



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


_lowerCAmelCase	:   Union[str, Any]    =   logging.get_logger(__name__)

_lowerCAmelCase	:   int    =   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase	:   List[str]    =   {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase	:   List[str]    =   {
    "gpt2": 1_0_2_4,
    "gpt2-medium": 1_0_2_4,
    "gpt2-large": 1_0_2_4,
    "gpt2-xl": 1_0_2_4,
    "distilgpt2": 1_0_2_4,
}


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE__								=	PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE__								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE__								=	['input_ids', 'attention_mask']
		SCREAMING_SNAKE_CASE__								=	GPTaTokenizer
		def __init__(							self    ,a_=None    ,a_=None    ,a_=None    ,a_="<|endoftext|>"    ,a_="<|endoftext|>"    ,a_="<|endoftext|>"    ,a_=False    ,**a_    ,):







						"""simple docstring"""
						super().__init__(
						    a_    ,a_    ,tokenizer_file=a_    ,unk_token=a_    ,bos_token=a_    ,eos_token=a_    ,add_prefix_space=a_    ,**a_    ,)

						lowerCAmelCase__		  =  kwargs.pop('add_bos_token'    ,a_						)

						lowerCAmelCase__		  =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
						if pre_tok_state.get('add_prefix_space'    ,a_						) != add_prefix_space:
										lowerCAmelCase__		  =  getattr(a_    ,pre_tok_state.pop('type'						)						)
										lowerCAmelCase__		  =  add_prefix_space
										lowerCAmelCase__		  =  pre_tok_class(**a_						)

						lowerCAmelCase__		  =  add_prefix_space
		def 						SCREAMING_SNAKE_CASE_					(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  kwargs.get('is_split_into_words'    ,a_						)
						assert self.add_prefix_space or not is_split_into_words, (
						    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
						    "to use it with pretokenized inputs."
						)

						return super()._batch_encode_plus(*a_    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  kwargs.get('is_split_into_words'    ,a_						)

						assert self.add_prefix_space or not is_split_into_words, (
						    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
						    "to use it with pretokenized inputs."
						)

						return super()._encode_plus(*a_    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = None						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self._tokenizer.model.save(a_    ,name=a_						)
						return tuple(a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  []
						for is_user, text in conversation.iter_texts():
										input_ids.extend(self.encode(a_    ,add_special_tokens=a_						) + [self.eos_token_id]						)

						if len(a_						) > self.model_max_length:
										lowerCAmelCase__		  =  input_ids[-self.model_max_length :]
						return input_ids





import numpy as np







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__ = 1E-12    ,			snake_case__ = 100    ,			)	->     tuple[float, np.ndarray]:



				"""simple docstring"""





				assert np.shape(snake_case__					)[0] == np.shape(snake_case__					)[1]
				# Ensure proper dimensionality.
				assert np.shape(snake_case__					)[0] == np.shape(snake_case__					)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(snake_case__					) == np.iscomplexobj(snake_case__					)
				lowerCAmelCase__		  =  np.iscomplexobj(snake_case__					)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(snake_case__    ,			input_matrix.conj().T					)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				lowerCAmelCase__		  =  False
				lowerCAmelCase__		  =  0
				lowerCAmelCase__		  =  0
				lowerCAmelCase__		  =  1E12

				while not convergence:
								# Multiple matrix by the vector.
								lowerCAmelCase__		  =  np.dot(snake_case__    ,			snake_case__					)
								# Normalize the resulting output vector.
								lowerCAmelCase__		  =  w / np.linalg.norm(snake_case__					)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								lowerCAmelCase__		  =  vector.conj().T if is_complex else vector.T
								lowerCAmelCase__		  =  np.dot(snake_case__    ,			np.dot(snake_case__    ,			snake_case__					)					)

								# Check convergence.
								lowerCAmelCase__		  =  np.abs(lambda_ - lambda_previous					) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												lowerCAmelCase__		  =  True

								lowerCAmelCase__		  =  lambda_

				if is_complex:
								lowerCAmelCase__		  =  np.real(lambda_					)

				return lambda_, vector







def     UpperCAmelCase_		(					)	->     None:



				"""simple docstring"""





				lowerCAmelCase__		  =  np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]					)
				lowerCAmelCase__		  =  np.array([41, 4, 20]					)
				lowerCAmelCase__		  =  real_input_matrix.astype(np.complexaaa					)
				lowerCAmelCase__		  =  np.triu(1j * complex_input_matrix    ,			1					)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				lowerCAmelCase__		  =  np.array([41, 4, 20]					).astype(np.complexaaa					)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												lowerCAmelCase__		  =  real_input_matrix
												lowerCAmelCase__		  =  real_vector
								elif problem_type == "complex":
												lowerCAmelCase__		  =  complex_input_matrix
												lowerCAmelCase__		  =  complex_vector

								# Our implementation.
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  power_iteration(snake_case__    ,			snake_case__					)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  np.linalg.eigh(snake_case__					)
								# Last eigenvalue is the maximum one.
								lowerCAmelCase__		  =  eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								lowerCAmelCase__		  =  eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max					) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(snake_case__					) - np.abs(snake_case__					)					) <= 1E-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_power_iteration()
