





'''simple docstring'''

import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
  )
  from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST






class        UpperCAmelCase     (	lowerCAmelCase__    ):
 def __init__(    self				:Union[str, Any]						,	lowercase_				:str						,	lowercase_				:Union[str, Any]=13						,	lowercase_				:Tuple=7						,	lowercase_				:int=True						,	lowercase_				:List[Any]=True						,	lowercase_				:str=True						,	lowercase_				:List[str]=True						,	lowercase_				:str=99						,	lowercase_				:List[Any]=32						,	lowercase_				:int=5						,	lowercase_				:str=4						,	lowercase_				:Tuple=37						,	lowercase_				:Optional[Any]="gelu"						,	lowercase_				:str=0.1						,	lowercase_				:int=0.1						,	lowercase_				:Tuple=5_12						,	lowercase_				:Tuple=16						,	lowercase_				:List[str]=2						,	lowercase_				:Union[str, Any]=0.0_2						,	lowercase_				:Any=False						,	lowercase_				:Union[str, Any]=True						,	lowercase_				:Tuple="None"						,	lowercase_				:str=3						,	lowercase_				:Union[str, Any]=4						,	lowercase_				:str=None						,	)->							Optional[Any]:
     A__    =      parent
     A__    =      batch_size
     A__    =      seq_length
     A__    =      is_training
     A__    =      use_input_mask
     A__    =      use_token_type_ids
     A__    =      use_labels
     A__    =      vocab_size
     A__    =      hidden_size
     A__    =      num_hidden_layers
     A__    =      num_attention_heads
     A__    =      intermediate_size
     A__    =      hidden_act
     A__    =      hidden_dropout_prob
     A__    =      attention_probs_dropout_prob
     A__    =      max_position_embeddings
     A__    =      type_vocab_size
     A__    =      type_sequence_label_size
     A__    =      initializer_range
     A__    =      num_labels
     A__    =      num_choices
     A__    =      relative_attention
     A__    =      position_biased_input
     A__    =      pos_att_type
     A__    =      scope
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Optional[Any]:
     A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size							)

     A__    =      None
     if self.use_input_mask:
         A__    =      ids_tensor([self.batch_size, self.seq_length]						,	vocab_size=2							)

     A__    =      None
     if self.use_token_type_ids:
         A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.type_vocab_size							)

     A__    =      None
     A__    =      None
     A__    =      None
     if self.use_labels:
         A__    =      ids_tensor([self.batch_size]						,	self.type_sequence_label_size							)
         A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels							)
         A__    =      ids_tensor([self.batch_size]						,	self.num_choices							)

     A__    =      self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Tuple:
     return DebertaConfig(
         vocab_size=self.vocab_size						,	hidden_size=self.hidden_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	type_vocab_size=self.type_vocab_size						,	initializer_range=self.initializer_range						,	relative_attention=self.relative_attention						,	position_biased_input=self.position_biased_input						,	pos_att_type=self.pos_att_type						,	)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							int:
     A__    =      self.get_config()
     A__    =      3_00
     return config
 def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:Any							)->							List[str]:
     self.parent.assertListEqual(list(result.loss.size()							)						,	[]							)
 def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:str						,	lowercase_				:str						,	lowercase_				:Any						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[Any]						,	lowercase_				:Tuple						,	lowercase_				:Optional[int]							)->							Union[str, Any]:
     A__    =      DebertaModel(config=_SCREAMING_SNAKE_CASE							)
     model.to(_SCREAMING_SNAKE_CASE							)
     model.eval()
     A__    =      model(_SCREAMING_SNAKE_CASE						,	attention_mask=_SCREAMING_SNAKE_CASE						,	token_type_ids=_SCREAMING_SNAKE_CASE							)[0]
     A__    =      model(_SCREAMING_SNAKE_CASE						,	token_type_ids=_SCREAMING_SNAKE_CASE							)[0]
     A__    =      model(_SCREAMING_SNAKE_CASE							)[0]

     self.parent.assertListEqual(list(sequence_output.size()							)						,	[self.batch_size, self.seq_length, self.hidden_size]							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[Any]						,	lowercase_				:Dict						,	lowercase_				:Optional[Any]						,	lowercase_				:Any						,	lowercase_				:Tuple						,	lowercase_				:Union[str, Any]							)->							Optional[int]:
     A__    =      DebertaForMaskedLM(config=_SCREAMING_SNAKE_CASE							)
     model.to(_SCREAMING_SNAKE_CASE							)
     model.eval()
     A__    =      model(_SCREAMING_SNAKE_CASE						,	attention_mask=_SCREAMING_SNAKE_CASE						,	token_type_ids=_SCREAMING_SNAKE_CASE						,	labels=_SCREAMING_SNAKE_CASE							)

     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)							)
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:int						,	lowercase_				:Tuple						,	lowercase_				:Tuple						,	lowercase_				:Tuple						,	lowercase_				:Tuple						,	lowercase_				:Optional[int]						,	lowercase_				:int							)->							Optional[int]:
     A__    =      self.num_labels
     A__    =      DebertaForSequenceClassification(_SCREAMING_SNAKE_CASE							)
     model.to(_SCREAMING_SNAKE_CASE							)
     model.eval()
     A__    =      model(_SCREAMING_SNAKE_CASE						,	attention_mask=_SCREAMING_SNAKE_CASE						,	token_type_ids=_SCREAMING_SNAKE_CASE						,	labels=_SCREAMING_SNAKE_CASE							)
     self.parent.assertListEqual(list(result.logits.size()							)						,	[self.batch_size, self.num_labels]							)
     self.check_loss_output(_SCREAMING_SNAKE_CASE							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:int						,	lowercase_				:Tuple						,	lowercase_				:Dict						,	lowercase_				:Optional[Any]						,	lowercase_				:List[Any]						,	lowercase_				:int						,	lowercase_				:Any							)->							List[Any]:
     A__    =      self.num_labels
     A__    =      DebertaForTokenClassification(config=_SCREAMING_SNAKE_CASE							)
     model.to(_SCREAMING_SNAKE_CASE							)
     model.eval()
     A__    =      model(_SCREAMING_SNAKE_CASE						,	attention_mask=_SCREAMING_SNAKE_CASE						,	token_type_ids=_SCREAMING_SNAKE_CASE						,	labels=_SCREAMING_SNAKE_CASE							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)							)
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:List[str]						,	lowercase_				:List[str]						,	lowercase_				:Union[str, Any]						,	lowercase_				:Any						,	lowercase_				:Union[str, Any]						,	lowercase_				:Optional[Any]						,	lowercase_				:Any							)->							str:
     A__    =      DebertaForQuestionAnswering(config=_SCREAMING_SNAKE_CASE							)
     model.to(_SCREAMING_SNAKE_CASE							)
     model.eval()
     A__    =      model(
         _SCREAMING_SNAKE_CASE						,	attention_mask=_SCREAMING_SNAKE_CASE						,	token_type_ids=_SCREAMING_SNAKE_CASE						,	start_positions=_SCREAMING_SNAKE_CASE						,	end_positions=_SCREAMING_SNAKE_CASE						,	)
     self.parent.assertEqual(result.start_logits.shape						,	(self.batch_size, self.seq_length)							)
     self.parent.assertEqual(result.end_logits.shape						,	(self.batch_size, self.seq_length)							)



 def 					UpperCAmelCase_							(    self				:Any							)->							Dict:
     A__    =      self.prepare_config_and_inputs()
     (
         (
         A__ 
     ),     (
         A__ 
     ),     (
         A__ 
     ),     (
         A__ 
     ),     (
         A__ 
     ),     (
         A__ 
     ),     (
         A__ 
     ),      
     )    =      config_and_inputs
     A__    =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_torch
class        UpperCAmelCase     (	lowerCAmelCase__      ,	lowerCAmelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    (
     (
         DebertaModel,
         DebertaForMaskedLM,
         DebertaForSequenceClassification,
         DebertaForTokenClassification,
         DebertaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 __lowercase       		=    (
     {
         "feature-extraction": DebertaModel,
         "fill-mask": DebertaForMaskedLM,
         "question-answering": DebertaForQuestionAnswering,
         "text-classification": DebertaForSequenceClassification,
         "token-classification": DebertaForTokenClassification,
         "zero-shot": DebertaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 __lowercase       		=    True
 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:int							)->							int:
     A__    =      DebertaModelTester(self							)
     A__    =      ConfigTester(self						,	config_class=_SCREAMING_SNAKE_CASE						,	hidden_size=37							)
 def 					UpperCAmelCase_							(    self				:int							)->							str:
     self.config_tester.run_common_tests()
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Any:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_model(*_SCREAMING_SNAKE_CASE							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							int:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_sequence_classification(*_SCREAMING_SNAKE_CASE							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_masked_lm(*_SCREAMING_SNAKE_CASE							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							Any:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_question_answering(*_SCREAMING_SNAKE_CASE							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Dict:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_token_classification(*_SCREAMING_SNAKE_CASE							)



 @slow
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Union[str, Any]:
     for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         A__    =      DebertaModel.from_pretrained(_SCREAMING_SNAKE_CASE							)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE							)






@require_torch
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase     (	unittest.TestCase    ):
 @unittest.skip(reason="Model not available yet"							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
     pass



 @slow
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[int]:
     A__    =      DebertaModel.from_pretrained("microsoft/deberta-base"							)

     A__    =      torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]							)
     A__    =      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
     with torch.no_grad():
         A__    =      model(_SCREAMING_SNAKE_CASE						,	attention_mask=_SCREAMING_SNAKE_CASE							)[0]
     # compare the actual values for a slice.
     A__    =      torch.tensor(
         [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]							)
     self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,	_SCREAMING_SNAKE_CASE						,	atol=1E-4							)						,	F"{output[:, 1:4, 1:4]}"							)






'''simple docstring'''

import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase							:     Union[str, Any]		     =logging.get_logger(__name__)

__lowerCAmelCase							:     Optional[int]		     =[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        A__    =      k.replace(_lowerCamelCase						,	_lowerCamelCase	)

    if k.startswith("encoder"	):
        A__    =      k.replace(".attn"						,	".self_attn"	)
        A__    =      k.replace("norm1"						,	"self_attn_layer_norm"	)
        A__    =      k.replace("norm2"						,	"final_layer_norm"	)
    elif k.startswith("decoder"	):
        A__    =      k.replace("norm1"						,	"self_attn_layer_norm"	)
        A__    =      k.replace("norm2"						,	"encoder_attn_layer_norm"	)
        A__    =      k.replace("norm3"						,	"final_layer_norm"	)
    return k





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    A__    =      [
        "model.encoder.layernorm_embedding.weight",
        "model.encoder.layernorm_embedding.bias",
        "model.decoder.layernorm_embedding.weight",
        "model.decoder.layernorm_embedding.bias",
    ]
    for k in keys:
        A__    =      sd.pop(_lowerCamelCase	)
        A__    =      k.replace("layernorm_embedding"						,	"layer_norm"	)
        assert new_k not in sd
        A__    =      v


__lowerCAmelCase							:     Optional[int]		     =["START"]





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    A__    =      model["model"]
    A__    =      BlenderbotConfig.from_json_file(_lowerCamelCase	)
    A__    =      BlenderbotForConditionalGeneration(_lowerCamelCase	)
    A__    =      m.model.state_dict().keys()
    A__    =      []
    A__    =      {}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        A__    =      rename_state_dict_key(_lowerCamelCase	)
        if new_k not in valid_keys:
            failures.append([k, new_k]	)
        else:
            A__    =      v
    if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
        rename_layernorm_keys(_lowerCamelCase	)
    m.model.load_state_dict(_lowerCamelCase						,	strict=_lowerCamelCase	)
    m.half()
    m.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
  __lowerCAmelCase							:     Union[str, Any]		     =argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
  parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
  parser.add_argument(
      "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
  )
  __lowerCAmelCase							:     Union[str, Any]		     =parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



