


'''simple docstring'''



import argparse

import torch

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


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

_lowercase    :    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    lowerCamelCase__   ( A     :    Dict  ):




						'''simple docstring'''






						if k == "embeddings.weight":
												return "shared.weight"

						for parlai_name, hf_name in PATTERNS:
												UpperCAmelCase         =			k.replace(__A   ,     __A  )

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


def    lowerCamelCase__   ( A     :    Dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			[
						    '''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         =			sd.pop(__A  )
												UpperCAmelCase         =			k.replace('''layernorm_embedding'''   ,     '''layer_norm'''  )
												assert new_k not in sd
												UpperCAmelCase         =			v


_lowercase    :    List[str]			     =  ["""START"""]


@torch.no_grad()
def    lowerCamelCase__   ( A     :    List[str]   ,     A     :    Optional[Any]   ,     A     :    str  ):




						'''simple docstring'''






						UpperCAmelCase         =			torch.load(__A   ,     map_location='''cpu'''  )
						UpperCAmelCase         =			model['''model''']
						UpperCAmelCase         =			BlenderbotConfig.from_json_file(__A  )
						UpperCAmelCase         =			BlenderbotForConditionalGeneration(__A  )
						UpperCAmelCase         =			m.model.state_dict().keys()
						UpperCAmelCase         =			[]
						UpperCAmelCase         =			{}
						for k, v in sd.items():
												if k in IGNORE_KEYS:
																		continue

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


if __name__ == "__main__":
				_lowercase    :    Tuple			     =  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"""
				)
				_lowercase    :    Tuple			     =  parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)









'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Tuple							= ["image_processor", "tokenizer"]
				__magic_name__       :   Any							= "ViTImageProcessor"
				__magic_name__       :   str							= ("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				List[Any]=None   ,					lowerCAmelCase    :				List[str]=None   ,					**lowerCAmelCase    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''   ,					lowerCAmelCase   ,					)
																UpperCAmelCase         =			kwargs.pop('''feature_extractor'''   )

										UpperCAmelCase         =			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																raise ValueError('''You need to specify an `image_processor`.'''   )
										if tokenizer is None:
																raise ValueError('''You need to specify a `tokenizer`.'''   )

										super().__init__(lowerCAmelCase   ,					lowerCAmelCase   )




				def __call__(	self    :				str   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=None   ,					**lowerCAmelCase    :				List[str]   )->  List[Any]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''   )

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

										if text is not None:
																UpperCAmelCase         =			self.tokenizer(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if images is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None and images is not None:
																UpperCAmelCase         =			{
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase         =			image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase         =			{
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase   )   ,					tensor_type=lowerCAmelCase   )




				def   a__(	self    :				List[str]   ,					*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				def   a__(	self    :				Dict   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				@property
				def   a__(	self    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor_class





				@property
				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor





