


"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


SCREAMING_SNAKE_CASE :    List[Any]									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Union[str, Any]									=				{
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='blenderbot-small'
		lowerCamelCase__				=['past_key_values']
		lowerCamelCase__				={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



		def __init__(self				,       a_=5_02_65				,       a_=5_12				,       a_=8				,       a_=20_48				,       a_=16				,       a_=8				,       a_=20_48				,       a_=16				,       a_=0.0				,       a_=0.0				,       a_=True				,       a_=True				,       a_="gelu"				,       a_=5_12				,       a_=0.1				,       a_=0.0				,       a_=0.0				,       a_=0.02				,       a_=1				,       a_=False				,       a_=0				,       a_=1				,       a_=2				,       a_=2				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						Any						=				vocab_size
			__snake_case	:						Any						=				max_position_embeddings
			__snake_case	:						Tuple						=				d_model
			__snake_case	:						str						=				encoder_ffn_dim
			__snake_case	:						Optional[int]						=				encoder_layers
			__snake_case	:						int						=				encoder_attention_heads
			__snake_case	:						Optional[Any]						=				decoder_ffn_dim
			__snake_case	:						List[str]						=				decoder_layers
			__snake_case	:						List[str]						=				decoder_attention_heads
			__snake_case	:						Union[str, Any]						=				dropout
			__snake_case	:						str						=				attention_dropout
			__snake_case	:						List[Any]						=				activation_dropout
			__snake_case	:						Union[str, Any]						=				activation_function
			__snake_case	:						List[Any]						=				init_std
			__snake_case	:						List[str]						=				encoder_layerdrop
			__snake_case	:						List[Any]						=				decoder_layerdrop
			__snake_case	:						Optional[int]						=				use_cache
			__snake_case	:						Optional[Any]						=				encoder_layers
			__snake_case	:						List[Any]						=				scale_embedding  # scale factor will be sqrt(d_model) if True

			super().__init__(
			    pad_token_id=a_				,       bos_token_id=a_				,       eos_token_id=a_				,       is_encoder_decoder=a_				,       decoder_start_token_id=a_				,       forced_eos_token_id=a_				,       **a_				,       )



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			if self.task in ["default", "seq2seq-lm"]:
				__snake_case	:						Tuple						=				OrderedDict(
				    [
				        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
				        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
				    ]					)

				if self.use_past:
					__snake_case	:						Optional[Any]						=				{0: '''batch'''}
					__snake_case	:						Tuple						=				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
				else:
					__snake_case	:						Dict						=				{0: '''batch''', 1: '''decoder_sequence'''}
					__snake_case	:						Optional[Any]						=				{0: '''batch''', 1: '''decoder_sequence'''}

				if self.use_past:
					self.fill_with_past_key_values_(a_				,       direction='''inputs'''					)
			elif self.task == "causal-lm":
				# TODO: figure this case out.
				__snake_case	:						str						=				OrderedDict(
				    [
				        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
				        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
				    ]					)
				if self.use_past:
					__snake_case	,							__snake_case	:						Tuple						=				self.num_layers
					for i in range(a_					):
						__snake_case	:						int						=				{0: '''batch''', 2: '''past_sequence + sequence'''}
						__snake_case	:						int						=				{0: '''batch''', 2: '''past_sequence + sequence'''}
			else:
				__snake_case	:						Dict						=				OrderedDict(
				    [
				        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
				        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
				        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
				        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
				    ]					)

			return common_inputs



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			if self.task in ["default", "seq2seq-lm"]:
				__snake_case	:						str						=				super().outputs
			else:
				__snake_case	:						List[Any]						=				super(a_				,       self					).outputs
				if self.use_past:
					__snake_case	,							__snake_case	:						int						=				self.num_layers
					for i in range(a_					):
						__snake_case	:						Tuple						=				{0: '''batch''', 2: '''past_sequence + sequence'''}
						__snake_case	:						Optional[int]						=				{0: '''batch''', 2: '''past_sequence + sequence'''}
			return common_outputs



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = -1				,       a_ = -1				,       a_ = False				,       a_ = None				,       ):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    a_				,       a_				,       a_				,       a_				,       a_					)

			# Generate decoder inputs
			__snake_case	:						str						=				seq_length if not self.use_past else 1
			__snake_case	:						Union[str, Any]						=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    a_				,       a_				,       a_				,       a_				,       a_					)
			__snake_case	:						List[Any]						=				{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
			__snake_case	:						Dict						=				dict(**a_				,       **a_					)

			if self.use_past:
				if not is_torch_available():
					raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
				else:
					import torch
				__snake_case	,							__snake_case	:						int						=				common_inputs['''input_ids'''].shape
				__snake_case	:						Union[str, Any]						=				common_inputs['''decoder_input_ids'''].shape[1]
				__snake_case	,							__snake_case	:						Union[str, Any]						=				self.num_attention_heads
				__snake_case	:						Dict						=				(
				    batch,
				    num_encoder_attention_heads,
				    encoder_seq_length,
				    self._config.hidden_size // num_encoder_attention_heads,
				)
				__snake_case	:						Tuple						=				decoder_seq_length + 3
				__snake_case	:						Dict						=				(
				    batch,
				    num_decoder_attention_heads,
				    decoder_past_length,
				    self._config.hidden_size // num_decoder_attention_heads,
				)

				__snake_case	:						Optional[Any]						=				torch.cat(
				    [common_inputs['''decoder_attention_mask'''], torch.ones(a_				,       a_					)]				,       dim=1					)

				__snake_case	:						int						=				[]
				# If the number of encoder and decoder layers are present in the model configuration, both are considered
				__snake_case	,							__snake_case	:						str						=				self.num_layers
				__snake_case	:						Dict						=				min(a_				,       a_					)
				__snake_case	:						List[str]						=				max(a_				,       a_					) - min_num_layers
				__snake_case	:						Optional[int]						=				'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

				for _ in range(a_					):
					common_inputs["past_key_values"].append(
					    (
					        torch.zeros(a_					),
					        torch.zeros(a_					),
					        torch.zeros(a_					),
					        torch.zeros(a_					),
					    )					)
				# TODO: test this.
				__snake_case	:						Optional[Any]						=				encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
				for _ in range(a_				,       a_					):
					common_inputs["past_key_values"].append((torch.zeros(a_					), torch.zeros(a_					))					)
			return common_inputs



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = -1				,       a_ = -1				,       a_ = False				,       a_ = None				,       ):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    a_				,       a_				,       a_				,       a_				,       a_					)

			if self.use_past:
				if not is_torch_available():
					raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
				else:
					import torch
				__snake_case	,							__snake_case	:						Dict						=				common_inputs['''input_ids'''].shape
				# Not using the same length for past_key_values
				__snake_case	:						Optional[int]						=				seqlen + 2
				__snake_case	,							__snake_case	:						Dict						=				self.num_layers
				__snake_case	,							__snake_case	:						Optional[Any]						=				self.num_attention_heads
				__snake_case	:						Optional[int]						=				(
				    batch,
				    num_encoder_attention_heads,
				    past_key_values_length,
				    self._config.hidden_size // num_encoder_attention_heads,
				)

				__snake_case	:						str						=				common_inputs['''attention_mask'''].dtype
				__snake_case	:						Optional[int]						=				torch.cat(
				    [common_inputs['''attention_mask'''], torch.ones(a_				,       a_				,       dtype=a_					)]				,       dim=1					)
				__snake_case	:						List[str]						=				[
				    (torch.zeros(a_					), torch.zeros(a_					)) for _ in range(a_					)
				]
			return common_inputs



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = -1				,       a_ = -1				,       a_ = False				,       a_ = None				,       ):





			'''simple docstring'''

			__snake_case	:						Dict						=				compute_effective_axis_dimension(
			    a_				,       fixed_dimension=OnnxConfig.default_fixed_batch				,       num_token_to_add=0					)

			# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
			__snake_case	:						List[Any]						=				tokenizer.num_special_tokens_to_add(a_					)
			__snake_case	:						Union[str, Any]						=				compute_effective_axis_dimension(
			    a_				,       fixed_dimension=OnnxConfig.default_fixed_sequence				,       num_token_to_add=a_					)

			# Generate dummy inputs according to compute batch and sequence
			__snake_case	:						Optional[int]						=				[''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
			__snake_case	:						Union[str, Any]						=				dict(tokenizer(a_				,       return_tensors=a_					)					)
			return common_inputs



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = -1				,       a_ = -1				,       a_ = False				,       a_ = None				,       ):





			'''simple docstring'''

			if self.task in ["default", "seq2seq-lm"]:
				__snake_case	:						str						=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
				    a_				,       batch_size=a_				,       seq_length=a_				,       is_pair=a_				,       framework=a_					)

			elif self.task == "causal-lm":
				__snake_case	:						str						=				self._generate_dummy_inputs_for_causal_lm(
				    a_				,       batch_size=a_				,       seq_length=a_				,       is_pair=a_				,       framework=a_					)
			else:
				__snake_case	:						int						=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    a_				,       batch_size=a_				,       seq_length=a_				,       is_pair=a_				,       framework=a_					)

			return common_inputs



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			if self.task in ["default", "seq2seq-lm"]:
				__snake_case	:						Tuple						=				super()._flatten_past_key_values_(a_				,       a_				,       a_				,       a_					)
			else:
				__snake_case	:						str						=				super(a_				,       self					)._flatten_past_key_values_(
				    a_				,       a_				,       a_				,       a_					)









"""simple docstring"""
from __future__ import annotations

from scipy.special import comb  # type: ignore



class 			_UpperCAmelCase :






		'''simple docstring'''

		def __init__(self				,       a_					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			assert 0 <= t <= 1, "Time t must be between 0 and 1."
			__snake_case	:						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 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

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

			__snake_case	:						List[str]						=				self.basis_function(a_					)
			__snake_case	:						str						=				0.0
			__snake_case	:						Union[str, 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 				SCREAMING_SNAKE_CASE  (self				,       a_ = 0.01					):





			'''simple docstring'''

			from matplotlib import pyplot as plt  # type: ignore

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

			__snake_case	:						int						=				0.0
			while t <= 1:
				__snake_case	:						Union[str, Any]						=				self.bezier_curve_function(a_					)
				to_plot_x.append(value[0]					)
				to_plot_y.append(value[1]					)
				t += step_size

			__snake_case	:						List[Any]						=				[i[0] for i in self.list_of_points]
			__snake_case	:						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





