

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


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """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 	lowercase    (				_lowercase							):


		"""simple docstring"""




		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	,       __snake_case=5_04_00	,       __snake_case=20_48	,       __snake_case=40_96	,       __snake_case=28	,       __snake_case=16	,       __snake_case=64	,       __snake_case=None	,       __snake_case="gelu_new"	,       __snake_case=0.0	,       __snake_case=0.0	,       __snake_case=0.0	,       __snake_case=1e-5	,       __snake_case=0.0_2	,       __snake_case=True	,       __snake_case=5_02_56	,       __snake_case=5_02_56	,       __snake_case=False	,       **__snake_case	,       ):
			_UpperCamelCase   :		Any			=						vocab_size
			_UpperCamelCase   :		Optional[Any]			=						n_positions
			_UpperCamelCase   :		str			=						n_embd
			_UpperCamelCase   :		Dict			=						n_layer
			_UpperCamelCase   :		Any			=						n_head
			_UpperCamelCase   :		int			=						n_inner
			_UpperCamelCase   :		Optional[Any]			=						rotary_dim
			_UpperCamelCase   :		Any			=						activation_function
			_UpperCamelCase   :		Union[str, Any]			=						resid_pdrop
			_UpperCamelCase   :		Any			=						embd_pdrop
			_UpperCamelCase   :		Any			=						attn_pdrop
			_UpperCamelCase   :		Dict			=						layer_norm_epsilon
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		Optional[int]			=						use_cache

			_UpperCamelCase   :		int			=						bos_token_id
			_UpperCamelCase   :		Any			=						eos_token_id

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







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case = "default"	,       __snake_case = None	,       __snake_case = False	,       ):
			super().__init__(__snake_case	,       task=__snake_case	,       patching_specs=__snake_case	,       use_past=__snake_case)
			if not getattr(self._config	,       'pad_token_id'	,       __snake_case):
				# TODO: how to do that better?
				_UpperCamelCase   :		Optional[int]			=						0


		@property
		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}})
			if self.use_past:
				self.fill_with_past_key_values_(__snake_case	,       direction='inputs')
				_UpperCamelCase   :		Union[str, Any]			=						{0: 'batch', 1: 'past_sequence + sequence'}
			else:
				_UpperCamelCase   :		Optional[int]			=						{0: 'batch', 1: 'sequence'}

			return common_inputs


		@property
		def 		A__					(				self):
			return self._config.n_layer


		@property
		def 		A__					(				self):
			return self._config.n_head


		def 		A__					(				self	,       __snake_case	,       __snake_case = -1	,       __snake_case = -1	,       __snake_case = False	,       __snake_case = None	,       ):
			_UpperCamelCase   :		Dict			=						super(__snake_case	,       self).generate_dummy_inputs(
			    __snake_case	,       batch_size=__snake_case	,       seq_length=__snake_case	,       is_pair=__snake_case	,       framework=__snake_case)

			# We need to order the input in the way they appears in the forward()
			_UpperCamelCase   :		Any			=						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

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

			_UpperCamelCase   :		Optional[Any]			=						common_inputs['attention_mask']
			if self.use_past:
				_UpperCamelCase   :		Tuple			=						ordered_inputs['attention_mask'].dtype
				_UpperCamelCase   :		str			=						torch.cat(
				    [ordered_inputs['attention_mask'], torch.ones(__snake_case	,       __snake_case	,       dtype=__snake_case)]	,       dim=1)

			return ordered_inputs






		@property
		def 		A__					(				self):
			return 13







from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCAmelCase__								=		numpy.array([0, 0])
lowerCAmelCase__								=		numpy.array([0.5, 0.8_66_02_54])
lowerCAmelCase__								=		numpy.array([1, 0])
lowerCAmelCase__								=		[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]							,       UpperCAmelCase_							:     int	)						->				list[numpy.ndarray]:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						initial_vectors
	for _ in range(UpperCAmelCase_	):
		_UpperCamelCase   :		str			=						iteration_step(UpperCAmelCase_	)
	return vectors






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]	)						->				list[numpy.ndarray]:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						[]
	for i, start_vector in enumerate(vectors[:-1]	):
		_UpperCamelCase   :		Union[str, Any]			=						vectors[i + 1]
		new_vectors.append(UpperCAmelCase_	)
		_UpperCamelCase   :		Tuple			=						end_vector - start_vector
		new_vectors.append(start_vector + difference_vector / 3	)
		new_vectors.append(
		    start_vector + difference_vector / 3 + rotate(difference_vector / 3							,       6_0	)	)
		new_vectors.append(start_vector + difference_vector * 2 / 3	)
	new_vectors.append(vectors[-1]	)
	return new_vectors






def 					lowerCamelCase_     (							UpperCAmelCase_							:     numpy.ndarray							,       UpperCAmelCase_							:     float	)						->				numpy.ndarray:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						numpy.radians(UpperCAmelCase_	)
	_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						numpy.cos(UpperCAmelCase_	), numpy.sin(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						numpy.array(((c, -s), (s, c))	)
	return numpy.dot(UpperCAmelCase_							,       UpperCAmelCase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]	)						->				None:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						plt.gca()
	axes.set_aspect('equal'	)

	# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
	# y-coordinates as inputs, which are constructed from the vector-list using
	# zip()
	_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						zip(*UpperCAmelCase_	)
	plt.plot(UpperCAmelCase_							,       UpperCAmelCase_	)
	plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							lowerCAmelCase__								=		iterate(INITIAL_VECTORS, 5)
							plot(processed_vectors)




