import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


_lowerCamelCase					:   Any							=		logging.get_logger(__name__)

_lowerCamelCase					:   str							=		{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase					:   Union[str, Any]							=		[
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

_lowerCamelCase					:   Any							=		{
    '''vocab_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
        '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
        '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
        '''funnel-transformer/small-base''': (
            '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
        '''funnel-transformer/large-base''': (
            '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
        ),
    },
}
_lowerCamelCase					:   Optional[int]							=		{f"""funnel-transformer/{name}""": 5_12 for name in _model_names}
_lowerCamelCase					:   List[str]							=		{f"""funnel-transformer/{name}""": {'''do_lower_case''': True} for name in _model_names}


class 						lowercase    (     a     ):
							lowercase__						:       str								=							VOCAB_FILES_NAMES
							lowercase__						:       List[str]								=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__						:       str								=							PRETRAINED_INIT_CONFIGURATION
							lowercase__						:       int								=							FunnelTokenizer
							lowercase__						:       Optional[Any]								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__						:       int								=							2




							def __init__(						self   :					List[Any]	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					List[str]=None	,							_UpperCamelCase   :					List[Any]=True	,							_UpperCamelCase   :					List[str]="<unk>"	,							_UpperCamelCase   :					str="<sep>"	,							_UpperCamelCase   :					Tuple="<pad>"	,							_UpperCamelCase   :					Optional[int]="<cls>"	,							_UpperCamelCase   :					Optional[Any]="<mask>"	,							_UpperCamelCase   :					Optional[int]="<s>"	,							_UpperCamelCase   :					Any="</s>"	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					List[str]=True	,							_UpperCamelCase   :					Dict=None	,							_UpperCamelCase   :					Optional[Any]="##"	,							**_UpperCamelCase   :					Dict	,							)					->				Optional[Any]:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase	,							tokenizer_file=_UpperCamelCase	,							do_lower_case=_UpperCamelCase	,							unk_token=_UpperCamelCase	,							sep_token=_UpperCamelCase	,							pad_token=_UpperCamelCase	,							cls_token=_UpperCamelCase	,							mask_token=_UpperCamelCase	,							bos_token=_UpperCamelCase	,							eos_token=_UpperCamelCase	,							clean_text=_UpperCamelCase	,							tokenize_chinese_chars=_UpperCamelCase	,							strip_accents=_UpperCamelCase	,							wordpieces_prefix=_UpperCamelCase	,							**_UpperCamelCase	,							)

												SCREAMING_SNAKE_CASE    			=       json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
												if (
												    normalizer_state.get("lowercase"	,							_UpperCamelCase      ) != do_lower_case
												    or normalizer_state.get("strip_accents"	,							_UpperCamelCase      ) != strip_accents
												    or normalizer_state.get("handle_chinese_chars"	,							_UpperCamelCase      ) != tokenize_chinese_chars
												):
																	SCREAMING_SNAKE_CASE    			=       getattr(_UpperCamelCase	,							normalizer_state.pop("type"      )      )
																	SCREAMING_SNAKE_CASE    			=       do_lower_case
																	SCREAMING_SNAKE_CASE    			=       strip_accents
																	SCREAMING_SNAKE_CASE    			=       tokenize_chinese_chars
																	SCREAMING_SNAKE_CASE    			=       normalizer_class(**_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       do_lower_case




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[int]=None      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

												return output




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None      )					->				List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       [self.sep_token_id]
												SCREAMING_SNAKE_CASE    			=       [self.cls_token_id]
												if token_ids_a is None:
																	return len(cls      ) * [self.cls_token_type_id] + len(token_ids_a + sep      ) * [0]
												return len(cls      ) * [self.cls_token_type_id] + len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[str] = None      )					->				Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self._tokenizer.model.save(_UpperCamelCase	,							name=_UpperCamelCase      )
												return tuple(_UpperCamelCase      )





import numpy as np





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							float = 1e-12      , UpperCAmelCase__     :							int = 1_0_0      , ):
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[1]
					# Ensure proper dimensionality.
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[0]
					# Ensure inputs are either both complex or both real
					assert np.iscomplexobj(UpperCAmelCase__  ) == np.iscomplexobj(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       np.iscomplexobj(UpperCAmelCase__  )
					if is_complex:
										# Ensure complex input_matrix is Hermitian
										assert np.array_equal(UpperCAmelCase__      , 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.

					SCREAMING_SNAKE_CASE    			=       False
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       1e12

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

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

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

										SCREAMING_SNAKE_CASE    			=       lambda_

					if is_complex:
										SCREAMING_SNAKE_CASE    			=       np.real(lambda_  )

					return lambda_, vector





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]  )
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  )
					SCREAMING_SNAKE_CASE    			=       real_input_matrix.astype(np.complexaaa  )
					SCREAMING_SNAKE_CASE    			=       np.triu(1j * complex_input_matrix      , 1  )
					complex_input_matrix += imag_matrix
					complex_input_matrix += -1 * imag_matrix.T
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  ).astype(np.complexaaa  )

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

										# Our implementation.
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       power_iteration(UpperCAmelCase__      , UpperCAmelCase__  )

										# Numpy implementation.

										# Get eigenvalues and eigenvectors using built-in numpy
										# eigh (eigh used for symmetric or hermetian matrices).
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       np.linalg.eigh(UpperCAmelCase__  )
										# Last eigenvalue is the maximum one.
										SCREAMING_SNAKE_CASE    			=       eigen_values[-1]
										# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
										SCREAMING_SNAKE_CASE    			=       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(UpperCAmelCase__  ) - np.abs(UpperCAmelCase__  )  ) <= 1e-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




