




'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


a       :						Tuple								=		logging.get_logger(__name__)



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			["""pixel_values"""]





			def __init__(      self						:    Tuple      ,		a_						:    bool = True      ,		a_						:    Dict[str, int] = None      ,		a_						:    int = 0.9      ,		a_						:    PILImageResampling = PILImageResampling.BICUBIC      ,		a_						:    bool = True      ,		a_						:    Dict[str, int] = None      ,		a_						:    Union[int, float] = 1 / 255      ,		a_						:    bool = True      ,		a_						:    bool = True      ,		a_						:    Optional[Union[float, List[float]]] = None      ,		a_						:    Optional[Union[float, List[float]]] = None      ,		**a_						:    Optional[int]      ,		):



						"""simple docstring"""




						super().__init__(**a_							)
						__snake_case			=		size if size is not None else {"shortest_edge": 224}
						__snake_case			=		get_size_dict(a_      ,		default_to_square=a_							)
						__snake_case			=		crop_size if crop_size is not None else {"height": 224, "width": 224}
						__snake_case			=		get_size_dict(a_      ,		param_name="crop_size"							)

						__snake_case			=		do_resize
						__snake_case			=		size
						__snake_case			=		crop_pct
						__snake_case			=		resample
						__snake_case			=		do_center_crop
						__snake_case			=		crop_size
						__snake_case			=		do_rescale
						__snake_case			=		rescale_factor
						__snake_case			=		do_normalize
						__snake_case			=		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						__snake_case			=		image_std if image_std is not None else IMAGENET_DEFAULT_STD





			def 							A (      self						:    Tuple      ,		a_						:    np.ndarray      ,		a_						:    Dict[str, int]      ,		a_						:    Optional[float] = None      ,		a_						:    PILImageResampling = PILImageResampling.BICUBIC      ,		a_						:    Optional[Union[str, ChannelDimension]] = None      ,		**a_						:    Any      ,		):



						"""simple docstring"""




						__snake_case			=		get_size_dict(a_      ,		default_to_square=a_							)
						if "shortest_edge" not in size and ("height" not in size or "width" not in size):
									raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''							)
						if crop_pct is not None:
									if "shortest_edge" in size:
												__snake_case			=		int(size["shortest_edge"] / crop_pct							)
									elif "height" in size and "width" in size:
												if size["height"] == size["width"]:
															__snake_case			=		int(size["height"] / crop_pct							)
												else:
															__snake_case			=		(int(size["height"] / crop_pct							), int(size["width"] / crop_pct							))
									else:
												raise ValueError("Invalid size for resize: {}".format(a_							)							)

									__snake_case			=		get_resize_output_image_size(a_      ,		size=a_      ,		default_to_square=a_							)
						else:
									if "shortest_edge" in size:
												__snake_case			=		get_resize_output_image_size(a_      ,		size=size["shortest_edge"]      ,		default_to_square=a_							)
									elif "height" in size and "width" in size:
												__snake_case			=		(size["height"], size["width"])
									else:
												raise ValueError("Invalid size for resize: {}".format(a_							)							)

						return resize(a_      ,		size=a_      ,		resample=a_      ,		data_format=a_      ,		**a_							)





			def 							A (      self						:    int      ,		a_						:    np.ndarray      ,		a_						:    Dict[str, int]      ,		a_						:    Optional[Union[str, ChannelDimension]] = None      ,		**a_						:    str      ,		):



						"""simple docstring"""




						__snake_case			=		get_size_dict(a_							)
						if "height" not in size or "width" not in size:
									raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''							)
						return center_crop(a_      ,		size=(size["height"], size["width"])      ,		data_format=a_      ,		**a_							)





			def 							A (      self						:    Dict      ,		a_						:    np.ndarray      ,		a_						:    Union[int, float]      ,		a_						:    Optional[Union[str, ChannelDimension]] = None      ,		**a_						:    Any      ,		):



						"""simple docstring"""




						return rescale(a_      ,		scale=a_      ,		data_format=a_      ,		**a_							)





			def 							A (      self						:    str      ,		a_						:    np.ndarray      ,		a_						:    Union[float, List[float]]      ,		a_						:    Union[float, List[float]]      ,		a_						:    Optional[Union[str, ChannelDimension]] = None      ,		**a_						:    Dict      ,		):



						"""simple docstring"""




						return normalize(a_      ,		mean=a_      ,		std=a_      ,		data_format=a_      ,		**a_							)






			def 							A (      self						:    Dict      ,		a_						:    ImageInput      ,		a_						:    bool = None      ,		a_						:    Dict[str, int] = None      ,		a_						:    int = None      ,		a_						:    PILImageResampling = None      ,		a_						:    bool = None      ,		a_						:    Dict[str, int] = None      ,		a_						:    bool = None      ,		a_						:    float = None      ,		a_						:    bool = None      ,		a_						:    Optional[Union[float, List[float]]] = None      ,		a_						:    Optional[Union[float, List[float]]] = None      ,		a_						:    Optional[Union[str, TensorType]] = None      ,		a_						:    ChannelDimension = ChannelDimension.FIRST      ,		**a_						:    int      ,		):



						"""simple docstring"""




						__snake_case			=		do_resize if do_resize is not None else self.do_resize
						__snake_case			=		crop_pct if crop_pct is not None else self.crop_pct
						__snake_case			=		resample if resample is not None else self.resample
						__snake_case			=		do_center_crop if do_center_crop is not None else self.do_center_crop
						__snake_case			=		do_rescale if do_rescale is not None else self.do_rescale
						__snake_case			=		rescale_factor if rescale_factor is not None else self.rescale_factor
						__snake_case			=		do_normalize if do_normalize is not None else self.do_normalize
						__snake_case			=		image_mean if image_mean is not None else self.image_mean
						__snake_case			=		image_std if image_std is not None else self.image_std

						__snake_case			=		size if size is not None else self.size
						__snake_case			=		get_size_dict(a_      ,		default_to_square=a_							)
						__snake_case			=		crop_size if crop_size is not None else self.crop_size
						__snake_case			=		get_size_dict(a_      ,		param_name="crop_size"							)

						__snake_case			=		make_list_of_images(a_							)

						if not valid_images(a_							):
									raise ValueError(
									    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
									    "torch.Tensor, tf.Tensor or jax.ndarray."							)

						if do_resize and size is None or resample is None:
									raise ValueError("Size and resample must be specified if do_resize is True."							)

						if do_center_crop and crop_pct is None:
									raise ValueError("Crop_pct must be specified if do_center_crop is True."							)

						if do_rescale and rescale_factor is None:
									raise ValueError("Rescale factor must be specified if do_rescale is True."							)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("Image mean and std must be specified if do_normalize is True."							)

						# All transformations expect numpy arrays.
						__snake_case			=		[to_numpy_array(a_							) for image in images]

						if do_resize:
									__snake_case			=		[self.resize(image=a_      ,		size=a_      ,		crop_pct=a_      ,		resample=a_							) for image in images]

						if do_center_crop:
									__snake_case			=		[self.center_crop(image=a_      ,		size=a_							) for image in images]

						if do_rescale:
									__snake_case			=		[self.rescale(image=a_      ,		scale=a_							) for image in images]

						if do_normalize:
									__snake_case			=		[self.normalize(image=a_      ,		mean=a_      ,		std=a_							) for image in images]

						__snake_case			=		[to_channel_dimension_format(a_      ,		a_							) for image in images]

						__snake_case			=		{"pixel_values": images}
						return BatchFeature(data=a_      ,		tensor_type=a_							)









import argparse

import torch

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


logging.set_verbosity_info()
_lowercase							:   List[str]						=						logging.get_logger(__name__)

_lowercase							:   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					( UpperCamelCase__:  str    )			-> int:





	"""simple docstring"""







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

	for parlai_name, hf_name in PATTERNS:
		A             =					k.replace(UpperCamelCase__				,       UpperCamelCase__    )

	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 	_lowerCAmelCase					( UpperCamelCase__:  Tuple    )			-> str:





	"""simple docstring"""







	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(UpperCamelCase__    )
		A             =					k.replace("""layernorm_embedding"""				,       """layer_norm"""    )
		assert new_k not in sd
		A             =					v


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





@torch.no_grad()
def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[int]    )			-> int:





	"""simple docstring"""







	A             =					torch.load(UpperCamelCase__				,       map_location="""cpu"""    )
	A             =					model["""model"""]
	A             =					BlenderbotConfig.from_json_file(UpperCamelCase__    )
	A             =					BlenderbotForConditionalGeneration(UpperCamelCase__    )
	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(UpperCamelCase__    )
		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(UpperCamelCase__    )
	m.model.load_state_dict(UpperCamelCase__				,       strict=UpperCamelCase__    )
	m.half()
	m.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
				_lowercase							:   str						=						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							:   List[str]						=						parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



