


'''simple docstring'''



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__lowercase     :						str	=     logging.get_logger(__name__)

__lowercase     :						int	=     {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "gptj"
	A_           =   {
	    "max_position_embeddings": "n_positions",
	    "hidden_size": "n_embd",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}




	def __init__(       self						,    __a=5_0400						,    __a=2048						,    __a=4096						,    __a=28						,    __a=16						,    __a=64						,    __a=None						,    __a="gelu_new"						,    __a=0.0						,    __a=0.0						,    __a=0.0						,    __a=1E-5						,    __a=0.02						,    __a=True						,    __a=5_0256						,    __a=5_0256						,    __a=False						,    **__a						,    ):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  vocab_size
						__a      :	Optional[int]    =  n_positions
						__a      :	Any    =  n_embd
						__a      :	str    =  n_layer
						__a      :	Union[str, Any]    =  n_head
						__a      :	Optional[int]    =  n_inner
						__a      :	Dict    =  rotary_dim
						__a      :	Dict    =  activation_function
						__a      :	Dict    =  resid_pdrop
						__a      :	Union[str, Any]    =  embd_pdrop
						__a      :	List[Any]    =  attn_pdrop
						__a      :	List[Any]    =  layer_norm_epsilon
						__a      :	Dict    =  initializer_range
						__a      :	List[str]    =  use_cache

						__a      :	Dict    =  bos_token_id
						__a      :	Tuple    =  eos_token_id

						super().__init__(
						    bos_token_id=__a						,    eos_token_id=__a						,    tie_word_embeddings=__a						,    **__a							)





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def __init__(       self						,    __a						,    __a = "default"						,    __a = None						,    __a = False						,    ):

						'''simple docstring'''





						super().__init__(__a						,    task=__a						,    patching_specs=__a						,    use_past=__a							)
						if not getattr(self._config						,    'pad_token_id'						,    __a							):
											# TODO: how to do that better?
											__a      :	List[str]    =  0




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}							)
						if self.use_past:
											self.fill_with_past_key_values_(__a						,    direction='inputs'							)
											__a      :	List[str]    =  {0: 'batch', 1: 'past_sequence + sequence'}
						else:
											__a      :	List[Any]    =  {0: 'batch', 1: 'sequence'}

						return common_inputs




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return self._config.n_layer




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return self._config.n_head




	def 				__UpperCAmelCase							(       self						,    __a						,    __a = -1						,    __a = -1						,    __a = False						,    __a = None						,    ):

						'''simple docstring'''





						__a      :	List[str]    =  super(__a						,    self							).generate_dummy_inputs(
						    __a						,    batch_size=__a						,    seq_length=__a						,    is_pair=__a						,    framework=__a							)

						# We need to order the input in the way they appears in the forward()
						__a      :	str    =  OrderedDict({'input_ids': common_inputs['input_ids']}							)

						# Need to add the past_keys
						if self.use_past:
											if not is_torch_available():
																raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'							)
											else:
																import torch

																__a   ,    __a      :	Dict    =  common_inputs['input_ids'].shape
																# Not using the same length for past_key_values
																__a      :	List[str]    =  seqlen + 2
																__a      :	Dict    =  (
																    batch,
																    self.num_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // self.num_attention_heads,
																)
																__a      :	Optional[int]    =  [
																    (torch.zeros(__a							), torch.zeros(__a							)) for _ in range(self.num_layers							)
																]

						__a      :	Union[str, Any]    =  common_inputs['attention_mask']
						if self.use_past:
											__a      :	List[str]    =  ordered_inputs['attention_mask'].dtype
											__a      :	Union[str, Any]    =  torch.cat(
											    [ordered_inputs['attention_mask'], torch.ones(__a						,    __a						,    dtype=__a							)]						,    dim=1							)

						return ordered_inputs





	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return 13







'''simple docstring'''



import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
	# Create universe of discourse in Python using linspace ()
	__lowercase     :						int	=     np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

	# Create two fuzzy sets by defining any membership function
	# (trapmf(), gbellmf(), gaussmf(), etc).
	__lowercase     :						Any	=     [0, 25, 50]
	__lowercase     :						int	=     [25, 50, 75]
	__lowercase     :						List[str]	=     fuzz.membership.trimf(X, abca)
	__lowercase     :						Any	=     fuzz.membership.trimf(X, abca)

	# Compute the different operations using inbuilt functions.
	__lowercase     :						List[Any]	=     np.ones(75)
	__lowercase     :						Any	=     np.zeros((75,))
	# 1. Union = max(µA(x), µB(x))
	__lowercase     :						int	=     fuzz.fuzzy_or(X, young, X, middle_aged)[1]
	# 2. Intersection = min(µA(x), µB(x))
	__lowercase     :						int	=     fuzz.fuzzy_and(X, young, X, middle_aged)[1]
	# 3. Complement (A) = (1- min(µA(x))
	__lowercase     :						str	=     fuzz.fuzzy_not(young)
	# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
	__lowercase     :						List[Any]	=     fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
	# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
	__lowercase     :						Optional[Any]	=     young + middle_aged - (young * middle_aged)
	# 6. Algebraic Product = (µA(x) * µB(x))
	__lowercase     :						str	=     young * middle_aged
	# 7. Bounded Sum = min[1,(µA(x), µB(x))]
	__lowercase     :						Optional[Any]	=     fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
	# 8. Bounded difference = min[0,(µA(x), µB(x))]
	__lowercase     :						Union[str, Any]	=     fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

	# max-min composition
	# max-product composition

	# Plot each set A, set B and each operation result using plot() and subplot().
	from matplotlib import pyplot as plt

	plt.figure()

	plt.subplot(4, 3, 1)
	plt.plot(X, young)
	plt.title('Young')
	plt.grid(True)

	plt.subplot(4, 3, 2)
	plt.plot(X, middle_aged)
	plt.title('Middle aged')
	plt.grid(True)

	plt.subplot(4, 3, 3)
	plt.plot(X, union)
	plt.title('union')
	plt.grid(True)

	plt.subplot(4, 3, 4)
	plt.plot(X, intersection)
	plt.title('intersection')
	plt.grid(True)

	plt.subplot(4, 3, 5)
	plt.plot(X, complement_a)
	plt.title('complement_a')
	plt.grid(True)

	plt.subplot(4, 3, 6)
	plt.plot(X, difference)
	plt.title('difference a/b')
	plt.grid(True)

	plt.subplot(4, 3, 7)
	plt.plot(X, alg_sum)
	plt.title('alg_sum')
	plt.grid(True)

	plt.subplot(4, 3, 8)
	plt.plot(X, alg_product)
	plt.title('alg_product')
	plt.grid(True)

	plt.subplot(4, 3, 9)
	plt.plot(X, bdd_sum)
	plt.title('bdd_sum')
	plt.grid(True)

	plt.subplot(4, 3, 10)
	plt.plot(X, bdd_difference)
	plt.title('bdd_difference')
	plt.grid(True)

	plt.subplots_adjust(hspace=0.5)
	plt.show()



