
"""simple docstring"""

import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def     lowercase__						(					lowerCAmelCase				:					str ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					str="attention" ) ->      Tuple:






    """simple docstring"""




    UpperCAmelCase									=					params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
    UpperCAmelCase									=					params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
    UpperCAmelCase									=					params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
    UpperCAmelCase									=					params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
    return k, o, q, v




def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					List[Any]=False ) ->      Optional[int]:






    """simple docstring"""




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

    UpperCAmelCase									=					params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
    return wi, wo




def     lowercase__						(					lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Dict ) ->      Union[str, Any]:






    """simple docstring"""




    return params[F"{prefix}/layers_{i}/{layer_name}/scale"]




def     lowercase__						(					lowerCAmelCase				:					dict ,				*, lowerCAmelCase				:					int ,				lowerCAmelCase				:					bool ) ->      int:






    """simple docstring"""




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

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    UpperCAmelCase									=					"""encoder/layers_0/mlp/wi_0/kernel""" in old
    print('Split MLP:' ,				UpperCAmelCase__ )

    UpperCAmelCase									=					collections.OrderedDict()

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

    # Encoder.
    for i in range(UpperCAmelCase__ ):
        # Block i, layer 0 (Self Attention).
        UpperCAmelCase									=					tax_layer_norm_lookup(UpperCAmelCase__ ,				UpperCAmelCase__ ,				'encoder' ,				'pre_attention_layer_norm' )
        UpperCAmelCase									=					tax_attention_lookup(UpperCAmelCase__ ,				UpperCAmelCase__ ,				'encoder' ,				'attention' )
        UpperCAmelCase									=					layer_norm
        UpperCAmelCase									=					k.T
        UpperCAmelCase									=					o.T
        UpperCAmelCase									=					q.T
        UpperCAmelCase									=					v.T

        # Block i, layer 1 (MLP).
        UpperCAmelCase									=					tax_layer_norm_lookup(UpperCAmelCase__ ,				UpperCAmelCase__ ,				'encoder' ,				'pre_mlp_layer_norm' )
        UpperCAmelCase									=					tax_mlp_lookup(UpperCAmelCase__ ,				UpperCAmelCase__ ,				'encoder' ,				UpperCAmelCase__ )
        UpperCAmelCase									=					layer_norm
        if split_mlp_wi:
            UpperCAmelCase									=					wi[0].T
            UpperCAmelCase									=					wi[1].T
        else:
            UpperCAmelCase									=					wi.T
        UpperCAmelCase									=					wo.T

    UpperCAmelCase									=					old[
        """encoder/relpos_bias/rel_embedding"""
    ].T
    UpperCAmelCase									=					old["""encoder/encoder_norm/scale"""]

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

            # Block i, layer 1 (Cross Attention).
            UpperCAmelCase									=					tax_layer_norm_lookup(UpperCAmelCase__ ,				UpperCAmelCase__ ,				'decoder' ,				'pre_cross_attention_layer_norm' )
            UpperCAmelCase									=					tax_attention_lookup(UpperCAmelCase__ ,				UpperCAmelCase__ ,				'decoder' ,				'encoder_decoder_attention' )
            UpperCAmelCase									=					layer_norm
            UpperCAmelCase									=					k.T
            UpperCAmelCase									=					o.T
            UpperCAmelCase									=					q.T
            UpperCAmelCase									=					v.T

            # Block i, layer 2 (MLP).
            UpperCAmelCase									=					tax_layer_norm_lookup(UpperCAmelCase__ ,				UpperCAmelCase__ ,				'decoder' ,				'pre_mlp_layer_norm' )
            UpperCAmelCase									=					tax_mlp_lookup(UpperCAmelCase__ ,				UpperCAmelCase__ ,				'decoder' ,				UpperCAmelCase__ )
            UpperCAmelCase									=					layer_norm
            if split_mlp_wi:
                UpperCAmelCase									=					wi[0].T
                UpperCAmelCase									=					wi[1].T
            else:
                UpperCAmelCase									=					wi.T
            UpperCAmelCase									=					wo.T

        UpperCAmelCase									=					old["""decoder/decoder_norm/scale"""]
        UpperCAmelCase									=					old[
            """decoder/relpos_bias/rel_embedding"""
        ].T

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

    return new




def     lowercase__						(					lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					bool ) ->      int:






    """simple docstring"""




    UpperCAmelCase									=					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:
        UpperCAmelCase									=					state_dict["""shared.weight"""]

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            UpperCAmelCase									=					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.' )
            UpperCAmelCase									=					state_dict["""shared.weight"""]

    return state_dict




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Optional[int] ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					checkpoints.load_tax_checkpoint(UpperCAmelCase__ )
    UpperCAmelCase									=					convert_tax_to_pytorch(UpperCAmelCase__ ,				num_layers=config.num_layers ,				is_encoder_only=UpperCAmelCase__ )
    UpperCAmelCase									=					make_state_dict(UpperCAmelCase__ ,				UpperCAmelCase__ )
    model.load_state_dict(UpperCAmelCase__ ,				strict=UpperCAmelCase__ )




def     lowercase__						(					lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					bool = False ) ->      List[str]:






    """simple docstring"""




    UpperCAmelCase									=					TaConfig.from_json_file(UpperCAmelCase__ )
    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:
        UpperCAmelCase									=					TaEncoderModel(UpperCAmelCase__ )
    else:
        UpperCAmelCase									=					TaForConditionalGeneration(UpperCAmelCase__ )

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

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

    # Verify that we can load the checkpoint.
    model.from_pretrained(UpperCAmelCase__ )
    print('Done' )


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_      					=						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
   )
   SCREAMING_SNAKE_CASE_      					=						parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
   )





"""simple docstring"""

import sys
import turtle




def     lowercase__						(					lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ) ->      tuple[float, float]:






    """simple docstring"""




    return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2




def     lowercase__						(					lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					int ,				) ->      None:






    """simple docstring"""




    my_pen.up()
    my_pen.goto(vertexa[0] ,				vertexa[1] )
    my_pen.down()
    my_pen.goto(vertexa[0] ,				vertexa[1] )
    my_pen.goto(vertexa[0] ,				vertexa[1] )
    my_pen.goto(vertexa[0] ,				vertexa[1] )

    if depth == 0:
        return

    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )
    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )
    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )


if __name__ == "__main__":
   if len(sys.argv) != 2:
      raise ValueError(
          '''Correct format for using this script: '''
          '''python fractals.py <int:depth_for_fractal>'''
      )
   SCREAMING_SNAKE_CASE_    					=						turtle.Turtle()
   my_pen.ht()
   my_pen.speed(5)
   my_pen.pencolor('''red''')

   SCREAMING_SNAKE_CASE_    					=						[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
   triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))



