




"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def   UpperCamelCase__     (		lowercase__  :     Optional[Any]       ,     lowercase__  :     List[str]       ,     lowercase__  :     Optional[int]   ):
  snake_case		:						int								=				TaConfig.from_pretrained(lowercase__   )
  snake_case		:						List[str]								=				FlaxTaForConditionalGeneration(config=lowercase__   )
  snake_case		:						Any								=				checkpoints.load_tax_checkpoint(lowercase__   )

  snake_case		:						Any								=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

  # Encoder
  for layer_index in range(config.num_layers   ):
    snake_case		:						Any								=				F'''layers_{str(lowercase__   )}'''

    # Self-Attention
    snake_case		:						Dict								=				tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
    snake_case		:						Optional[Any]								=				tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
    snake_case		:						Any								=				tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
    snake_case		:						List[str]								=				tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

    # Layer Normalization
    snake_case		:						int								=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

    if split_mlp_wi:
      snake_case		:						Tuple								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      snake_case		:						Any								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      snake_case		:						Any								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

    snake_case		:						Dict								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    snake_case		:						Optional[Any]								=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    snake_case		:						Tuple								=				tax_attention_key
    snake_case		:						int								=				tax_attention_out
    snake_case		:						List[Any]								=				tax_attention_query
    snake_case		:						Union[str, Any]								=				tax_attention_value

    snake_case		:						Any								=				tax_attention_layer_norm

    if split_mlp_wi:
      snake_case		:						Tuple								=				tax_mlp_wi_a
      snake_case		:						List[str]								=				tax_mlp_wi_a
    else:
      snake_case		:						Optional[int]								=				tax_mlp_wi

    snake_case		:						Optional[Any]								=				tax_mlp_wo
    snake_case		:						Optional[Any]								=				tax_mlp_layer_norm

  # Only for layer 0:
  snake_case		:						int								=				tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
  snake_case		:						List[Any]								=				tax_encoder_rel_embedding

  # Assigning
  snake_case		:						Union[str, Any]								=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
  snake_case		:						List[str]								=				tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_decoder_layers   ):
    snake_case		:						Optional[Any]								=				F'''layers_{str(lowercase__   )}'''

    # Self-Attention
    snake_case		:						int								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
    snake_case		:						str								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
    snake_case		:						List[Any]								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
    snake_case		:						List[Any]								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

    # Layer Normalization
    snake_case		:						int								=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
        "scale"
    ]

    # Encoder-Decoder-Attention
    snake_case		:						str								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
        "kernel"
    ]
    snake_case		:						Optional[int]								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
        "kernel"
    ]
    snake_case		:						List[str]								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
        "kernel"
    ]
    snake_case		:						Tuple								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
        "kernel"
    ]

    # Layer Normalization
    snake_case		:						int								=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

    # MLP
    if split_mlp_wi:
      snake_case		:						List[str]								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      snake_case		:						Tuple								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      snake_case		:						Optional[Any]								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

    snake_case		:						Any								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    snake_case		:						Optional[Any]								=				tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    snake_case		:						Dict								=				tax_attention_key
    snake_case		:						List[Any]								=				tax_attention_out
    snake_case		:						List[Any]								=				tax_attention_query
    snake_case		:						List[Any]								=				tax_attention_value

    snake_case		:						Optional[int]								=				tax_pre_attention_layer_norm

    snake_case		:						int								=				tax_enc_dec_attention_key
    snake_case		:						Optional[Any]								=				tax_enc_dec_attention_out
    snake_case		:						List[str]								=				tax_enc_dec_attention_query
    snake_case		:						Tuple								=				tax_enc_dec_attention_value

    snake_case		:						Optional[Any]								=				tax_cross_layer_norm

    if split_mlp_wi:
      snake_case		:						Any								=				tax_mlp_wi_a
      snake_case		:						List[Any]								=				tax_mlp_wi_a
    else:
      snake_case		:						Union[str, Any]								=				tax_mlp_wi

    snake_case		:						Tuple								=				tax_mlp_wo

    snake_case		:						Union[str, Any]								=				txa_mlp_layer_norm

  # Decoder Normalization
  snake_case		:						Optional[Any]								=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
  snake_case		:						Optional[Any]								=				txa_decoder_norm

  # Only for layer 0:
  snake_case		:						str								=				tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
  snake_case		:						Tuple								=				tax_decoder_rel_embedding

  # Token Embeddings
  snake_case		:						int								=				tax_model["target"]["token_embedder"]["embedding"]
  snake_case		:						Union[str, Any]								=				txa_token_embeddings

  # LM Head (only in v1.1 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    snake_case		:						Union[str, Any]								=				tax_model["target"]["decoder"]["logits_dense"]["kernel"]

  flax_model.save_pretrained(lowercase__   )
  print("T5X Model was sucessfully converted!"   )


if __name__ == "__main__":
       __A 		=			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
       )
       parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
       parser.add_argument(
           "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
       )
       __A 		=			parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







"""simple docstring"""

import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class 			lowerCamelCase__    :


			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						int								=				0
					# max greyscale value for #FFFFFF
					snake_case		:						List[str]								=				int(self.get_greyscale(255				, 255				, 255							)							)

					if not self.min_threshold < threshold < self.max_threshold:
							snake_case		:						Union[str, Any]								=				F'''Factor value should be from 0 to {self.max_threshold}'''
							raise ValueError(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				input_img
					snake_case		:						Optional[Any]								=				threshold
					snake_case			,				snake_case		:						List[str]								=				self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					snake_case		:						Union[str, Any]								=				[
					    [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
					]
					snake_case		:						Tuple								=				np.ones((self.width, self.height, 3)				, np.uinta							) * 255


			@classmethod
			def 							lowerCamelCase_				(     cls				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					for y in range(self.height							):
							for x in range(self.width							):
									snake_case		:						Dict								=				int(self.get_greyscale(*self.input_img[y][x]							)							)
									if self.threshold > greyscale + self.error_table[y][x]:
											snake_case		:						List[str]								=				(0, 0, 0)
											snake_case		:						List[Any]								=				greyscale + self.error_table[x][y]
									else:
											snake_case		:						int								=				(255, 255, 255)
											snake_case		:						Tuple								=				greyscale + self.error_table[x][y] - 255
									self.error_table[y][x + 1] += int(8 / 32 * current_error							)
									self.error_table[y][x + 2] += int(4 / 32 * current_error							)
									self.error_table[y + 1][x] += int(8 / 32 * current_error							)
									self.error_table[y + 1][x + 1] += int(4 / 32 * current_error							)
									self.error_table[y + 1][x + 2] += int(2 / 32 * current_error							)
									self.error_table[y + 1][x - 1] += int(4 / 32 * current_error							)
									self.error_table[y + 1][x - 2] += int(2 / 32 * current_error							)


if __name__ == "__main__":
							# create Burke's instances with original images in greyscale
							__A 		=			[
							    Burkes(imread("image_data/lena.jpg", 1), threshold)
							    for threshold in (1, 126, 130, 140)
							]

							for burkes in burkes_instances:
														burkes.process()

							for burkes in burkes_instances:
														imshow(
														    f'Original image with dithering threshold: {burkes.threshold}',
														    burkes.output_img,
														)

							waitKey(0)
							destroyAllWindows()

