
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def 						__magic_name__ (    __a			:       Tuple       ,		__a			:       str       ,		__a			:       Union[str, Any]						):



     '''simple docstring'''




     UpperCamelCase__				=				AutoConfig.from_pretrained(__snake_case						)
     UpperCamelCase__				=				FlaxAutoModelForSeqaSeqLM.from_config(config=__snake_case						)
     UpperCamelCase__				=				checkpoints.load_tax_checkpoint(__snake_case						)

     UpperCamelCase__				=				'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

     if config.model_type == "t5":
          UpperCamelCase__				=				'SelfAttention'
     if config.model_type == "longt5" and config.encoder_attention_type == "local":
          UpperCamelCase__				=				'LocalSelfAttention'
     elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
          UpperCamelCase__				=				'TransientGlobalSelfAttention'
     else:
          raise ValueError(
              """Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`"""
              """ attribute with a value from [\'local\', \'transient-global]."""						)

     # Encoder
     for layer_index in range(config.num_layers						):
          UpperCamelCase__				=				f"layers_{str(__snake_case						)}"

          # Self-Attention
          UpperCamelCase__				=				tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
          UpperCamelCase__				=				tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
          UpperCamelCase__				=				tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
          UpperCamelCase__				=				tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

          # Global input layer norm
          if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
               UpperCamelCase__				=				tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

          # Layer Normalization
          UpperCamelCase__				=				tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

          if split_mlp_wi:
               UpperCamelCase__				=				tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
               UpperCamelCase__				=				tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
          else:
               UpperCamelCase__				=				tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

          UpperCamelCase__				=				tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

          # Layer Normalization
          UpperCamelCase__				=				tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

          # Assigning
          UpperCamelCase__				=				flax_model.params['encoder']['block'][str(__snake_case						)]['layer']
          UpperCamelCase__				=				tax_attention_key
          UpperCamelCase__				=				tax_attention_out
          UpperCamelCase__				=				tax_attention_query
          UpperCamelCase__				=				tax_attention_value

          UpperCamelCase__				=				tax_attention_layer_norm

          # Global input layer norm
          if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
               UpperCamelCase__				=				tax_global_layer_norm

          if split_mlp_wi:
               UpperCamelCase__				=				tax_mlp_wi_a
               UpperCamelCase__				=				tax_mlp_wi_a
          else:
               UpperCamelCase__				=				tax_mlp_wi

          UpperCamelCase__				=				tax_mlp_wo
          UpperCamelCase__				=				tax_mlp_layer_norm

          UpperCamelCase__				=				flax_model_encoder_layer_block

     # Only for layer 0:
     UpperCamelCase__				=				tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
     UpperCamelCase__				=				tax_encoder_rel_embedding

     # Side/global relative position_bias + layer norm
     if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
          UpperCamelCase__				=				tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
          UpperCamelCase__				=				tax_encoder_global_rel_embedding

     # Assigning
     UpperCamelCase__				=				tax_model['target']['encoder']['encoder_norm']['scale']
     UpperCamelCase__				=				tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_layers						):
          UpperCamelCase__				=				f"layers_{str(__snake_case						)}"

          # Self-Attention
          UpperCamelCase__				=				tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
          UpperCamelCase__				=				tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
          UpperCamelCase__				=				tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
          UpperCamelCase__				=				tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

          # Layer Normalization
          UpperCamelCase__				=				tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
              'scale'
          ]

          # Encoder-Decoder-Attention
          UpperCamelCase__				=				tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
          UpperCamelCase__				=				tax_enc_dec_attention_module['key']['kernel']
          UpperCamelCase__				=				tax_enc_dec_attention_module['out']['kernel']
          UpperCamelCase__				=				tax_enc_dec_attention_module['query']['kernel']
          UpperCamelCase__				=				tax_enc_dec_attention_module['value']['kernel']

          # Layer Normalization
          UpperCamelCase__				=				tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

          # MLP
          if split_mlp_wi:
               UpperCamelCase__				=				tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
               UpperCamelCase__				=				tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
          else:
               UpperCamelCase__				=				tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

          UpperCamelCase__				=				tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

          # Layer Normalization
          UpperCamelCase__				=				tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

          # Assigning
          UpperCamelCase__				=				flax_model.params['decoder']['block'][str(__snake_case						)]['layer']
          UpperCamelCase__				=				tax_attention_key
          UpperCamelCase__				=				tax_attention_out
          UpperCamelCase__				=				tax_attention_query
          UpperCamelCase__				=				tax_attention_value

          UpperCamelCase__				=				tax_pre_attention_layer_norm

          UpperCamelCase__				=				tax_enc_dec_attention_key
          UpperCamelCase__				=				tax_enc_dec_attention_out
          UpperCamelCase__				=				tax_enc_dec_attention_query
          UpperCamelCase__				=				tax_enc_dec_attention_value

          UpperCamelCase__				=				tax_cross_layer_norm

          if split_mlp_wi:
               UpperCamelCase__				=				tax_mlp_wi_a
               UpperCamelCase__				=				tax_mlp_wi_a
          else:
               UpperCamelCase__				=				tax_mlp_wi

          UpperCamelCase__				=				tax_mlp_wo

          UpperCamelCase__				=				txa_mlp_layer_norm

          UpperCamelCase__				=				flax_model_decoder_layer_block

     # Decoder Normalization
     UpperCamelCase__				=				tax_model['target']['decoder']['decoder_norm']['scale']
     UpperCamelCase__				=				txa_decoder_norm

     # Only for layer 0:
     UpperCamelCase__				=				tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
     UpperCamelCase__				=				tax_decoder_rel_embedding

     # Token Embeddings
     UpperCamelCase__				=				tax_model['target']['token_embedder']['embedding']
     UpperCamelCase__				=				txa_token_embeddings

     # LM Head (only in v1.1 and LongT5 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          UpperCamelCase__				=				tax_model['target']['decoder']['logits_dense']['kernel']

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


if __name__ == "__main__":
    lowerCamelCase_							=     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
    )
    parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
    parser.add_argument(
        '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
    )
    lowerCamelCase_							=     parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












'''simple docstring'''




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros






def        _lowerCAmelCase			(  __snake_case  : Optional[Any] ,							__snake_case  : Optional[int]			)     ->     List[str]:
     __A						: List[str]      =       k_size // 2
     __A ,__A						: List[Any]      =       mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __A						: List[Any]      =       1 / (2 * pi * sigma) * exp(-(square(__snake_case			) + square(__snake_case			)) / (2 * square(__snake_case			))			)
     return g






def        _lowerCAmelCase			(  __snake_case  : List[str] ,							__snake_case  : List[str] ,							__snake_case  : int			)     ->     Union[str, Any]:
     __A ,__A						: Tuple      =       image.shape[0], image.shape[1]
     # dst image height and width
     __A						: Tuple      =       height - k_size + 1
     __A						: Optional[Any]      =       width - k_size + 1

     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     __A						: str      =       zeros((dst_height * dst_width, k_size * k_size)			)
     __A						: Optional[Any]      =       0
     for i, j in product(range(__snake_case			) ,							range(__snake_case			)			):
          __A						: int      =       ravel(image[i : i + k_size, j : j + k_size]			)
          __A						: List[str]      =       window
          row += 1

     #  turn the kernel into shape(k*k, 1)
     __A						: List[Any]      =       gen_gaussian_kernel(__snake_case ,							__snake_case			)
     __A						: Any      =       ravel(__snake_case			)

     # reshape and get the dst image
     __A						: Dict      =       dot(__snake_case ,							__snake_case			).reshape(__snake_case ,							__snake_case			).astype(__snake_case			)

     return dst


if __name__ == "__main__":
  # read original image
  lowercase__    :      List[Any]						=    imread(r'''../image_data/lena.jpg''')
  # turn image in gray scale value
  lowercase__    :      Dict						=    cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  lowercase__    :      Any						=    gaussian_filter(gray, 3, sigma=1)
  lowercase__    :      str						=    gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
  imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
  waitKey()