

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






						"""simple docstring"""




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







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.vocab_size		)
										_UpperCAmelCase    :						Any							=      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    :						Any							=      input_ids.clamp(self.pad_token_id + 1		)
										_UpperCAmelCase    :						str							=      decoder_input_ids.clamp(self.pad_token_id + 1		)

										_UpperCAmelCase    :						int							=      self.get_config()
										_UpperCAmelCase    :						Union[str, Any]							=      prepare_mam_aaa_inputs_dict(snake_case_  ,   snake_case_  ,   snake_case_		)
										return config, inputs_dict


						def     _UpperCAmelCase			(    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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.prepare_config_and_inputs()
										return config, inputs_dict


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      MaMaaaModel(config=snake_case_		).get_decoder().to(snake_case_		).eval()
										_UpperCAmelCase    :						Optional[int]							=      inputs_dict["""input_ids"""]
										_UpperCAmelCase    :						Tuple							=      inputs_dict["""attention_mask"""]
										_UpperCAmelCase    :						Optional[Any]							=      inputs_dict["""head_mask"""]

										# first forward pass
										_UpperCAmelCase    :						Union[str, Any]							=      model(snake_case_  ,   attention_mask=snake_case_  ,   head_mask=snake_case_  ,   use_cache=snake_case_		)

										_UpperCAmelCase    :						Optional[Any]							=      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    :						Any							=      ids_tensor((self.batch_size, 3)  ,   2		)

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

										_UpperCAmelCase    :						int							=      model(snake_case_  ,   attention_mask=snake_case_		)["""last_hidden_state"""]
										_UpperCAmelCase    :						List[Any]							=      model(snake_case_  ,   attention_mask=snake_case_  ,   past_key_values=snake_case_		)[
										    """last_hidden_state"""
										]

										# select random slice
										_UpperCAmelCase    :						str							=      ids_tensor((1,)  ,   output_from_past.shape[-1]		).item()
										_UpperCAmelCase    :						Tuple							=      output_from_no_past[:, -3:, random_slice_idx].detach()
										_UpperCAmelCase    :						List[Any]							=      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  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      MaMaaaModel(config=snake_case_		).to(snake_case_		).eval()
										_UpperCAmelCase    :						Any							=      model(**snake_case_		)

										_UpperCAmelCase    :						Union[str, Any]							=      outputs.encoder_last_hidden_state
										_UpperCAmelCase    :						Optional[int]							=      outputs.last_hidden_state

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

										_UpperCAmelCase    :						Optional[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:
														_UpperCAmelCase    :						Union[str, Any]							=      model.get_decoder()
														decoder.save_pretrained(snake_case_		)
														_UpperCAmelCase    :						Optional[Any]							=      MaMaaaDecoder.from_pretrained(snake_case_		).to(snake_case_		)

										_UpperCAmelCase    :						str							=      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					(		_a							,	_a							,	_a							,	unittest.TestCase			):






						"""simple docstring"""




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


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''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		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      MaMaaaModelTester(self		)
										_UpperCAmelCase    :						Any							=      ConfigTester(self  ,   config_class=snake_case_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
														_UpperCAmelCase    :						int							=      model_class(snake_case_		)

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case_		)


						def     _UpperCAmelCase			(    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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
														_UpperCAmelCase    :						Union[str, Any]							=      model_class(snake_case_		)
														model.to(snake_case_		)
														model.eval()

														_UpperCAmelCase    :						int							=      copy.deepcopy(self._prepare_for_class(snake_case_  ,   snake_case_		)		)

														if not self.is_encoder_decoder:
																		_UpperCAmelCase    :						Any							=      inputs["""input_ids"""]
																		del inputs["input_ids"]
														else:
																		_UpperCAmelCase    :						Optional[Any]							=      inputs["""input_ids"""]
																		_UpperCAmelCase    :						int							=      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    :						str							=      wte(snake_case_		)
														else:
																		_UpperCAmelCase    :						List[Any]							=      wte(snake_case_		)
																		_UpperCAmelCase    :						List[Any]							=      wte(snake_case_		)

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





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.model_tester.prepare_config_and_inputs()
										_UpperCAmelCase    :						str							=      input_dict["""input_ids"""]
										_UpperCAmelCase    :						Union[str, Any]							=      input_ids.ne(1		).to(snake_case_		)
										_UpperCAmelCase    :						List[str]							=      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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    Optional[Any]:
				return torch.tensor(_lowerCAmelCase					, dtype=torch.long					, device=_lowerCAmelCase	)


SCREAMING_SNAKE_CASE_            = 1e-4





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






						"""simple docstring"""




						@cached_property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      MaMaaaModel.from_pretrained("facebook/m2m100_418M"		).to(snake_case_		)
										_UpperCAmelCase    :						List[Any]							=      _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]		)
										_UpperCAmelCase    :						Tuple							=      _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]		)
										_UpperCAmelCase    :						Optional[int]							=      prepare_mam_aaa_inputs_dict(model.config  ,   snake_case_  ,   snake_case_		)
										with torch.no_grad():
														_UpperCAmelCase    :						str							=      model(**snake_case_		)[0]
										_UpperCAmelCase    :						List[Any]							=      torch.Size((1, 11, 1024)		)
										self.assertEqual(output.shape  ,   snake_case_		)
										# change to expected output here
										_UpperCAmelCase    :						Tuple							=      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=snake_case_		)
										self.assertTrue(torch.allclose(output[:, :3, :3]  ,   snake_case_  ,   atol=snake_case_		)		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"		).to(snake_case_		)

										# change to intended input
										_UpperCAmelCase    :						Optional[int]							=      _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]		)
										_UpperCAmelCase    :						Optional[int]							=      _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]		)
										_UpperCAmelCase    :						Any							=      prepare_mam_aaa_inputs_dict(model.config  ,   snake_case_  ,   snake_case_		)
										with torch.no_grad():
														_UpperCAmelCase    :						Optional[Any]							=      model(**snake_case_		)[0]
										_UpperCAmelCase    :						Dict							=      torch.Size((1, 11, model.config.vocab_size)		)
										self.assertEqual(output.shape  ,   snake_case_		)
										# change to expected output here
										_UpperCAmelCase    :						Union[str, Any]							=      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=snake_case_		)
										self.assertTrue(torch.allclose(output[:, :3, :3]  ,   snake_case_  ,   atol=snake_case_		)		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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

										_UpperCAmelCase    :						List[str]							=      [
										    """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    :						Union[str, Any]							=      tokenizer(snake_case_  ,   padding=snake_case_  ,   return_tensors="pt"		)

										_UpperCAmelCase    :						int							=      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    :						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.""",
										]

										_UpperCAmelCase    :						List[Any]							=      tokenizer.batch_decode(
										    hypotheses_batch.tolist()  ,   clean_up_tokenization_spaces=snake_case_  ,   skip_special_tokens=snake_case_		)
										assert generated == expected_en





import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = [
    ['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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_UpperCAmelCase    :						Optional[int]							=      k.replace(lowerCAmelCase					, lowerCAmelCase	)

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      [
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								_UpperCAmelCase    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								_UpperCAmelCase    :						Tuple							=      rename_state_dict_key(lowerCAmelCase	)
								if new_k not in valid_keys:
												failures.append([k, new_k]	)
								else:
												_UpperCAmelCase    :						Tuple							=      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__":
					SCREAMING_SNAKE_CASE_            = 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'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


