





'''simple docstring'''






import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      return params[f"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase="attention"  ):





      """simple docstring"""


      _lowerCAmelCase							     =							_lowerCAmelCase							     =							np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]  )
      _lowerCAmelCase							     =							k_tmp.reshape(k_tmp.shape[0]							,	k_tmp.shape[1] * k_tmp.shape[2]  )
      _lowerCAmelCase							     =							np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]  )
      _lowerCAmelCase							     =							o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]							,	o_tmp.shape[2]  )
      _lowerCAmelCase							     =							np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]  )
      _lowerCAmelCase							     =							q_tmp.reshape(q_tmp.shape[0]							,	q_tmp.shape[1] * q_tmp.shape[2]  )
      _lowerCAmelCase							     =							np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]  )
      _lowerCAmelCase							     =							v_tmp.reshape(v_tmp.shape[0]							,	v_tmp.shape[1] * v_tmp.shape[2]  )
      return k, o, q, v
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase=False  ):





      """simple docstring"""


      if split_mlp_wi:
            _lowerCAmelCase							     =							params[f"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
            _lowerCAmelCase							     =							params[f"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
            _lowerCAmelCase							     =							(wi_a, wi_a)
      else:
            _lowerCAmelCase							     =							params[f"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

      _lowerCAmelCase							     =							params[f"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
      return wi, wo
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      return params[f"{prefix}/{prefix}/{layer_name}/scale"][:, i]
def    UpperCamelCase__      (		lowerCAmelCase							,	*, lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase = False  ):





      """simple docstring"""


      _lowerCAmelCase							     =							traverse_util.flatten_dict(variables["""target"""]  )
      _lowerCAmelCase							     =							{"""/""".join(lowerCAmelCase  ): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      _lowerCAmelCase							     =							"""encoder/encoder/mlp/wi_0/kernel""" in old
      print("""Split MLP:"""							,	lowerCAmelCase  )

      _lowerCAmelCase							     =							collections.OrderedDict()

      # Shared embeddings.
      _lowerCAmelCase							     =							old["""token_embedder/embedding"""]

      # Encoder.
      for i in range(lowerCAmelCase  ):
            # Block i, layer 0 (Self Attention).
            _lowerCAmelCase							     =							tax_layer_norm_lookup(lowerCAmelCase							,	lowerCAmelCase							,	"""encoder"""							,	"""pre_attention_layer_norm"""  )
            _lowerCAmelCase     ,							_lowerCAmelCase     ,							_lowerCAmelCase     ,							_lowerCAmelCase							     =							tax_attention_lookup(lowerCAmelCase							,	lowerCAmelCase							,	"""encoder"""							,	"""attention"""  )
            _lowerCAmelCase							     =							layer_norm
            _lowerCAmelCase							     =							k.T
            _lowerCAmelCase							     =							o.T
            _lowerCAmelCase							     =							q.T
            _lowerCAmelCase							     =							v.T

            # Block i, layer 1 (MLP).
            _lowerCAmelCase							     =							tax_layer_norm_lookup(lowerCAmelCase							,	lowerCAmelCase							,	"""encoder"""							,	"""pre_mlp_layer_norm"""  )
            _lowerCAmelCase     ,							_lowerCAmelCase							     =							tax_mlp_lookup(lowerCAmelCase							,	lowerCAmelCase							,	"""encoder"""							,	lowerCAmelCase  )
            _lowerCAmelCase							     =							layer_norm
            if split_mlp_wi:
                  _lowerCAmelCase							     =							wi[0].T
                  _lowerCAmelCase							     =							wi[1].T
            else:
                  _lowerCAmelCase							     =							wi.T
            _lowerCAmelCase							     =							wo.T
            if scalable_attention:
                  # convert the rel_embedding of each layer
                  _lowerCAmelCase							     =							tax_relpos_bias_lookup(
                      lowerCAmelCase							,	lowerCAmelCase							,	"""encoder"""  ).T

      _lowerCAmelCase							     =							old["""encoder/encoder_norm/scale"""]

      if not scalable_attention:
            _lowerCAmelCase							     =							tax_relpos_bias_lookup(
                lowerCAmelCase							,	0							,	"""encoder"""  ).T
            _lowerCAmelCase							     =							tax_relpos_bias_lookup(
                lowerCAmelCase							,	0							,	"""decoder"""  ).T

      if not is_encoder_only:
            # Decoder.
            for i in range(lowerCAmelCase  ):
                  # Block i, layer 0 (Self Attention).
                  _lowerCAmelCase							     =							tax_layer_norm_lookup(lowerCAmelCase							,	lowerCAmelCase							,	"""decoder"""							,	"""pre_self_attention_layer_norm"""  )
                  _lowerCAmelCase     ,							_lowerCAmelCase     ,							_lowerCAmelCase     ,							_lowerCAmelCase							     =							tax_attention_lookup(lowerCAmelCase							,	lowerCAmelCase							,	"""decoder"""							,	"""self_attention"""  )
                  _lowerCAmelCase							     =							layer_norm
                  _lowerCAmelCase							     =							k.T
                  _lowerCAmelCase							     =							o.T
                  _lowerCAmelCase							     =							q.T
                  _lowerCAmelCase							     =							v.T

                  # Block i, layer 1 (Cross Attention).
                  _lowerCAmelCase							     =							tax_layer_norm_lookup(lowerCAmelCase							,	lowerCAmelCase							,	"""decoder"""							,	"""pre_cross_attention_layer_norm"""  )
                  _lowerCAmelCase     ,							_lowerCAmelCase     ,							_lowerCAmelCase     ,							_lowerCAmelCase							     =							tax_attention_lookup(lowerCAmelCase							,	lowerCAmelCase							,	"""decoder"""							,	"""encoder_decoder_attention"""  )
                  _lowerCAmelCase							     =							layer_norm
                  _lowerCAmelCase							     =							k.T
                  _lowerCAmelCase							     =							o.T
                  _lowerCAmelCase							     =							q.T
                  _lowerCAmelCase							     =							v.T

                  # Block i, layer 2 (MLP).
                  _lowerCAmelCase							     =							tax_layer_norm_lookup(lowerCAmelCase							,	lowerCAmelCase							,	"""decoder"""							,	"""pre_mlp_layer_norm"""  )
                  _lowerCAmelCase     ,							_lowerCAmelCase							     =							tax_mlp_lookup(lowerCAmelCase							,	lowerCAmelCase							,	"""decoder"""							,	lowerCAmelCase  )
                  _lowerCAmelCase							     =							layer_norm
                  if split_mlp_wi:
                        _lowerCAmelCase							     =							wi[0].T
                        _lowerCAmelCase							     =							wi[1].T
                  else:
                        _lowerCAmelCase							     =							wi.T
                  _lowerCAmelCase							     =							wo.T

                  if scalable_attention:
                        # convert the rel_embedding of each layer
                        _lowerCAmelCase							     =							tax_relpos_bias_lookup(lowerCAmelCase							,	lowerCAmelCase							,	"""decoder"""  ).T

            _lowerCAmelCase							     =							old["""decoder/decoder_norm/scale"""]

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  _lowerCAmelCase							     =							old["""decoder/logits_dense/kernel"""].T

      return new
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							collections.OrderedDict([(k, torch.from_numpy(v.copy()  )) for (k, v) in converted_params.items()]  )

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            _lowerCAmelCase							     =							state_dict["""shared.weight"""]

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  _lowerCAmelCase							     =							state_dict["""shared.weight"""]

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print("""Using shared word embeddings as lm_head."""  )
                  _lowerCAmelCase							     =							state_dict["""shared.weight"""]

      return state_dict
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							checkpoints.load_tax_checkpoint(lowerCAmelCase  )
      _lowerCAmelCase							     =							convert_tax_to_pytorch(
          lowerCAmelCase							,	num_layers=config.num_layers							,	is_encoder_only=lowerCAmelCase							,	scalable_attention=lowerCAmelCase  )
      _lowerCAmelCase							     =							make_state_dict(lowerCAmelCase							,	lowerCAmelCase  )
      model.load_state_dict(lowerCAmelCase							,	strict=lowerCAmelCase  )
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase = False							,	lowerCAmelCase = False							,	):





      """simple docstring"""


      _lowerCAmelCase							     =							MTaConfig.from_json_file(lowerCAmelCase  )
      print(f"Building PyTorch model from configuration: {config}"  )
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            _lowerCAmelCase							     =							UMTaEncoderModel(lowerCAmelCase  )
      else:
            _lowerCAmelCase							     =							UMTaForConditionalGeneration(lowerCAmelCase  )

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  )

      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}"  )
      model.save_pretrained(lowerCAmelCase  )

      # Verify that we can load the checkpoint.
      model.from_pretrained(lowerCAmelCase  )
      print("""Done"""  )


if __name__ == "__main__":
      A__				:   Tuple      =argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
      # Required parameters
      parser.add_argument(
          '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
      )
      parser.add_argument(
          '''--config_file''',
          default=None,
          type=str,
          required=True,
          help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      parser.add_argument(
          '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
      )
      parser.add_argument(
          '''--scalable_attention''',
          action='''store_true''',
          help='''Whether the model uses scaled attention (umt5 model)''',
          default=False,
      )
      A__				:   Union[str, Any]      =parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path,
          args.config_file,
          args.pytorch_dump_path,
          args.is_encoder_only,
          args.scalable_attention,
      )







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    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     k_size // 2
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      SCREAMING_SNAKE_CASE_      =     1 / (2 * pi * sigma) * exp(-(square(__UpperCamelCase ) + square(__UpperCamelCase )) / (2 * square(__UpperCamelCase )) )
      return g


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     image.shape[0], image.shape[1]
      # dst image height and width
      SCREAMING_SNAKE_CASE_      =     height - k_size + 1
      SCREAMING_SNAKE_CASE_      =     width - k_size + 1

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

      #  turn the kernel into shape(k*k, 1)
      SCREAMING_SNAKE_CASE_      =     gen_gaussian_kernel(__UpperCamelCase   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     ravel(__UpperCamelCase )

      # reshape and get the dst image
      SCREAMING_SNAKE_CASE_      =     dot(__UpperCamelCase   ,					__UpperCamelCase ).reshape(__UpperCamelCase   ,					__UpperCamelCase ).astype(__UpperCamelCase )

      return dst


if __name__ == "__main__":
      # read original image
      A   :     Tuple       =				imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      A   :     Optional[int]       =				cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      A   :     Tuple       =				gaussian_filter(gray, 3, sigma=1)
      A   :     Optional[int]       =				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()


