


import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
lowercase_			=  {
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Dict				)							->				Tuple:





    '''simple docstring'''






    A__   				=       list(s_dict.keys()				)
    for key in keys:
        A__   				=       R'.*/layers_(\d+)'
        A__   				=       key
        if re.match(a_   ,      a_				):
            A__   				=       re.sub(R'layers_(\d+)'   ,      R'block/\1/layer'   ,      a_				)

        A__   				=       R'(encoder|decoder)\/'

        if re.match(a_   ,      a_				):
            A__   				=       re.match(a_   ,      a_				).groups()
            if groups[0] == "encoder":
                A__   				=       re.sub(R'/mlp/'   ,      R'/1/mlp/'   ,      a_				)
                A__   				=       re.sub(R'/pre_mlp_layer_norm/'   ,      R'/1/layer_norm/'   ,      a_				)

            elif groups[0] == "decoder":
                A__   				=       re.sub(R'/mlp/'   ,      R'/2/mlp/'   ,      a_				)
                A__   				=       re.sub(R'/pre_mlp_layer_norm/'   ,      R'/2/layer_norm/'   ,      a_				)

        # 2. Convert other classic mappings
        for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
            if old_key in new_key:
                A__   				=       new_key.replace(a_   ,      a_				)

        print(f'{key} -> {new_key}'				)
        A__   				=       s_dict.pop(a_				)

    if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        A__   				=       s_dict[
            'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
        ].T
    if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        A__   				=       s_dict[
            'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
        ].T

    # 3. Take extra care of the EXPERTS layer
    for key in list(s_dict.keys()				):
        if "expert" in key:
            A__   				=       s_dict[key].shape[0]
            A__   				=       s_dict[key]
            for idx in range(a_				):
                A__   				=       expert_weihts[idx]
                print(f'{key} -> {key.replace("expert/"   ,      "nested fstring"				)}'				)

            s_dict.pop(a_				)

    return s_dict


lowercase_			=  {
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				int:





    '''simple docstring'''






    import regex as re

    with open(a_   ,      'r'				) as f:
        A__   				=       f.read()

    A__   				=       re.findall(R'(.*) = ([0-9.]*)'   ,      a_				)
    A__   				=       {}
    for param, value in regex_match:
        if param in GIN_TO_CONFIG_MAPPING and value != "":
            A__   				=       float(a_				) if '.' in value else int(a_				)

    A__   				=       re.findall(R'(.*activations) = \(\'(.*)\',\)'   ,      a_				)[0]
    A__   				=       str(activation[1]				)

    A__   				=       num_experts
    A__   				=       SwitchTransformersConfig(**a_				)
    return config
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     str=None   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]="./"   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]=8				)							->				Tuple:





    '''simple docstring'''






    print(f'Loading flax weights from : {flax_checkpoint_path}'				)
    A__   				=       checkpoints.load_tax_checkpoint(a_				)

    if gin_file is not None:
        A__   				=       convert_gin_to_config(a_   ,      a_				)
    else:
        A__   				=       SwitchTransformersConfig.from_pretrained(a_				)

    A__   				=       SwitchTransformersForConditionalGeneration(a_				)

    A__   				=       flax_params['target']
    A__   				=       flatten_dict(a_   ,      sep='/'				)
    A__   				=       rename_keys(a_				)
    A__   				=       unflatten_dict(a_   ,      sep='/'				)

    # Load the flax params in the PT model
    load_flax_weights_in_pytorch_model(a_   ,      a_				)

    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    pt_model.save_pretrained(a_				)


if __name__ == "__main__":
     lowercase_			=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--switch_t5x_checkpoint_path",
         default=None,
         type=str,
         required=True,
         help=(
             "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
             " model architecture. If not provided, a `gin_file` has to be provided."
         ),
     )
     parser.add_argument(
         "--gin_file",
         default=None,
         type=str,
         required=False,
         help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
     )
     parser.add_argument(
         "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
     )
     parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
     lowercase_			=  parser.parse_args()
     convert_flax_checkpoint_to_pytorch(
         args.switch_tax_checkpoint_path,
         args.config_name,
         args.gin_file,
         args.pytorch_dump_folder_path,
         args.num_experts,
     )




from __future__ import annotations

from scipy.special import comb  # type: ignore


class 			A						:







    """simple docstring"""





    def __init__(				self			:	Any,lowercase_			:	list[tuple[float, float]]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       list_of_points
        # Degree determines the flexibility of the curve.
        # Degree = 1 will produce a straight line.
        A__   				=       len(lowercase_		) - 1


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	float		)->   list[float]:





        '''simple docstring'''







        assert 0 <= t <= 1, "Time t must be between 0 and 1."
        A__   				=       []
        for i in range(len(self.list_of_points		)		):
            # basis function for each i
            output_values.append(
                comb(self.degree,lowercase_		) * ((1 - t) ** (self.degree - i)) * (t**i)		)
        # the basis must sum up to 1 for it to produce a valid Bezier curve.
        assert round(sum(lowercase_		),5		) == 1
        return output_values


    def 						snake_case__					(				self			:	str,lowercase_			:	float		)->   tuple[float, float]:





        '''simple docstring'''







        assert 0 <= t <= 1, "Time t must be between 0 and 1."

        A__   				=       self.basis_function(lowercase_		)
        A__   				=       0.0
        A__   				=       0.0
        for i in range(len(self.list_of_points		)		):
            # For all points, sum up the product of i-th basis function and i-th point.
            x += basis_function[i] * self.list_of_points[i][0]
            y += basis_function[i] * self.list_of_points[i][1]
        return (x, y)


    def 						snake_case__					(				self			:	str,lowercase_			:	float = 0.01		)->   str:





        '''simple docstring'''







        from matplotlib import pyplot as plt  # type: ignore

        A__   				=       []  # x coordinates of points to plot
        A__   				=       []  # y coordinates of points to plot

        A__   				=       0.0
        while t <= 1:
            A__   				=       self.bezier_curve_function(lowercase_		)
            to_plot_x.append(value[0]		)
            to_plot_y.append(value[1]		)
            t += step_size

        A__   				=       [i[0] for i in self.list_of_points]
        A__   				=       [i[1] for i in self.list_of_points]

        plt.plot(
            lowercase_,lowercase_,color='blue',label='Curve of Degree ' + str(self.degree		),)
        plt.scatter(lowercase_,lowercase_,color='red',label='Control Points'		)
        plt.legend()
        plt.show()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
     BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
     BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

