



from __future__ import annotations

from scipy.special import comb  # type: ignore



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""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		,       lowerCamelCase_		) * ((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(lowerCamelCase_		)		,       5		) == 1
						return output_values




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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

						a					     =   self.basis_function(lowerCamelCase_		)
						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      UpperCamelCase_       (self		,       lowerCamelCase_ = 0.01		):
						"""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(lowerCamelCase_		)
											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(
						    lowerCamelCase_		,       lowerCamelCase_		,       color="blue"		,       label="Curve of Degree " + str(self.degree		)		,       )
						plt.scatter(lowerCamelCase_		,       lowerCamelCase_		,       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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase: str      			=		logging.get_logger(__name__)

_lowercase: List[str]      			=		{
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							"trocr"
	__A							=							["past_key_values"]
	__A							=							{
	    "num_attention_heads": "decoder_attention_heads",
	    "hidden_size": "d_model",
	    "num_hidden_layers": "decoder_layers",
	}




	def __init__(self		,       lowerCamelCase_=50265		,       lowerCamelCase_=1024		,       lowerCamelCase_=12		,       lowerCamelCase_=16		,       lowerCamelCase_=4096		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=512		,       lowerCamelCase_=0.1		,       lowerCamelCase_=0.0		,       lowerCamelCase_=0.0		,       lowerCamelCase_=2		,       lowerCamelCase_=0.02		,       lowerCamelCase_=0.0		,       lowerCamelCase_=True		,       lowerCamelCase_=False		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=1		,       lowerCamelCase_=0		,       lowerCamelCase_=2		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   vocab_size
						a					     =   d_model
						a					     =   decoder_layers
						a					     =   decoder_attention_heads
						a					     =   decoder_ffn_dim
						a					     =   activation_function
						a					     =   max_position_embeddings
						a					     =   dropout
						a					     =   attention_dropout
						a					     =   activation_dropout
						a					     =   init_std
						a					     =   decoder_layerdrop
						a					     =   use_cache
						a					     =   scale_embedding
						a					     =   use_learned_position_embeddings
						a					     =   layernorm_embedding

						super().__init__(
						    pad_token_id=lowerCamelCase_		,       bos_token_id=lowerCamelCase_		,       eos_token_id=lowerCamelCase_		,       decoder_start_token_id=lowerCamelCase_		,       **lowerCamelCase_		,       )
