import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


lowerCamelCase_				:		Any       						=       logging.get_logger(__name__)

lowerCamelCase_				:		Dict       						=       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_				:		List[Any]       						=       {
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """allenai/longformer-base-4096""": (
            """https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCamelCase_				:		Optional[int]       						=       {
    """allenai/longformer-base-4096""": 4_096,
    """allenai/longformer-large-4096""": 4_096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4_096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4_096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4_096,
}


class 						a__							(   __snake_case		):
			A__				:   Optional[int]								=			VOCAB_FILES_NAMES
			A__				:   Any								=			PRETRAINED_VOCAB_FILES_MAP
			A__				:   Dict								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__				:   List[str]								=			['input_ids', 'attention_mask']
			A__				:   Any								=			LongformerTokenizer



			def __init__(						self      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						UpperCAmelCase="replace"      ,						UpperCAmelCase="<s>"      ,						UpperCAmelCase="</s>"      ,						UpperCAmelCase="</s>"      ,						UpperCAmelCase="<s>"      ,						UpperCAmelCase="<unk>"      ,						UpperCAmelCase="<pad>"      ,						UpperCAmelCase="<mask>"      ,						UpperCAmelCase=False      ,						UpperCAmelCase=True      ,						**UpperCAmelCase      ,						) ->    Dict:
								super().__init__(
								    UpperCAmelCase      ,						UpperCAmelCase      ,						tokenizer_file=UpperCAmelCase      ,						errors=UpperCAmelCase      ,						bos_token=UpperCAmelCase      ,						eos_token=UpperCAmelCase      ,						sep_token=UpperCAmelCase      ,						cls_token=UpperCAmelCase      ,						unk_token=UpperCAmelCase      ,						pad_token=UpperCAmelCase      ,						mask_token=UpperCAmelCase      ,						add_prefix_space=UpperCAmelCase      ,						trim_offsets=UpperCAmelCase      ,						**UpperCAmelCase      ,						)

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

								__a							=   add_prefix_space

								__a							=   'post_processor'
								__a							=   getattr(self.backend_tokenizer      ,						UpperCAmelCase      ,						UpperCAmelCase    )
								if tokenizer_component_instance:
													__a							=   json.loads(tokenizer_component_instance.__getstate__()    )

													# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
													if "sep" in state:
																		__a							=   tuple(state['sep']    )
													if "cls" in state:
																		__a							=   tuple(state['cls']    )

													__a							=   False

													if state.get('add_prefix_space'      ,						UpperCAmelCase    ) != add_prefix_space:
																		__a							=   add_prefix_space
																		__a							=   True

													if state.get('trim_offsets'      ,						UpperCAmelCase    ) != trim_offsets:
																		__a							=   trim_offsets
																		__a							=   True

													if changes_to_apply:
																		__a							=   getattr(UpperCAmelCase      ,						state.pop('type'    )    )
																		__a							=   component_class(**UpperCAmelCase    )
																		setattr(self.backend_tokenizer      ,						UpperCAmelCase      ,						UpperCAmelCase    )



			@property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								if self._mask_token is None:
													if self.verbose:
																		logger.error('Using mask_token, but it is not set yet.'    )
													return None
								return str(self._mask_token    )



			@mask_token.setter
			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Optional[Any]:
								__a							=   AddedToken(UpperCAmelCase      ,						lstrip=UpperCAmelCase      ,						rstrip=UpperCAmelCase    ) if isinstance(UpperCAmelCase      ,						UpperCAmelCase    ) else value
								__a							=   value



			def __SCREAMING_SNAKE_CASE							(						self      ,						*UpperCAmelCase      ,						**UpperCAmelCase    ) ->    BatchEncoding:
								__a							=   kwargs.get('is_split_into_words'      ,						UpperCAmelCase    )
								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(*UpperCAmelCase      ,						**UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						*UpperCAmelCase      ,						**UpperCAmelCase    ) ->    BatchEncoding:
								__a							=   kwargs.get('is_split_into_words'      ,						UpperCAmelCase    )

								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(*UpperCAmelCase      ,						**UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = None    ) ->    Tuple[str]:
								__a							=   self._tokenizer.model.save(UpperCAmelCase      ,						name=UpperCAmelCase    )
								return tuple(UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=None    ) ->    str:
								__a							=   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
													return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = None    ) ->    List[int]:
								__a							=   [self.sep_token_id]
								__a							=   [self.cls_token_id]

								if token_ids_a is None:
													return len(cls + token_ids_a + sep    ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]





from typing import Any

import numpy as np
def 							lowerCAmelCase(	__lowerCamelCase						):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(__lowerCamelCase      ,				matrix.T						) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					__a					,      __a							=   np.linalg.eigh(__lowerCamelCase						)

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0						)						)
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a							=   np.random.randn(__lowerCamelCase      ,				__lowerCamelCase						)
					__a							=   np.dot(__lowerCamelCase      ,				random_matrix.T						)
					assert _is_matrix_spd(__lowerCamelCase						)
					return spd_matrix
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase = 1000      ,				__lowerCamelCase = 1e-8      ,				):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[0]
					assert _is_matrix_spd(__lowerCamelCase						)

					# Initialize solution guess, residual, search direction.
					__a							=   np.zeros((np.shape(__lowerCamelCase						)[0], 1)						)
					__a							=   np.copy(__lowerCamelCase						)
					__a							=   np.copy(__lowerCamelCase						)

					# Set initial errors in solution guess and residual.
					__a							=   1e9
					__a							=   1e9
					__a							=   1e9

					# Set iteration counter to threshold number of iterations.
					__a							=   0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

										# The main algorithm.

										# Update search direction magnitude.
										__a							=   np.dot(ra.T      ,				__lowerCamelCase						) / np.dot(pa.T      ,				__lowerCamelCase						)
										# Update solution guess.
										__a							=   xa + alpha * pa
										# Calculate new residual.
										__a							=   ra - alpha * w
										# Calculate new Krylov subspace scale.
										__a							=   np.dot(r.T      ,				__lowerCamelCase						) / np.dot(ra.T      ,				__lowerCamelCase						)
										# Calculate new A conjuage search direction.
										__a							=   r + beta * pa

										# Calculate errors.
										__a							=   np.linalg.norm(r - ra						)
										__a							=   np.linalg.norm(x - xa						)
										__a							=   np.maximum(__lowerCamelCase      ,				__lowerCamelCase						)

										# Update variables.
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x
def 							lowerCAmelCase(	):
					__a							=   3
					__a							=   _create_spd_matrix(__lowerCamelCase						)
					__a							=   np.random.randn(__lowerCamelCase      ,				1						)
					__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

					# Numpy solution.
					__a							=   np.linalg.solve(__lowerCamelCase      ,				__lowerCamelCase						)

					# Our implementation.
					__a							=   conjugate_gradient(__lowerCamelCase      ,				__lowerCamelCase						)

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true						) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true						) <= 1e-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_conjugate_gradient()




