'''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 snake_case_      (					__snake_case  :							int	,   __snake_case  :							str	,   __snake_case  :							Any	,   __snake_case  :							List[Any]=None	,   __snake_case  :							Any=None	,   __snake_case  :							Any=None	,   __snake_case  :							Union[str, Any]=None	,   __snake_case  :							List[Any]=None	,   )			->					List[Any]:
   if attention_mask is None:
      lowerCAmelCase_			=      input_ids.ne(config.pad_token_id)
   if decoder_attention_mask is None:
      lowerCAmelCase_			=      decoder_input_ids.ne(config.pad_token_id)
   if head_mask is None:
      lowerCAmelCase_			=      torch.ones(config.encoder_layers	,   config.encoder_attention_heads	,   device=__snake_case)
   if decoder_head_mask is None:
      lowerCAmelCase_			=      torch.ones(config.decoder_layers	,   config.decoder_attention_heads	,   device=__snake_case)
   if cross_attn_head_mask is None:
      lowerCAmelCase_			=      torch.ones(config.decoder_layers	,   config.decoder_attention_heads	,   device=__snake_case)
   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    ,     _lowerCamelCase    ,     _lowerCamelCase=13    ,     _lowerCamelCase=7    ,     _lowerCamelCase=True    ,     _lowerCamelCase=False    ,     _lowerCamelCase=99    ,     _lowerCamelCase=16    ,     _lowerCamelCase=2    ,     _lowerCamelCase=4    ,     _lowerCamelCase=4    ,     _lowerCamelCase="relu"    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=0.0    ,     _lowerCamelCase=0.0    ,     _lowerCamelCase=20    ,     _lowerCamelCase=2    ,     _lowerCamelCase=1    ,     _lowerCamelCase=0    ,     ):
     lowerCAmelCase_			=      parent
     lowerCAmelCase_			=      batch_size
     lowerCAmelCase_			=      seq_length
     lowerCAmelCase_			=      is_training
     lowerCAmelCase_			=      use_labels
     lowerCAmelCase_			=      vocab_size
     lowerCAmelCase_			=      hidden_size
     lowerCAmelCase_			=      num_hidden_layers
     lowerCAmelCase_			=      num_attention_heads
     lowerCAmelCase_			=      intermediate_size
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      hidden_dropout_prob
     lowerCAmelCase_			=      attention_probs_dropout_prob
     lowerCAmelCase_			=      encoder_layerdrop
     lowerCAmelCase_			=      decoder_layerdrop
     lowerCAmelCase_			=      max_position_embeddings
     lowerCAmelCase_			=      eos_token_id
     lowerCAmelCase_			=      pad_token_id
     lowerCAmelCase_			=      bos_token_id





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size )
     lowerCAmelCase_			=      self.eos_token_id  # Eos Token
     lowerCAmelCase_			=      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_			=      input_ids.clamp(self.pad_token_id + 1 )
     lowerCAmelCase_			=      decoder_input_ids.clamp(self.pad_token_id + 1 )

     lowerCAmelCase_			=      self.get_config()
     lowerCAmelCase_			=      prepare_mam_aaa_inputs_dict(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )
     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_			=      self.prepare_config_and_inputs()
     return config, inputs_dict





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      MaMaaaModel(config=_lowerCamelCase ).get_decoder().to(_lowerCamelCase ).eval()
     lowerCAmelCase_			=      inputs_dict['''input_ids''']
     lowerCAmelCase_			=      inputs_dict['''attention_mask''']
     lowerCAmelCase_			=      inputs_dict['''head_mask''']

     # first forward pass
     lowerCAmelCase_			=      model(_lowerCamelCase    ,     attention_mask=_lowerCamelCase    ,     head_mask=_lowerCamelCase    ,     use_cache=_lowerCamelCase )

     lowerCAmelCase_	,lowerCAmelCase_			=      outputs.to_tuple()

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

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

     lowerCAmelCase_			=      model(_lowerCamelCase    ,     attention_mask=_lowerCamelCase )['''last_hidden_state''']
     lowerCAmelCase_			=      model(_lowerCamelCase    ,     attention_mask=_lowerCamelCase    ,     past_key_values=_lowerCamelCase )[
         '''last_hidden_state'''
     ]

     # select random slice
     lowerCAmelCase_			=      ids_tensor((1,)    ,     output_from_past.shape[-1] ).item()
     lowerCAmelCase_			=      output_from_no_past[:, -3:, random_slice_idx].detach()
     lowerCAmelCase_			=      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(_lowerCamelCase    ,     _lowerCamelCase    ,     atol=1E-2 ) )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      MaMaaaModel(config=_lowerCamelCase ).to(_lowerCamelCase ).eval()
     lowerCAmelCase_			=      model(**_lowerCamelCase )

     lowerCAmelCase_			=      outputs.encoder_last_hidden_state
     lowerCAmelCase_			=      outputs.last_hidden_state

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

     lowerCAmelCase_			=      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_			=      model.get_decoder()
        decoder.save_pretrained(_lowerCamelCase )
        lowerCAmelCase_			=      MaMaaaDecoder.from_pretrained(_lowerCamelCase ).to(_lowerCamelCase )

     lowerCAmelCase_			=      decoder(
         input_ids=inputs_dict['''decoder_input_ids''']    ,     attention_mask=inputs_dict['''decoder_attention_mask''']    ,     encoder_hidden_states=_lowerCamelCase    ,     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       ( __a						,       __a						,       __a						,       unittest.TestCase		):
  __A :				List[Any]											= (
      (
          MaMaaaModel,
          MaMaaaForConditionalGeneration,
      )
      if is_torch_available()
      else ()
  )
  __A :				str											= (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
  __A :				Tuple											= (
      {
          'conversational': MaMaaaForConditionalGeneration,
          'feature-extraction': MaMaaaModel,
          'summarization': MaMaaaForConditionalGeneration,
          'text2text-generation': MaMaaaForConditionalGeneration,
          'translation': MaMaaaForConditionalGeneration,
      }
      if is_torch_available()
      else {}
  )
  __A :				Any											= True
  __A :				str											= True
  __A :				List[str]											= False
  __A :				str											= False





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     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_			=      MaMaaaModelTester(self )
     lowerCAmelCase_			=      ConfigTester(self    ,     config_class=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     self.config_tester.run_common_tests()





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
        lowerCAmelCase_			=      model_class(_lowerCamelCase )

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





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(*_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_encoder_decoder_model_standalone(*_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
        lowerCAmelCase_			=      model_class(_lowerCamelCase )
        model.to(_lowerCamelCase )
        model.eval()

        lowerCAmelCase_			=      copy.deepcopy(self._prepare_for_class(_lowerCamelCase    ,     _lowerCamelCase ) )

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

        lowerCAmelCase_			=      model.get_input_embeddings()
        if not self.is_encoder_decoder:
           lowerCAmelCase_			=      wte(_lowerCamelCase )
        else:
           lowerCAmelCase_			=      wte(_lowerCamelCase )
           lowerCAmelCase_			=      wte(_lowerCamelCase )

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







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






def snake_case_      (					__snake_case  :							Dict)			->					str:
   return torch.tensor(__snake_case	,   dtype=torch.long	,   device=__snake_case)


A_     :						Optional[int]    							=1e-4






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





  @cached_property
  def 						UpperCAmelCase_	(	self ):
     return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M''' )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      MaMaaaModel.from_pretrained('''facebook/m2m100_418M''' ).to(_lowerCamelCase )
     lowerCAmelCase_			=      _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]] )
     lowerCAmelCase_			=      _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]] )
     lowerCAmelCase_			=      prepare_mam_aaa_inputs_dict(model.config    ,     _lowerCamelCase    ,     _lowerCamelCase )
     with torch.no_grad():
        lowerCAmelCase_			=      model(**_lowerCamelCase )[0]
     lowerCAmelCase_			=      torch.Size((1, 11, 1024) )
     self.assertEqual(output.shape    ,     _lowerCamelCase )
     # change to expected output here
     lowerCAmelCase_			=      torch.tensor(
         [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]]    ,     device=_lowerCamelCase )
     self.assertTrue(torch.allclose(output[:, :3, :3]    ,     _lowerCamelCase    ,     atol=_lowerCamelCase ) )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M''' ).to(_lowerCamelCase )

     # change to intended input
     lowerCAmelCase_			=      _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]] )
     lowerCAmelCase_			=      _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]] )
     lowerCAmelCase_			=      prepare_mam_aaa_inputs_dict(model.config    ,     _lowerCamelCase    ,     _lowerCamelCase )
     with torch.no_grad():
        lowerCAmelCase_			=      model(**_lowerCamelCase )[0]
     lowerCAmelCase_			=      torch.Size((1, 11, model.config.vocab_size) )
     self.assertEqual(output.shape    ,     _lowerCamelCase )
     # change to expected output here
     lowerCAmelCase_			=      torch.tensor(
         [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]]    ,     device=_lowerCamelCase )
     self.assertTrue(torch.allclose(output[:, :3, :3]    ,     _lowerCamelCase    ,     atol=_lowerCamelCase ) )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M''' ).to(_lowerCamelCase )
     lowerCAmelCase_			=      MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''    ,     src_lang='''fr'''    ,     tgt_lang='''en''' )

     lowerCAmelCase_			=      [
         '''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_			=      tokenizer(_lowerCamelCase    ,     padding=_lowerCamelCase    ,     return_tensors='''pt''' )

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

     lowerCAmelCase_			=      [
         '''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_			=      tokenizer.batch_decode(
         hypotheses_batch.tolist()    ,     clean_up_tokenization_spaces=_lowerCamelCase    ,     skip_special_tokens=_lowerCamelCase )
     assert generated == expected_en






'''simple docstring'''

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							Union[str, Any]=False)			->					Optional[int]:
   lowerCAmelCase_			=      OmegaConf.load(__snake_case)
   if display:
      print(yaml.dump(OmegaConf.to_container(__snake_case)))
   return config



def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]=None	,   __snake_case  :							str=None)			->					str:
   if conf_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.yaml'''
   lowerCAmelCase_			=      load_config(__snake_case	,   display=__snake_case)
   lowerCAmelCase_			=      VQModel(**config.model.params)
   if ckpt_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.pt'''
   lowerCAmelCase_			=      torch.load(__snake_case	,   map_location=__snake_case)
   if ".ckpt" in ckpt_path:
      lowerCAmelCase_			=      sd['''state_dict''']
   model.load_state_dict(__snake_case	,   strict=__snake_case)
   model.to(__snake_case)
   del sd
   return model



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							str)			->					Optional[int]:
   lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      model.encode(__snake_case)
   print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''')
   lowerCAmelCase_			=      model.decode(__snake_case)
   return xrec



def snake_case_      (					__snake_case  :							int	,   __snake_case  :							List[Any]=False)			->					Union[str, Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      string.rsplit('''.'''	,   1)
   if reload:
      lowerCAmelCase_			=      importlib.import_module(__snake_case)
      importlib.reload(__snake_case)
   return getattr(importlib.import_module(__snake_case	,   package=__snake_case)	,   cls)



def snake_case_      (					__snake_case  :							Tuple)			->					Union[str, Any]:
   if "target" not in config:
      raise KeyError('''Expected key `target` to instantiate.''')
   return get_obj_from_str(config['''target'''])(**config.get('''params'''	,   {}))



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Any	,   __snake_case  :							Optional[int]=True	,   __snake_case  :							Any=True)			->					List[str]:
   lowerCAmelCase_			=      instantiate_from_config(__snake_case)
   if sd is not None:
      model.load_state_dict(__snake_case)
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}






def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]	,   __snake_case  :							Dict	,   __snake_case  :							str)			->					Any:
   # load the specified checkpoint
   if ckpt:
      lowerCAmelCase_			=      torch.load(__snake_case	,   map_location='''cpu''')
      lowerCAmelCase_			=      pl_sd['''global_step''']
      print(F'''loaded model from global step {global_step}.''')
   else:
      lowerCAmelCase_			=      {'''state_dict''': None}
      lowerCAmelCase_			=      None
   lowerCAmelCase_			=      load_model_from_config(config.model	,   pl_sd['''state_dict''']	,   gpu=__snake_case	,   eval_mode=__snake_case)['''model''']
   return model, global_step





