


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """instructblip_vision_model"""


     def __init__(	self  ,       __A=1408  ,       __A=6144  ,       __A=39  ,       __A=16  ,       __A=224  ,       __A=14  ,       __A="gelu"  ,       __A=1E-6  ,       __A=0.0  ,       __A=1E-10  ,       __A=True  ,       **__A  ,       ):
        super().__init__(**__A    )

        __a					=  hidden_size
        __a					=  intermediate_size
        __a					=  num_hidden_layers
        __a					=  num_attention_heads
        __a					=  patch_size
        __a					=  image_size
        __a					=  initializer_range
        __a					=  attention_dropout
        __a					=  layer_norm_eps
        __a					=  hidden_act
        __a					=  qkv_bias


     @classmethod
     def 	snake_case_  (	cls  ,       __A  ,       **__A    ):
        cls._set_token_in_kwargs(__A    )

        __a	,    __a					=  cls.get_config_dict(__A  ,       **__A    )

        # get the vision config dict if we are loading from InstructBlipConfig
        if config_dict.get("""model_type"""    ) == "instructblip":
           __a					=  config_dict["""vision_config"""]

        if "model_type" in config_dict and hasattr(cls  ,       """model_type"""    ) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
               f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''    )

        return cls.from_dict(__A  ,       **__A    )







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """instructblip_qformer"""


     def __init__(	self  ,       __A=30522  ,       __A=768  ,       __A=12  ,       __A=12  ,       __A=3072  ,       __A="gelu"  ,       __A=0.1  ,       __A=0.1  ,       __A=512  ,       __A=0.02  ,       __A=1E-12  ,       __A=0  ,       __A="absolute"  ,       __A=2  ,       __A=1408  ,       **__A  ,       ):
        super().__init__(pad_token_id=__A  ,       **__A    )

        __a					=  vocab_size
        __a					=  hidden_size
        __a					=  num_hidden_layers
        __a					=  num_attention_heads
        __a					=  hidden_act
        __a					=  intermediate_size
        __a					=  hidden_dropout_prob
        __a					=  attention_probs_dropout_prob
        __a					=  max_position_embeddings
        __a					=  initializer_range
        __a					=  layer_norm_eps
        __a					=  position_embedding_type
        __a					=  cross_attention_frequency
        __a					=  encoder_hidden_size


     @classmethod
     def 	snake_case_  (	cls  ,       __A  ,       **__A    ):
        cls._set_token_in_kwargs(__A    )

        __a	,    __a					=  cls.get_config_dict(__A  ,       **__A    )

        # get the qformer config dict if we are loading from InstructBlipConfig
        if config_dict.get("""model_type"""    ) == "instructblip":
           __a					=  config_dict["""qformer_config"""]

        if "model_type" in config_dict and hasattr(cls  ,       """model_type"""    ) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
               f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''    )

        return cls.from_dict(__A  ,       **__A    )







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """instructblip"""
     _lowerCamelCase   =      True


     def __init__(	self  ,       __A=None  ,       __A=None  ,       __A=None  ,       __A=32  ,       **__A    ):
        super().__init__(**__A    )

        if vision_config is None:
           __a					=  {}
           logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values."""    )

        if qformer_config is None:
           __a					=  {}
           logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."""    )

        if text_config is None:
           __a					=  {}
           logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""    )

        __a					=  InstructBlipVisionConfig(**__A    )
        __a					=  InstructBlipQFormerConfig(**__A    )
        __a					=  text_config["""model_type"""] if """model_type""" in text_config else """opt"""
        __a					=  CONFIG_MAPPING[text_model_type](**__A    )

        __a					=  self.text_config.tie_word_embeddings
        __a					=  self.text_config.is_encoder_decoder

        __a					=  num_query_tokens
        __a					=  self.vision_config.hidden_size
        __a					=  self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
        __a					=  1.0
        __a					=  0.02


     @classmethod
     def 	snake_case_  (	cls  ,       __A  ,       __A  ,       __A  ,       **__A  ,       ):
        return cls(
            vision_config=vision_config.to_dict()  ,       qformer_config=qformer_config.to_dict()  ,       text_config=text_config.to_dict()  ,       **__A  ,       )


     def 	snake_case_  (	self    ):
        __a					=  copy.deepcopy(self.__dict__    )
        __a					=  self.vision_config.to_dict()
        __a					=  self.qformer_config.to_dict()
        __a					=  self.text_config.to_dict()
        __a					=  self.__class__.model_type
        return output










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

