


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
SCREAMING_SNAKE_CASE :   str							      =				{"LayoutLMv2Config", "LayoutLMv3Config"}






@is_pipeline_test
class _lowerCamelCase(		unittest.TestCase     ):
							lowercase_				:			List[Any]									=    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							lowercase_				:			Tuple									=    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														lowercase_				:			List[Any]									=    {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														lowercase_				:			int									=    {
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}
							def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Union[str, Any]:




											"""simple docstring"""

											_lowercase :							Tuple   	=	ZeroShotClassificationPipeline(
											    model=lowerCAmelCase__,   tokenizer=lowerCAmelCase__,   candidate_labels=['polics', 'health'])
											return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
							def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




											"""simple docstring"""

											_lowercase :							Tuple   	=	classifier('Who are you voting for in 2020?',   candidate_labels='politics')
											self.assertEqual(lowerCAmelCase__,   {'sequence': ANY(lowerCAmelCase__), 'labels': [ANY(lowerCAmelCase__)], 'scores': [ANY(lowerCAmelCase__)]})

											# No kwarg
											_lowercase :							Any   	=	classifier('Who are you voting for in 2020?',   ['politics'])
											self.assertEqual(lowerCAmelCase__,   {'sequence': ANY(lowerCAmelCase__), 'labels': [ANY(lowerCAmelCase__)], 'scores': [ANY(lowerCAmelCase__)]})

											_lowercase :							Optional[int]   	=	classifier('Who are you voting for in 2020?',   candidate_labels=['politics'])
											self.assertEqual(lowerCAmelCase__,   {'sequence': ANY(lowerCAmelCase__), 'labels': [ANY(lowerCAmelCase__)], 'scores': [ANY(lowerCAmelCase__)]})

											_lowercase :							Optional[Any]   	=	classifier('Who are you voting for in 2020?',   candidate_labels='politics, public health')
											self.assertEqual(
											    lowerCAmelCase__,   {'sequence': ANY(lowerCAmelCase__), 'labels': [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)], 'scores': [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)]})
											self.assertAlmostEqual(sum(nested_simplify(outputs['scores'])),   1.0)

											_lowercase :							Tuple   	=	classifier('Who are you voting for in 2020?',   candidate_labels=['politics', 'public health'])
											self.assertEqual(
											    lowerCAmelCase__,   {'sequence': ANY(lowerCAmelCase__), 'labels': [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)], 'scores': [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)]})
											self.assertAlmostEqual(sum(nested_simplify(outputs['scores'])),   1.0)

											_lowercase :							Any   	=	classifier(
											    'Who are you voting for in 2020?',   candidate_labels='politics',   hypothesis_template='This text is about {}')
											self.assertEqual(lowerCAmelCase__,   {'sequence': ANY(lowerCAmelCase__), 'labels': [ANY(lowerCAmelCase__)], 'scores': [ANY(lowerCAmelCase__)]})

											# https://github.com/huggingface/transformers/issues/13846
											_lowercase :							Any   	=	classifier(['I am happy'],   ['positive', 'negative'])
											self.assertEqual(
											    lowerCAmelCase__,   [
											        {'sequence': ANY(lowerCAmelCase__), 'labels': [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)], 'scores': [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)]}
											        for i in range(1)
											    ],   )
											_lowercase :							Union[str, Any]   	=	classifier(['I am happy', 'I am sad'],   ['positive', 'negative'])
											self.assertEqual(
											    lowerCAmelCase__,   [
											        {'sequence': ANY(lowerCAmelCase__), 'labels': [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)], 'scores': [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)]}
											        for i in range(2)
											    ],   )

											with self.assertRaises(lowerCAmelCase__):
															classifier('',   candidate_labels='politics')

											with self.assertRaises(lowerCAmelCase__):
															classifier(lowerCAmelCase__,   candidate_labels='politics')

											with self.assertRaises(lowerCAmelCase__):
															classifier('Who are you voting for in 2020?',   candidate_labels='')

											with self.assertRaises(lowerCAmelCase__):
															classifier('Who are you voting for in 2020?',   candidate_labels=lowerCAmelCase__)

											with self.assertRaises(lowerCAmelCase__):
															classifier(
															    'Who are you voting for in 2020?',   candidate_labels='politics',   hypothesis_template='Not formatting template',   )

											with self.assertRaises(lowerCAmelCase__):
															classifier(
															    'Who are you voting for in 2020?',   candidate_labels='politics',   hypothesis_template=lowerCAmelCase__,   )

											self.run_entailment_id(lowerCAmelCase__)
							def     UpperCamelCase  (      self,   lowerCamelCase) ->     str:




											"""simple docstring"""

											_lowercase :							str   	=	zero_shot_classifier.model.config
											_lowercase :							Optional[int]   	=	config.labelaid
											_lowercase :							Union[str, Any]   	=	zero_shot_classifier.entailment_id

											_lowercase :							Union[str, Any]   	=	{'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
											self.assertEqual(zero_shot_classifier.entailment_id,   -1)

											_lowercase :							str   	=	{'entailment': 0, 'neutral': 1, 'contradiction': 2}
											self.assertEqual(zero_shot_classifier.entailment_id,   0)

											_lowercase :							Optional[Any]   	=	{'ENTAIL': 0, 'NON-ENTAIL': 1}
											self.assertEqual(zero_shot_classifier.entailment_id,   0)

											_lowercase :							Optional[int]   	=	{'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
											self.assertEqual(zero_shot_classifier.entailment_id,   2)

											_lowercase :							Union[str, Any]   	=	original_labelaid
											self.assertEqual(lowerCAmelCase__,   zero_shot_classifier.entailment_id)
							@require_torch
							def     UpperCamelCase  (      self) ->     Dict:




											"""simple docstring"""

											_lowercase :							Any   	=	pipeline(
											    'zero-shot-classification',   model='sshleifer/tiny-distilbert-base-cased-distilled-squad',   framework='pt',   )
											# There was a regression in 4.10 for this
											# Adding a test so we don't make the mistake again.
											# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
											zero_shot_classifier(
											    'Who are you voting for in 2020?' * 1_00,   candidate_labels=['politics', 'public health', 'science'])
							@require_torch
							def     UpperCamelCase  (      self) ->     Tuple:




											"""simple docstring"""

											_lowercase :							Optional[int]   	=	pipeline(
											    'zero-shot-classification',   model='sshleifer/tiny-distilbert-base-cased-distilled-squad',   framework='pt',   )
											_lowercase :							Optional[Any]   	=	zero_shot_classifier(
											    'Who are you voting for in 2020?',   candidate_labels=['politics', 'public health', 'science'])

											self.assertEqual(
											    nested_simplify(lowerCAmelCase__),   {
											        'sequence': 'Who are you voting for in 2020?',
											        'labels': ['science', 'public health', 'politics'],
											        'scores': [0.3_3_3, 0.3_3_3, 0.3_3_3],
											    },   )
							@require_tf
							def     UpperCamelCase  (      self) ->     Tuple:




											"""simple docstring"""

											_lowercase :							Optional[int]   	=	pipeline(
											    'zero-shot-classification',   model='sshleifer/tiny-distilbert-base-cased-distilled-squad',   framework='tf',   )
											_lowercase :							Union[str, Any]   	=	zero_shot_classifier(
											    'Who are you voting for in 2020?',   candidate_labels=['politics', 'public health', 'science'])

											self.assertEqual(
											    nested_simplify(lowerCAmelCase__),   {
											        'sequence': 'Who are you voting for in 2020?',
											        'labels': ['science', 'public health', 'politics'],
											        'scores': [0.3_3_3, 0.3_3_3, 0.3_3_3],
											    },   )
							@slow
							@require_torch
							def     UpperCamelCase  (      self) ->     Optional[int]:




											"""simple docstring"""

											_lowercase :							int   	=	pipeline('zero-shot-classification',   model='roberta-large-mnli',   framework='pt')
											_lowercase :							int   	=	zero_shot_classifier(
											    'Who are you voting for in 2020?',   candidate_labels=['politics', 'public health', 'science'])

											self.assertEqual(
											    nested_simplify(lowerCAmelCase__),   {
											        'sequence': 'Who are you voting for in 2020?',
											        'labels': ['politics', 'public health', 'science'],
											        'scores': [0.9_7_6, 0.0_1_5, 0.0_0_9],
											    },   )
											_lowercase :							str   	=	zero_shot_classifier(
											    'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
											    ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
											    ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
											    ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
											    ' machine translation tasks show these models to be superior in quality while being more parallelizable'
											    ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
											    ' English-to-German translation task, improving over the existing best results, including ensembles by'
											    ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
											    ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
											    ' fraction of the training costs of the best models from the literature. We show that the Transformer'
											    ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
											    ' large and limited training data.',   candidate_labels=['machine learning', 'statistics', 'translation', 'vision'],   multi_label=lowerCAmelCase__,   )
											self.assertEqual(
											    nested_simplify(lowerCAmelCase__),   {
											        'sequence': (
											            'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
											            ' networks in an encoder-decoder configuration. The best performing models also connect the'
											            ' encoder and decoder through an attention mechanism. We propose a new simple network'
											            ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
											            ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
											            ' superior in quality while being more parallelizable and requiring significantly less time to'
											            ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
											            ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
											            ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
											            ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
											            ' costs of the best models from the literature. We show that the Transformer generalizes well to'
											            ' other tasks by applying it successfully to English constituency parsing both with large and'
											            ' limited training data.'
											        ),
											        'labels': ['translation', 'machine learning', 'vision', 'statistics'],
											        'scores': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
											    },   )




							@slow
							@require_tf
							def     UpperCamelCase  (      self) ->     Dict:




											"""simple docstring"""

											_lowercase :							Optional[Any]   	=	pipeline('zero-shot-classification',   model='roberta-large-mnli',   framework='tf')
											_lowercase :							int   	=	zero_shot_classifier(
											    'Who are you voting for in 2020?',   candidate_labels=['politics', 'public health', 'science'])

											self.assertEqual(
											    nested_simplify(lowerCAmelCase__),   {
											        'sequence': 'Who are you voting for in 2020?',
											        'labels': ['politics', 'public health', 'science'],
											        'scores': [0.9_7_6, 0.0_1_5, 0.0_0_9],
											    },   )
											_lowercase :							str   	=	zero_shot_classifier(
											    'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
											    ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
											    ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
											    ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
											    ' machine translation tasks show these models to be superior in quality while being more parallelizable'
											    ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
											    ' English-to-German translation task, improving over the existing best results, including ensembles by'
											    ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
											    ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
											    ' fraction of the training costs of the best models from the literature. We show that the Transformer'
											    ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
											    ' large and limited training data.',   candidate_labels=['machine learning', 'statistics', 'translation', 'vision'],   multi_label=lowerCAmelCase__,   )
											self.assertEqual(
											    nested_simplify(lowerCAmelCase__),   {
											        'sequence': (
											            'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
											            ' networks in an encoder-decoder configuration. The best performing models also connect the'
											            ' encoder and decoder through an attention mechanism. We propose a new simple network'
											            ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
											            ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
											            ' superior in quality while being more parallelizable and requiring significantly less time to'
											            ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
											            ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
											            ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
											            ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
											            ' costs of the best models from the literature. We show that the Transformer generalizes well to'
											            ' other tasks by applying it successfully to English constituency parsing both with large and'
											            ' limited training data.'
											        ),
											        'labels': ['translation', 'machine learning', 'vision', 'statistics'],
											        'scores': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
											    },   )






from __future__ import annotations

SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				tuple[int, int, int]
SCREAMING_SNAKE_CASE :   Any							      =				tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"ABCDEFGHIJKLMNOPQRSTUVWXYZ"

# -------------------------- default selection --------------------------
# rotors --------------------------
SCREAMING_SNAKE_CASE :   Tuple							      =				"EGZWVONAHDCLFQMSIPJBYUKXTR"
SCREAMING_SNAKE_CASE :   List[str]							      =				"FOBHMDKEXQNRAULPGSJVTYICZW"
SCREAMING_SNAKE_CASE :   Dict							      =				"ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
SCREAMING_SNAKE_CASE :   Any							      =				{
    "A": "N",
    "N": "A",
    "B": "O",
    "O": "B",
    "C": "P",
    "P": "C",
    "D": "Q",
    "Q": "D",
    "E": "R",
    "R": "E",
    "F": "S",
    "S": "F",
    "G": "T",
    "T": "G",
    "H": "U",
    "U": "H",
    "I": "V",
    "V": "I",
    "J": "W",
    "W": "J",
    "K": "X",
    "X": "K",
    "L": "Y",
    "Y": "L",
    "M": "Z",
    "Z": "M",
}

# -------------------------- extra rotors --------------------------
SCREAMING_SNAKE_CASE :   List[Any]							      =				"RMDJXFUWGISLHVTCQNKYPBEZOA"
SCREAMING_SNAKE_CASE :   Dict							      =				"SGLCPQWZHKXAREONTFBVIYJUDM"
SCREAMING_SNAKE_CASE :   Optional[int]							      =				"HVSICLTYKQUBXDWAJZOMFGPREN"
SCREAMING_SNAKE_CASE :   List[Any]							      =				"RZWQHFMVDBKICJLNTUXAGYPSOE"
SCREAMING_SNAKE_CASE :   Optional[int]							      =				"LFKIJODBEGAMQPXVUHYSTCZRWN"
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"KOAEGVDHXPQZMLFTYWJNBRCIUS"



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
    # Checks if there are 3 unique rotors

    if (unique_rotsel := len(set(lowerCamelCase_				)				)) < 3:
        _lowercase :							int   	=	F'''Please use 3 unique rotors (not {unique_rotsel})'''
        raise Exception(lowerCamelCase_				)

    # Checks if rotor positions are valid
    _lowercase	,       _lowercase	,       _lowercase :							Union[str, Any]   	=	rotpos
    if not 0 < rotorposa <= len(lowerCamelCase_				):
        _lowercase :							Union[str, Any]   	=	F'''First rotor position is not within range of 1..26 ({rotorposa}'''
        raise ValueError(lowerCamelCase_				)
    if not 0 < rotorposa <= len(lowerCamelCase_				):
        _lowercase :							Tuple   	=	F'''Second rotor position is not within range of 1..26 ({rotorposa})'''
        raise ValueError(lowerCamelCase_				)
    if not 0 < rotorposa <= len(lowerCamelCase_				):
        _lowercase :							str   	=	F'''Third rotor position is not within range of 1..26 ({rotorposa})'''
        raise ValueError(lowerCamelCase_				)

    # Validates string and returns dict
    _lowercase :							Optional[int]   	=	_plugboard(lowerCamelCase_				)

    return rotpos, rotsel, pbdict



def        UpperCamelCase_(   lowerCamelCase_				)					->		dict[str, str]:

    # tests the input string if it
    # a) is type string
    # b) has even length (so pairs can be made)
    if not isinstance(lowerCamelCase_							,				lowerCamelCase_				):
        _lowercase :							List[Any]   	=	F'''Plugboard setting isn\'t type string ({type(lowerCamelCase_				)})'''
        raise TypeError(lowerCamelCase_				)
    elif len(lowerCamelCase_				) % 2 != 0:
        _lowercase :							Optional[Any]   	=	F'''Odd number of symbols ({len(lowerCamelCase_				)})'''
        raise Exception(lowerCamelCase_				)
    elif pbstring == "":
        return {}

    pbstring.replace(' '							,				''				)

    # Checks if all characters are unique
    _lowercase :							List[Any]   	=	set()
    for i in pbstring:
        if i not in abc:
            _lowercase :							str   	=	F'''\'{i}\' not in list of symbols'''
            raise Exception(lowerCamelCase_				)
        elif i in tmppbl:
            _lowercase :							Tuple   	=	F'''Duplicate symbol ({i})'''
            raise Exception(lowerCamelCase_				)
        else:
            tmppbl.add(lowerCamelCase_				)
    del tmppbl

    # Created the dictionary
    _lowercase :							List[Any]   	=	{}
    for j in range(0							,				len(lowerCamelCase_				) - 1							,				2				):
        _lowercase :							str   	=	pbstring[j + 1]
        _lowercase :							Union[str, Any]   	=	pbstring[j]

    return pb



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_ = (rotora, rotora, rotora)							,				lowerCamelCase_ = ""							,				)					->		str:
    _lowercase :							int   	=	text.upper()
    _lowercase	,       _lowercase	,       _lowercase :							Optional[int]   	=	_validator(
        lowerCamelCase_							,				lowerCamelCase_							,				plugb.upper()				)

    _lowercase	,       _lowercase	,       _lowercase :							Union[str, Any]   	=	rotor_position
    _lowercase	,       _lowercase	,       _lowercase :							Optional[Any]   	=	rotor_selection
    rotorposa -= 1
    rotorposa -= 1
    rotorposa -= 1

    _lowercase :							Any   	=	[]

    # encryption/decryption process --------------------------
    for symbol in text:
        if symbol in abc:
            # 1st plugboard --------------------------
            if symbol in plugboard:
                _lowercase :							List[str]   	=	plugboard[symbol]

            # rotor ra --------------------------
            _lowercase :							Any   	=	abc.index(lowerCamelCase_				) + rotorposa
            _lowercase :							Any   	=	rotora[index % len(lowerCamelCase_				)]

            # rotor rb --------------------------
            _lowercase :							List[Any]   	=	abc.index(lowerCamelCase_				) + rotorposa
            _lowercase :							Any   	=	rotora[index % len(lowerCamelCase_				)]

            # rotor rc --------------------------
            _lowercase :							Tuple   	=	abc.index(lowerCamelCase_				) + rotorposa
            _lowercase :							Tuple   	=	rotora[index % len(lowerCamelCase_				)]

            # reflector --------------------------
            # this is the reason you don't need another machine to decipher

            _lowercase :							List[Any]   	=	reflector[symbol]

            # 2nd rotors
            _lowercase :							int   	=	abc[rotora.index(lowerCamelCase_				) - rotorposa]
            _lowercase :							str   	=	abc[rotora.index(lowerCamelCase_				) - rotorposa]
            _lowercase :							int   	=	abc[rotora.index(lowerCamelCase_				) - rotorposa]

            # 2nd plugboard
            if symbol in plugboard:
                _lowercase :							Optional[int]   	=	plugboard[symbol]

            # moves/resets rotor positions
            rotorposa += 1
            if rotorposa >= len(lowerCamelCase_				):
                _lowercase :							Optional[Any]   	=	0
                rotorposa += 1
            if rotorposa >= len(lowerCamelCase_				):
                _lowercase :							Any   	=	0
                rotorposa += 1
            if rotorposa >= len(lowerCamelCase_				):
                _lowercase :							Any   	=	0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
        result.append(lowerCamelCase_				)

    return "".join(lowerCamelCase_				)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE :   Optional[int]							      =				"This is my Python script that emulates the Enigma machine from WWII."
 SCREAMING_SNAKE_CASE :   Optional[int]							      =				(1, 1, 1)
 SCREAMING_SNAKE_CASE :   List[Any]							      =				"pictures"
 SCREAMING_SNAKE_CASE :   int							      =				(rotora, rotora, rotora)
 SCREAMING_SNAKE_CASE :   List[Any]							      =				enigma(message, rotor_pos, rotor_sel, pb)

 print("Encrypted message:", en)
 print("Decrypted message:", enigma(en, rotor_pos, rotor_sel, pb))


