
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 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__="attention"		):
      UpperCamelCase__     : List[str]											=					params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      UpperCamelCase__     : List[Any]											=					params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      UpperCamelCase__     : Dict											=					params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      UpperCamelCase__     : Any											=					params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__=False		):
      if split_mlp_wi:
            UpperCamelCase__     : Union[str, Any]											=					params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            UpperCamelCase__     : Optional[Any]											=					params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            UpperCamelCase__     : List[Any]											=					(wi_a, wi_a)
      else:
            UpperCamelCase__     : Union[str, Any]											=					params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

      UpperCamelCase__     : Optional[Any]											=					params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		*, UpperCamelCase__			,		UpperCamelCase__		):
      UpperCamelCase__     : Any											=					traverse_util.flatten_dict(variables['''target''']		)
      UpperCamelCase__     : Tuple											=					{'''/'''.join(lowercase_		): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      UpperCamelCase__     : List[str]											=					'''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:'''			,		lowercase_		)

      UpperCamelCase__     : List[str]											=					collections.OrderedDict()

      # Shared embeddings.
      UpperCamelCase__     : List[str]											=					old['''token_embedder/embedding''']

      # Encoder.
      for i in range(lowercase_		):
            # Block i, layer 0 (Self Attention).
            UpperCamelCase__     : List[Any]											=					tax_layer_norm_lookup(lowercase_			,		lowercase_			,		'''encoder'''			,		'''pre_attention_layer_norm'''		)
            UpperCamelCase__     : List[str]											=					tax_attention_lookup(lowercase_			,		lowercase_			,		'''encoder'''			,		'''attention'''		)
            UpperCamelCase__     : List[str]											=					layer_norm
            UpperCamelCase__     : Any											=					k.T
            UpperCamelCase__     : int											=					o.T
            UpperCamelCase__     : int											=					q.T
            UpperCamelCase__     : int											=					v.T

            # Block i, layer 1 (MLP).
            UpperCamelCase__     : List[str]											=					tax_layer_norm_lookup(lowercase_			,		lowercase_			,		'''encoder'''			,		'''pre_mlp_layer_norm'''		)
            UpperCamelCase__     : List[Any]											=					tax_mlp_lookup(lowercase_			,		lowercase_			,		'''encoder'''			,		lowercase_		)
            UpperCamelCase__     : Any											=					layer_norm
            if split_mlp_wi:
                  UpperCamelCase__     : Union[str, Any]											=					wi[0].T
                  UpperCamelCase__     : List[Any]											=					wi[1].T
            else:
                  UpperCamelCase__     : Optional[Any]											=					wi.T
            UpperCamelCase__     : Any											=					wo.T

      UpperCamelCase__     : List[str]											=					old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      UpperCamelCase__     : str											=					old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(lowercase_		):
                  # Block i, layer 0 (Self Attention).
                  UpperCamelCase__     : List[str]											=					tax_layer_norm_lookup(lowercase_			,		lowercase_			,		'''decoder'''			,		'''pre_self_attention_layer_norm'''		)
                  UpperCamelCase__     : List[Any]											=					tax_attention_lookup(lowercase_			,		lowercase_			,		'''decoder'''			,		'''self_attention'''		)
                  UpperCamelCase__     : Optional[int]											=					layer_norm
                  UpperCamelCase__     : Dict											=					k.T
                  UpperCamelCase__     : Dict											=					o.T
                  UpperCamelCase__     : Optional[Any]											=					q.T
                  UpperCamelCase__     : List[Any]											=					v.T

                  # Block i, layer 1 (Cross Attention).
                  UpperCamelCase__     : List[Any]											=					tax_layer_norm_lookup(lowercase_			,		lowercase_			,		'''decoder'''			,		'''pre_cross_attention_layer_norm'''		)
                  UpperCamelCase__     : Any											=					tax_attention_lookup(lowercase_			,		lowercase_			,		'''decoder'''			,		'''encoder_decoder_attention'''		)
                  UpperCamelCase__     : str											=					layer_norm
                  UpperCamelCase__     : Dict											=					k.T
                  UpperCamelCase__     : Optional[int]											=					o.T
                  UpperCamelCase__     : Dict											=					q.T
                  UpperCamelCase__     : str											=					v.T

                  # Block i, layer 2 (MLP).
                  UpperCamelCase__     : List[str]											=					tax_layer_norm_lookup(lowercase_			,		lowercase_			,		'''decoder'''			,		'''pre_mlp_layer_norm'''		)
                  UpperCamelCase__     : Dict											=					tax_mlp_lookup(lowercase_			,		lowercase_			,		'''decoder'''			,		lowercase_		)
                  UpperCamelCase__     : List[str]											=					layer_norm
                  if split_mlp_wi:
                        UpperCamelCase__     : List[Any]											=					wi[0].T
                        UpperCamelCase__     : List[Any]											=					wi[1].T
                  else:
                        UpperCamelCase__     : Optional[int]											=					wi.T
                  UpperCamelCase__     : int											=					wo.T

            UpperCamelCase__     : List[str]											=					old['''decoder/decoder_norm/scale''']
            UpperCamelCase__     : str											=					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__     : List[Any]											=					old['''decoder/logits_dense/kernel'''].T

      return new


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      UpperCamelCase__     : Tuple											=					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__     : Optional[int]											=					state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  UpperCamelCase__     : Optional[int]											=					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__     : Optional[Any]											=					state_dict['''shared.weight''']

      return state_dict


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      UpperCamelCase__     : List[Any]											=					checkpoints.load_tax_checkpoint(lowercase_		)
      UpperCamelCase__     : Optional[Any]											=					convert_tax_to_pytorch(lowercase_			,		num_layers=config.num_layers			,		is_encoder_only=lowercase_		)
      UpperCamelCase__     : Tuple											=					make_state_dict(lowercase_			,		lowercase_		)
      model.load_state_dict(lowercase_			,		strict=lowercase_		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__ = False		):
      UpperCamelCase__     : Any											=					TaConfig.from_json_file(lowercase_		)
      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__     : str											=					TaEncoderModel(lowercase_		)
      else:
            UpperCamelCase__     : Optional[Any]											=					TaForConditionalGeneration(lowercase_		)

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

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

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


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









from __future__ import annotations

from scipy.special import comb  # type: ignore







class       __A							:




      """simple docstring"""







      def __init__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  str						=		list_of_points
             # Degree determines the flexibility of the curve.
             # Degree = 1 will produce a straight line.
             _lowerCamelCase				:  List[Any]						=		len(a__) - 1


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             assert 0 <= t <= 1, "Time t must be between 0 and 1."
             _lowerCamelCase				:  list[float]						=		[]
             for i in range(len(self.list_of_points)):
                    # basis function for each i
                    output_values.append(
                        comb(self.degree   ,				a__) * ((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(a__)   ,				5) == 1
             return output_values


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





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

             _lowerCamelCase				:  Any						=		self.basis_function(a__)
             _lowerCamelCase				:  List[Any]						=		0.0
             _lowerCamelCase				:  List[Any]						=		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   ,				a__ = 0.01):



             """simple docstring"""





             from matplotlib import pyplot as plt  # type: ignore

             _lowerCamelCase				:  list[float]						=		[]  # x coordinates of points to plot
             _lowerCamelCase				:  list[float]						=		[]  # y coordinates of points to plot

             _lowerCamelCase				:  List[Any]						=		0.0
             while t <= 1:
                    _lowerCamelCase				:  Tuple						=		self.bezier_curve_function(a__)
                    to_plot_x.append(value[0])
                    to_plot_y.append(value[1])
                    t += step_size

             _lowerCamelCase				:  List[Any]						=		[i[0] for i in self.list_of_points]
             _lowerCamelCase				:  Union[str, Any]						=		[i[1] for i in self.list_of_points]

             plt.plot(
                 a__   ,				a__   ,				color='''blue'''   ,				label='''Curve of Degree ''' + str(self.degree)   ,				)
             plt.scatter(a__   ,				a__   ,				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

