

from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

class 			a					(		lowerCamelCase_			):






						"""simple docstring"""




						_lowercase        =     ['''pixel_values''']


						def __init__(    self  ,   A_ = True  ,   A_ = None  ,   A_ = PILImageResampling.BILINEAR  ,   A_ = True  ,   A_ = None  ,   A_ = True  ,   A_ = 1 / 255  ,   A_ = True  ,   A_ = None  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


										super().__init__(**__snake_case		)
										_UpperCAmelCase    :						List[str]							=      size if size is not None else {"shortest_edge": 256}
										_UpperCAmelCase    :						Optional[Any]							=      get_size_dict(__snake_case  ,   default_to_square=__snake_case		)
										_UpperCAmelCase    :						Optional[int]							=      crop_size if crop_size is not None else {"height": 224, "width": 224}
										_UpperCAmelCase    :						List[str]							=      get_size_dict(__snake_case  ,   param_name="crop_size"		)
										_UpperCAmelCase    :						Dict							=      do_resize
										_UpperCAmelCase    :						Dict							=      size
										_UpperCAmelCase    :						int							=      resample
										_UpperCAmelCase    :						Tuple							=      do_center_crop
										_UpperCAmelCase    :						int							=      crop_size
										_UpperCAmelCase    :						List[str]							=      do_rescale
										_UpperCAmelCase    :						Union[str, Any]							=      rescale_factor
										_UpperCAmelCase    :						Optional[int]							=      do_normalize
										_UpperCAmelCase    :						Optional[int]							=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_UpperCAmelCase    :						str							=      image_std if image_std is not None else IMAGENET_STANDARD_STD


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_ = PILImageResampling.BICUBIC  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      get_size_dict(__snake_case  ,   default_to_square=__snake_case		)
										if "shortest_edge" not in size:
														raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'		)
										_UpperCAmelCase    :						Any							=      get_resize_output_image_size(__snake_case  ,   size=size["shortest_edge"]  ,   default_to_square=__snake_case		)
										return resize(__snake_case  ,   size=__snake_case  ,   resample=__snake_case  ,   data_format=__snake_case  ,   **__snake_case		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      get_size_dict(__snake_case		)
										if "height" not in size or "width" not in size:
														raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'		)
										return center_crop(__snake_case  ,   size=(size["height"], size["width"])  ,   data_format=__snake_case  ,   **__snake_case		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_ = None  ,   **A_		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = ChannelDimension.FIRST  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase    :						Optional[Any]							=      size if size is not None else self.size
										_UpperCAmelCase    :						Tuple							=      get_size_dict(__snake_case  ,   default_to_square=__snake_case		)
										_UpperCAmelCase    :						Optional[int]							=      resample if resample is not None else self.resample
										_UpperCAmelCase    :						List[Any]							=      do_center_crop if do_center_crop is not None else self.do_center_crop
										_UpperCAmelCase    :						Optional[Any]							=      crop_size if crop_size is not None else self.crop_size
										_UpperCAmelCase    :						Tuple							=      get_size_dict(__snake_case  ,   param_name="crop_size"		)
										_UpperCAmelCase    :						Optional[int]							=      do_rescale if do_rescale is not None else self.do_rescale
										_UpperCAmelCase    :						Union[str, Any]							=      rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCAmelCase    :						Tuple							=      do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase    :						int							=      image_mean if image_mean is not None else self.image_mean
										_UpperCAmelCase    :						List[str]							=      image_std if image_std is not None else self.image_std

										_UpperCAmelCase    :						Optional[Any]							=      make_list_of_images(__snake_case		)

										if not valid_images(__snake_case		):
														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:
														raise ValueError("Size must be specified if do_resize is True."		)

										if do_center_crop and crop_size is None:
														raise ValueError("Crop size 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.
										_UpperCAmelCase    :						Dict							=      [to_numpy_array(__snake_case		) for image in images]

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

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

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

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

										_UpperCAmelCase    :						Dict							=      [to_channel_dimension_format(__snake_case  ,   __snake_case		) for image in images]

										_UpperCAmelCase    :						Optional[int]							=      {"pixel_values": images}
										return BatchFeature(data=__snake_case  ,   tensor_type=__snake_case		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      outputs.logits

										# Resize logits and compute semantic segmentation maps
										if target_sizes is not None:
														if len(__snake_case		) != len(__snake_case		):
																		raise ValueError(
																		    "Make sure that you pass in as many target sizes as the batch dimension of the logits"		)

														if is_torch_tensor(__snake_case		):
																		_UpperCAmelCase    :						Tuple							=      target_sizes.numpy()

														_UpperCAmelCase    :						Optional[Any]							=      []

														for idx in range(len(__snake_case		)		):
																		_UpperCAmelCase    :						Tuple							=      torch.nn.functional.interpolate(
																		    logits[idx].unsqueeze(dim=0		)  ,   size=target_sizes[idx]  ,   mode="bilinear"  ,   align_corners=__snake_case		)
																		_UpperCAmelCase    :						Any							=      resized_logits[0].argmax(dim=0		)
																		semantic_segmentation.append(__snake_case		)
										else:
														_UpperCAmelCase    :						List[Any]							=      logits.argmax(dim=1		)
														_UpperCAmelCase    :						Tuple							=      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

										return semantic_segmentation





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)


