decoder_layerdrop
    __UpperCAmelCase:					Any		= max_position_embeddings
    __UpperCAmelCase:					Optional[Any]		= eos_token_id
    __UpperCAmelCase:					List[str]		= pad_token_id
    __UpperCAmelCase:					Union[str, Any]		= bos_token_id







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size					)
    __UpperCAmelCase:					Dict		= self.eos_token_id  # Eos Token
    __UpperCAmelCase:					List[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
    __UpperCAmelCase:					Tuple		= input_ids.clamp(self.pad_token_id + 1					)
    __UpperCAmelCase:					Tuple		= decoder_input_ids.clamp(self.pad_token_id + 1					)

    __UpperCAmelCase:					Tuple		= self.get_config()
    __UpperCAmelCase:					str		= prepare_mam_aaa_inputs_dict(snake_case_   ,    snake_case_   ,    snake_case_					)
    return config, inputs_dict







  def    lowercase_  (							self					):

    '''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    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					Tuple		= self.prepare_config_and_inputs()
    return config, inputs_dict







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= MaMaaaModel(config=snake_case_					).get_decoder().to(snake_case_					).eval()
    __UpperCAmelCase:					List[Any]		= inputs_dict["""input_ids"""]
    __UpperCAmelCase:					str		= inputs_dict["""attention_mask"""]
    __UpperCAmelCase:					List[Any]		= inputs_dict["""head_mask"""]

    # first forward pass
    __UpperCAmelCase:					int		= model(snake_case_   ,    attention_mask=snake_case_   ,    head_mask=snake_case_   ,    use_cache=snake_case_					)

    __UpperCAmelCase, __UpperCAmelCase:					str		= outputs.to_tuple()

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

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

    __UpperCAmelCase:					Optional[Any]		= model(snake_case_   ,    attention_mask=snake_case_					)["""last_hidden_state"""]
    __UpperCAmelCase:					Any		= model(snake_case_   ,    attention_mask=snake_case_   ,    past_key_values=snake_case_					)[
        """last_hidden_state"""
    ]

    # select random slice
    __UpperCAmelCase:					Any		= ids_tensor((1,)   ,    output_from_past.shape[-1]					).item()
    __UpperCAmelCase:					Dict		= output_from_no_past[:, -3:, random_slice_idx].detach()
    __UpperCAmelCase:					Optional[int]		= 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    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= MaMaaaModel(config=snake_case_					).to(snake_case_					).eval()
    __UpperCAmelCase:					int		= model(**snake_case_					)

    __UpperCAmelCase:					List[str]		= outputs.encoder_last_hidden_state
    __UpperCAmelCase:					List[Any]		= outputs.last_hidden_state

    with tempfile.TemporaryDirectory() as tmpdirname:
      __UpperCAmelCase:					List[str]		= model.get_encoder()
      encoder.save_pretrained(snake_case_					)
      __UpperCAmelCase:					str		= MaMaaaEncoder.from_pretrained(snake_case_					).to(snake_case_					)

    __UpperCAmelCase:					Tuple		= 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:
      __UpperCAmelCase:					Union[str, Any]		= model.get_decoder()
      decoder.save_pretrained(snake_case_					)
      __UpperCAmelCase:					List[str]		= MaMaaaDecoder.from_pretrained(snake_case_					).to(snake_case_					)

    __UpperCAmelCase:					Optional[Any]		= 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    a (       __lowerCAmelCase						,  __lowerCAmelCase						,  __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					(
      (
          MaMaaaModel,
          MaMaaaForConditionalGeneration,
      )
      if is_torch_available()
      else ()
  )
  __lowerCAmelCase				      =					(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
  __lowerCAmelCase				      =					(
      {
          """conversational""": MaMaaaForConditionalGeneration,
          """feature-extraction""": MaMaaaModel,
          """summarization""": MaMaaaForConditionalGeneration,
          """text2text-generation""": MaMaaaForConditionalGeneration,
          """translation""": MaMaaaForConditionalGeneration,
      }
      if is_torch_available()
      else {}
  )
  __lowerCAmelCase				      =					True
  __lowerCAmelCase				      =					True
  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					False







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''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    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= MaMaaaModelTester(self					)
    __UpperCAmelCase:					List[Any]		= ConfigTester(self   ,    config_class=snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    self.config_tester.run_common_tests()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					str		= self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
      __UpperCAmelCase:					Optional[Any]		= model_class(snake_case_					)

      with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(snake_case_					)
        __UpperCAmelCase, __UpperCAmelCase:					int		= model_class.from_pretrained(snake_case_   ,    output_loading_info=snake_case_					)
      self.assertEqual(info["""missing_keys"""]   ,    []					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_encoder_decoder_model_standalone(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					Any		= self.model_tester.prepare_config_and_inputs_for_common()

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

      __UpperCAmelCase:					Any		= copy.deepcopy(self._prepare_for_class(snake_case_   ,    snake_case_					)					)

      if not self.is_encoder_decoder:
        __UpperCAmelCase:					List[str]		= inputs["""input_ids"""]
        del inputs["input_ids"]
      else:
        __UpperCAmelCase:					List[Any]		= inputs["""input_ids"""]
        __UpperCAmelCase:					Union[str, Any]		= inputs.get("""decoder_input_ids"""   ,    snake_case_					)
        del inputs["input_ids"]
        inputs.pop("""decoder_input_ids"""   ,    snake_case_					)

      __UpperCAmelCase:					Optional[Any]		= model.get_input_embeddings()
      if not self.is_encoder_decoder:
        __UpperCAmelCase:					List[str]		= wte(snake_case_					)
      else:
        __UpperCAmelCase:					Tuple		= wte(snake_case_					)
        __UpperCAmelCase:					Optional[Any]		= wte(snake_case_					)

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







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					List[str]		= self.model_tester.prepare_config_and_inputs()
    __UpperCAmelCase:					Tuple		= input_dict["""input_ids"""]
    __UpperCAmelCase:					Union[str, Any]		= input_ids.ne(1					).to(snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= 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  UpperCamelCase__					(					_lowercase    :				Tuple     )							->	str:
  return torch.tensor(_lowercase   ,    dtype=torch.long   ,    device=_lowercase     )


SCREAMING_SNAKE_CASE_	   =			1E-4







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



  """simple docstring"""







  @cached_property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""					).to(snake_case_					)
    __UpperCAmelCase:					Dict		= _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]					)
    __UpperCAmelCase:					int		= _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]					)
    __UpperCAmelCase:					Optional[int]		= prepare_mam_aaa_inputs_dict(model.config   ,    snake_case_   ,    snake_case_					)
    with torch.no_grad():
      __UpperCAmelCase:					Optional[int]		= model(**snake_case_					)[0]
    __UpperCAmelCase:					Optional[int]		= torch.Size((1, 11, 1024)					)
    self.assertEqual(output.shape   ,    snake_case_					)
    # change to expected output here
    __UpperCAmelCase:					List[Any]		= torch.tensor(
        [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]   ,    device=snake_case_					)
    self.assertTrue(torch.allclose(output[:, :3, :3]   ,    snake_case_   ,    atol=snake_case_					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""					).to(snake_case_					)

    # change to intended input
    __UpperCAmelCase:					str		= _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]					)
    __UpperCAmelCase:					Dict		= _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]					)
    __UpperCAmelCase:					int		= prepare_mam_aaa_inputs_dict(model.config   ,    snake_case_   ,    snake_case_					)
    with torch.no_grad():
      __UpperCAmelCase:					Dict		= model(**snake_case_					)[0]
    __UpperCAmelCase:					List[Any]		= torch.Size((1, 11, model.config.vocab_size)					)
    self.assertEqual(output.shape   ,    snake_case_					)
    # change to expected output here
    __UpperCAmelCase:					Any		= torch.tensor(
        [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]   ,    device=snake_case_					)
    self.assertTrue(torch.allclose(output[:, :3, :3]   ,    snake_case_   ,    atol=snake_case_					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""					).to(snake_case_					)
    __UpperCAmelCase:					List[str]		= MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""   ,    src_lang="""fr"""   ,    tgt_lang="""en"""					)

    __UpperCAmelCase:					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
    __UpperCAmelCase:					Any		= tokenizer(snake_case_   ,    padding=snake_case_   ,    return_tensors="""pt"""					)

    __UpperCAmelCase:					List[str]		= 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"""					)   ,    )

    __UpperCAmelCase:					List[str]		= [
        """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.""",
    ]

    __UpperCAmelCase:					int		= tokenizer.batch_decode(
        hypotheses_batch.tolist()   ,    clean_up_tokenization_spaces=snake_case_   ,    skip_special_tokens=snake_case_					)
    assert generated == expected_en
'''simple docstring'''






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


SCREAMING_SNAKE_CASE_	   =			['small', 'medium', 'large']

SCREAMING_SNAKE_CASE_	   =			'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_	   =			'lm_head.weight'






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str     )							->	List[str]:
  __UpperCAmelCase:					Tuple		= torch.load(_lowercase     )
  __UpperCAmelCase:					Tuple		= d.pop(_lowercase     )
  os.makedirs(_lowercase   ,    exist_ok=_lowercase     )
  torch.save(_lowercase   ,    os.path.join(_lowercase   ,    _lowercase     )     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    parser.add_argument('--dialogpt_path', default='.', type=str)
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        SCREAMING_SNAKE_CASE_	   =			os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
        SCREAMING_SNAKE_CASE_	   =			F"""./DialoGPT-{MODEL}"""
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )