


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
	from ... import FeatureExtractionMixin, TensorType

A				:       Dict      =  logging.get_logger(__name__)

A				:       str      =  {
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}






class      UpperCamelCase(	__lowercase ):
					snake_case_		:					Dict    					=						'''imagegpt'''
					snake_case_		:					Optional[int]    					=						['''past_key_values''']
					snake_case_		:					Union[str, Any]    					=						{
					    '''hidden_size''': '''n_embd''',
					    '''max_position_embeddings''': '''n_positions''',
					    '''num_attention_heads''': '''n_head''',
					    '''num_hidden_layers''': '''n_layer''',
					}


					def __init__(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		str=5_1_2 + 1  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=3_2 * 3_2  ,     SCREAMING_SNAKE_CASE						:		List[Any]=5_1_2  ,     SCREAMING_SNAKE_CASE						:		Any=2_4  ,     SCREAMING_SNAKE_CASE						:		List[str]=8  ,     SCREAMING_SNAKE_CASE						:		int=None  ,     SCREAMING_SNAKE_CASE						:		Tuple="quick_gelu"  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=0.1  ,     SCREAMING_SNAKE_CASE						:		List[str]=0.1  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=0.1  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=1e-5  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=0.02  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=True  ,     SCREAMING_SNAKE_CASE						:		Dict=True  ,     SCREAMING_SNAKE_CASE						:		Any=False  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=False  ,     SCREAMING_SNAKE_CASE						:		Dict=False  ,     **SCREAMING_SNAKE_CASE						:		Dict  ,     )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		vocab_size
											__snake_case				=		n_positions
											__snake_case				=		n_embd
											__snake_case				=		n_layer
											__snake_case				=		n_head
											__snake_case				=		n_inner
											__snake_case				=		activation_function
											__snake_case				=		resid_pdrop
											__snake_case				=		embd_pdrop
											__snake_case				=		attn_pdrop
											__snake_case				=		layer_norm_epsilon
											__snake_case				=		initializer_range
											__snake_case				=		scale_attn_weights
											__snake_case				=		use_cache
											__snake_case				=		scale_attn_by_inverse_layer_idx
											__snake_case				=		reorder_and_upcast_attn
											__snake_case				=		tie_word_embeddings

											super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE  ,     **SCREAMING_SNAKE_CASE    )






class      UpperCamelCase(	__lowercase ):


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											return OrderedDict(
											    [
											        ("input_ids", {0: "batch", 1: "sequence"}),
											    ]    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		"FeatureExtractionMixin"  ,     SCREAMING_SNAKE_CASE						:		int = 1  ,     SCREAMING_SNAKE_CASE						:		int = -1  ,     SCREAMING_SNAKE_CASE						:		bool = False  ,     SCREAMING_SNAKE_CASE						:		Optional["TensorType"] = None  ,     SCREAMING_SNAKE_CASE						:		int = 3  ,     SCREAMING_SNAKE_CASE						:		int = 3_2  ,     SCREAMING_SNAKE_CASE						:		int = 3_2  ,     )      ->						Mapping[str, Any]:




											'''simple docstring'''

											__snake_case				=		self._generate_dummy_images(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		dict(preprocessor(images=SCREAMING_SNAKE_CASE  ,     return_tensors=SCREAMING_SNAKE_CASE    )    )

											return inputs












'''simple docstring'''






import argparse

import torch

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


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

__UpperCamelCase					    =	[
    ["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 					_a						(       _lowerCamelCase ) ->				Union[str, Any]:



			"""simple docstring"""


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

			for parlai_name, hf_name in PATTERNS:
						__snake_case					:      List[str]							      =     k.replace(_lowerCamelCase    ,	_lowerCamelCase )

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







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Optional[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:
						__snake_case					:      Optional[Any]							      =     sd.pop(_lowerCamelCase )
						__snake_case					:      List[str]							      =     k.replace("""layernorm_embedding"""    ,	"""layer_norm""" )
						assert new_k not in sd
						__snake_case					:      Union[str, Any]							      =     v


__UpperCamelCase					    =	["START"]







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     torch.load(_lowerCamelCase    ,	map_location="""cpu""" )
			__snake_case					:      Dict							      =     model["""model"""]
			__snake_case					:      Optional[int]							      =     BlenderbotConfig.from_json_file(_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     BlenderbotForConditionalGeneration(_lowerCamelCase )
			__snake_case					:      List[Any]							      =     m.model.state_dict().keys()
			__snake_case					:      int							      =     []
			__snake_case					:      Union[str, Any]							      =     {}
			for k, v in sd.items():
						if k in IGNORE_KEYS:
									continue

						__snake_case					:      Optional[int]							      =     rename_state_dict_key(_lowerCamelCase )
						if new_k not in valid_keys:
									failures.append([k, new_k] )
						else:
									__snake_case					:      str							      =     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__":
	__UpperCamelCase					    =	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"
	)
	__UpperCamelCase					    =	parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


