




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: Optional[Any]			,							lowercase			: Dict  ):


      '''simple docstring'''



      lowerCamelCase_	  =					AutoConfig.from_pretrained(lowercase  )
      lowerCamelCase_	  =					FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase  )
      lowerCamelCase_	  =					checkpoints.load_tax_checkpoint(lowercase  )

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

      if config.model_type == "t5":
            lowerCamelCase_	  =					'SelfAttention'
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            lowerCamelCase_	  =					'LocalSelfAttention'
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            lowerCamelCase_	  =					'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  ):
            lowerCamelCase_	  =					f"""layers_{str(lowercase  )}"""

            # Self-Attention
            lowerCamelCase_	  =					tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
            lowerCamelCase_	  =					tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
            lowerCamelCase_	  =					tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
            lowerCamelCase_	  =					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":
                  lowerCamelCase_	  =					tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

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

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

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

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

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

            lowerCamelCase_	  =					tax_attention_layer_norm

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

            if split_mlp_wi:
                  lowerCamelCase_	  =					tax_mlp_wi_a
                  lowerCamelCase_	  =					tax_mlp_wi_a
            else:
                  lowerCamelCase_	  =					tax_mlp_wi

            lowerCamelCase_	  =					tax_mlp_wo
            lowerCamelCase_	  =					tax_mlp_layer_norm

            lowerCamelCase_	  =					flax_model_encoder_layer_block

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

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

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

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

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

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

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

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

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

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

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

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

            lowerCamelCase_	  =					tax_pre_attention_layer_norm

            lowerCamelCase_	  =					tax_enc_dec_attention_key
            lowerCamelCase_	  =					tax_enc_dec_attention_out
            lowerCamelCase_	  =					tax_enc_dec_attention_query
            lowerCamelCase_	  =					tax_enc_dec_attention_value

            lowerCamelCase_	  =					tax_cross_layer_norm

            if split_mlp_wi:
                  lowerCamelCase_	  =					tax_mlp_wi_a
                  lowerCamelCase_	  =					tax_mlp_wi_a
            else:
                  lowerCamelCase_	  =					tax_mlp_wi

            lowerCamelCase_	  =					tax_mlp_wo

            lowerCamelCase_	  =					txa_mlp_layer_norm

            lowerCamelCase_	  =					flax_model_decoder_layer_block

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

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

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

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

      flax_model.save_pretrained(lowercase  )
      print('T5X Model was sucessfully converted!'  )


if __name__ == "__main__":
 lowerCamelCase       :	Union[str, Any]									=     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       :	Dict									=     parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowerCamelCase       :	str									=     imread(r"digital_image_processing/image_data/lena_small.jpg")
lowerCamelCase       :	Optional[Any]									=     cvtColor(img, COLOR_BGR2GRAY)

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					cn.convert_to_negative(lowercase  )
      # assert negative_img array for at least one True
      assert negative_img.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      with Image.open('digital_image_processing/image_data/lena_small.jpg'  ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowercase			,							1_10  )  ).startswith(
                '<PIL.Image.Image image mode=RGB size=100x100 at'  )

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					canny.gen_gaussian_kernel(9			,							sigma=1.4  )
      # Assert ambiguous array
      assert resp.all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					imread('digital_image_processing/image_data/lena_small.jpg'			,							0  )
      # assert ambiguous array for all == True
      assert canny_img.all()
      lowerCamelCase_	  =					canny.canny(lowercase  )
      # assert canny array for at least one True
      assert canny_array.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert gg.gaussian_filter(lowercase			,							5			,							sigma=0.9  ).all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]  )
      lowerCamelCase_	  =					conv.img_convolve(lowercase			,							lowercase  ).astype(lowercase  )
      assert res.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert med.median_filter(lowercase			,							3  ).any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_  ,       lowerCamelCase_	  =					sob.sobel_filter(lowercase  )
      assert grad.any() and theta.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					sp.make_sepia(lowercase			,							20  )
      assert sepia.all()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"  ):


      '''simple docstring'''



      lowerCamelCase_	  =					bs.Burkes(imread(lowercase			,							1  )			,							1_20  )
      burkes.process()
      assert burkes.output_img.any()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"			,							):


      '''simple docstring'''



      lowerCamelCase_	  =					rs.NearestNeighbour(imread(lowercase			,							1  )			,							4_00			,							2_00  )
      nn.process()
      assert nn.output.any()






def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					'digital_image_processing/image_data/lena.jpg'

      # Reading the image and converting it to grayscale.
      lowerCamelCase_	  =					imread(lowercase			,							0  )

      # Test for get_neighbors_pixel function() return not None
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					image[x_coordinate][y_coordinate]

      lowerCamelCase_	  =					lbp.get_neighbors_pixel(
          lowercase			,							lowercase			,							lowercase			,							lowercase  )

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      lowerCamelCase_	  =					np.zeros((image.shape[0], image.shape[1])  )

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0			,							image.shape[0]  ):
            for j in range(0			,							image.shape[1]  ):
                  lowerCamelCase_	  =					lbp.local_binary_value(lowercase			,							lowercase			,							lowercase  )

      assert lbp_image.any()



