






'''simple docstring'''




import argparse

import torch

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


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

lowerCAmelCase__          =						[
    ['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       __UpperCAmelCase						(  lowerCamelCase_)							->     str:
		if k == "embeddings.weight":
				return "shared.weight"

		for parlai_name, hf_name in PATTERNS:
				UpperCamelCase__				:					Union[str, Any]					=					k.replace(lowerCamelCase_     ,      lowerCamelCase_)

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







def       __UpperCAmelCase						(  lowerCamelCase_)							->     int:
		UpperCamelCase__				:					Tuple					=					[
		    '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__				:					Optional[int]					=					sd.pop(lowerCamelCase_)
				UpperCamelCase__				:					List[str]					=					k.replace('layernorm_embedding'     ,      'layer_norm')
				assert new_k not in sd
				UpperCamelCase__				:					Union[str, Any]					=					v


lowerCAmelCase__          =						['START']







@torch.no_grad()
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     List[Any]:
		UpperCamelCase__				:					List[Any]					=					torch.load(lowerCamelCase_     ,      map_location='cpu')
		UpperCamelCase__				:					int					=					model['model']
		UpperCamelCase__				:					Optional[int]					=					BlenderbotConfig.from_json_file(lowerCamelCase_)
		UpperCamelCase__				:					Dict					=					BlenderbotForConditionalGeneration(lowerCamelCase_)
		UpperCamelCase__				:					int					=					m.model.state_dict().keys()
		UpperCamelCase__				:					Tuple					=					[]
		UpperCamelCase__				:					Tuple					=					{}
		for k, v in sd.items():
				if k in IGNORE_KEYS:
						continue

				UpperCamelCase__				:					List[Any]					=					rename_state_dict_key(lowerCamelCase_)
				if new_k not in valid_keys:
						failures.append([k, new_k])
				else:
						UpperCamelCase__				:					Dict					=					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__":
		lowerCAmelCase__          =						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'
		)
		lowerCAmelCase__          =						parser.parse_args()
		convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)












'''simple docstring'''




import numpy as np
from PIL import Image







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     np.ndarray:
		UpperCamelCase__				:					List[Any]					=					np.array(lowerCamelCase_)
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix')
		UpperCamelCase__				:					Tuple					=					0
		UpperCamelCase__				:					int					=					0
		UpperCamelCase__				:					Optional[int]					=					0
		UpperCamelCase__				:					str					=					0

		# compute the shape of the output matrix
		UpperCamelCase__				:					int					=					(arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape maxpool_shape
		UpperCamelCase__				:					Dict					=					np.zeros((maxpool_shape, maxpool_shape))

		while i < arr.shape[0]:
				if i + size > arr.shape[0]:
						# if the end of the matrix is reached, break
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the maximum of the pooling matrix
						UpperCamelCase__				:					Dict					=					np.max(arr[i : i + size, j : j + size])
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1

				# reset the column index to 0
				UpperCamelCase__				:					List[Any]					=					0
				UpperCamelCase__				:					Optional[int]					=					0

		return updated_arr







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     np.ndarray:
		UpperCamelCase__				:					Tuple					=					np.array(lowerCamelCase_)
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix')
		UpperCamelCase__				:					Optional[int]					=					0
		UpperCamelCase__				:					int					=					0
		UpperCamelCase__				:					List[str]					=					0
		UpperCamelCase__				:					List[Any]					=					0

		# compute the shape of the output matrix
		UpperCamelCase__				:					str					=					(arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape avgpool_shape
		UpperCamelCase__				:					Union[str, Any]					=					np.zeros((avgpool_shape, avgpool_shape))

		while i < arr.shape[0]:
				# if the end of the matrix is reached, break
				if i + size > arr.shape[0]:
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the average of the pooling matrix
						UpperCamelCase__				:					List[Any]					=					int(np.average(arr[i : i + size, j : j + size]))
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1
				# reset the column index to 0
				UpperCamelCase__				:					Union[str, Any]					=					0
				UpperCamelCase__				:					Optional[Any]					=					0

		return updated_arr


# Main Function
if __name__ == "__main__":
		from doctest import testmod

		testmod(name='avgpooling', verbose=True)

		# Loading the image
		lowerCAmelCase__          =						Image.open('path_to_image')

		# Converting the image to numpy array and maxpooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

		# Converting the image to numpy array and averagepooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()




