
'''simple docstring'''
import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							ProphetNetTokenizer
				lowerCAmelCase_     			=							False







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											super().setUp()

											UpperCamelCase   			=     [
											    '[UNK]',
											    '[CLS]',
											    '[SEP]',
											    '[PAD]',
											    '[MASK]',
											    'want',
											    '##want',
											    '##ed',
											    'wa',
											    'un',
											    'runn',
											    '##ing',
											    ',',
											    'low',
											    'lowest',
											]
											UpperCamelCase   			=     os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['vocab_file']     )
											with open(self.vocab_file						,				'w'						,				encoding='utf-8'     ) as vocab_writer:
																		vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]     )     )







				def 						UpperCAmelCase_    (       self						,				A_     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     'UNwant\u00E9d,running'
											UpperCamelCase   			=     'unwanted, running'
											return input_text, output_text







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     self.tokenizer_class(self.vocab_file     )

											UpperCamelCase   			=     tokenizer.tokenize('UNwant\u00E9d,running'     )
											self.assertListEqual(A_						,				['un', '##want', '##ed', ',', 'runn', '##ing']     )
											self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )						,				[9, 6, 7, 12, 10, 11]     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     BasicTokenizer()

											self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'     )						,				['ah', '\u535A', '\u63A8', 'zz']     )







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     BasicTokenizer(do_lower_case=A_     )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '     )						,				['hello', '!', 'how', 'are', 'you', '?']     )
											self.assertListEqual(tokenizer.tokenize('H\u00E9llo'     )						,				['hello']     )







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     BasicTokenizer(do_lower_case=A_						,				strip_accents=A_     )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '     )						,				['hällo', '!', 'how', 'are', 'you', '?']     )
											self.assertListEqual(tokenizer.tokenize('H\u00E9llo'     )						,				['h\u00E9llo']     )







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     BasicTokenizer(do_lower_case=A_						,				strip_accents=A_     )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '     )						,				['hallo', '!', 'how', 'are', 'you', '?']     )
											self.assertListEqual(tokenizer.tokenize('H\u00E9llo'     )						,				['hello']     )







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     BasicTokenizer(do_lower_case=A_     )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '     )						,				['hallo', '!', 'how', 'are', 'you', '?']     )
											self.assertListEqual(tokenizer.tokenize('H\u00E9llo'     )						,				['hello']     )







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     BasicTokenizer(do_lower_case=A_     )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '     )						,				['HeLLo', '!', 'how', 'Are', 'yoU', '?']     )







				def 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     BasicTokenizer(do_lower_case=A_						,				strip_accents=A_     )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '     )						,				['HäLLo', '!', 'how', 'Are', 'yoU', '?']     )







				def 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     BasicTokenizer(do_lower_case=A_						,				strip_accents=A_     )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '     )						,				['HaLLo', '!', 'how', 'Are', 'yoU', '?']     )







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     BasicTokenizer(do_lower_case=A_						,				never_split=['[UNK]']     )

											self.assertListEqual(
											    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'     )						,				['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']     )







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

											UpperCamelCase   			=     {}
											for i, token in enumerate(A_     ):
																		UpperCamelCase   			=     i
											UpperCamelCase   			=     WordpieceTokenizer(vocab=A_						,				unk_token='[UNK]'     )

											self.assertListEqual(tokenizer.tokenize(''     )						,				[]     )

											self.assertListEqual(tokenizer.tokenize('unwanted running'     )						,				['un', '##want', '##ed', 'runn', '##ing']     )

											self.assertListEqual(tokenizer.tokenize('unwantedX running'     )						,				['[UNK]', 'runn', '##ing']     )







				@require_torch
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased'     )

											UpperCamelCase   			=     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											UpperCamelCase   			=     [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
											UpperCamelCase   			=     tokenizer(A_						,				padding=A_						,				return_tensors='pt'     )
											self.assertIsInstance(A_						,				A_     )
											UpperCamelCase   			=     list(batch.input_ids.numpy()[0]     )
											self.assertListEqual(A_						,				A_     )

											self.assertEqual((2, 9)						,				batch.input_ids.shape     )
											self.assertEqual((2, 9)						,				batch.attention_mask.shape     )







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											self.assertTrue(_is_whitespace(' '     )     )
											self.assertTrue(_is_whitespace('\t'     )     )
											self.assertTrue(_is_whitespace('\r'     )     )
											self.assertTrue(_is_whitespace('\n'     )     )
											self.assertTrue(_is_whitespace('\u00A0'     )     )

											self.assertFalse(_is_whitespace('A'     )     )
											self.assertFalse(_is_whitespace('-'     )     )







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											self.assertTrue(_is_control('\u0005'     )     )

											self.assertFalse(_is_control('A'     )     )
											self.assertFalse(_is_control(' '     )     )
											self.assertFalse(_is_control('\t'     )     )
											self.assertFalse(_is_control('\r'     )     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											self.assertTrue(_is_punctuation('-'     )     )
											self.assertTrue(_is_punctuation('$'     )     )
											self.assertTrue(_is_punctuation('`'     )     )
											self.assertTrue(_is_punctuation('.'     )     )

											self.assertFalse(_is_punctuation('A'     )     )
											self.assertFalse(_is_punctuation(' '     )     )







				@slow
				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased'     )

											UpperCamelCase   			=     tokenizer.encode('sequence builders'						,				add_special_tokens=A_     )
											UpperCamelCase   			=     tokenizer.encode('multi-sequence build'						,				add_special_tokens=A_     )

											UpperCamelCase   			=     tokenizer.build_inputs_with_special_tokens(A_     )
											UpperCamelCase   			=     tokenizer.build_inputs_with_special_tokens(A_						,				A_     )

											assert encoded_sentence == text + [102]
											assert encoded_pair == text + [102] + text_a + [102]






'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCAmelCase					:    Any           = logging.get_logger(__name__)

lowerCAmelCase					:    Optional[int]           = {
    'deepmind/language-perceiver': 'https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""perceiver"""







				def __init__(       self						,				A_=256						,				A_=1280						,				A_=768						,				A_=1						,				A_=26						,				A_=8						,				A_=8						,				A_=None						,				A_=None						,				A_="kv"						,				A_=1						,				A_=1						,				A_="gelu"						,				A_=0.1						,				A_=0.02						,				A_=1e-12						,				A_=True						,				A_=262						,				A_=2048						,				A_=56						,				A_=[368, 496]						,				A_=16						,				A_=1920						,				A_=16						,				A_=[1, 16, 224, 224]						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_     )

											UpperCamelCase   			=     num_latents
											UpperCamelCase   			=     d_latents
											UpperCamelCase   			=     d_model
											UpperCamelCase   			=     num_blocks
											UpperCamelCase   			=     num_self_attends_per_block
											UpperCamelCase   			=     num_self_attention_heads
											UpperCamelCase   			=     num_cross_attention_heads
											UpperCamelCase   			=     qk_channels
											UpperCamelCase   			=     v_channels
											UpperCamelCase   			=     cross_attention_shape_for_attention
											UpperCamelCase   			=     self_attention_widening_factor
											UpperCamelCase   			=     cross_attention_widening_factor
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     use_query_residual
											# masked language modeling attributes
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     max_position_embeddings
											# image classification attributes
											UpperCamelCase   			=     image_size
											# flow attributes
											UpperCamelCase   			=     train_size
											# multimodal autoencoding attributes
											UpperCamelCase   			=     num_frames
											UpperCamelCase   			=     audio_samples_per_frame
											UpperCamelCase   			=     samples_per_patch
											UpperCamelCase   			=     output_shape


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				@property
				def 						UpperCAmelCase_    (       self     )->		Mapping[str, Mapping[int, str]]:







											'''simple docstring'''






											if self.task == "multiple-choice":
																		UpperCamelCase   			=     {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		UpperCamelCase   			=     {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('inputs', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]     )







				@property
				def 						UpperCAmelCase_    (       self     )->		float:







											'''simple docstring'''






											return 1e-4







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = -1						,				A_ = -1						,				A_ = -1						,				A_ = False						,				A_ = None						,				A_ = 3						,				A_ = 40						,				A_ = 40						,				)->		Mapping[str, Any]:







											'''simple docstring'''






											if isinstance(A_						,				A_     ):
																		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																		UpperCamelCase   			=     compute_effective_axis_dimension(
																		    A_						,				fixed_dimension=OnnxConfig.default_fixed_batch						,				num_token_to_add=0     )
																		# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
																		UpperCamelCase   			=     preprocessor.num_special_tokens_to_add(A_     )
																		UpperCamelCase   			=     compute_effective_axis_dimension(
																		    A_						,				fixed_dimension=OnnxConfig.default_fixed_sequence						,				num_token_to_add=A_     )
																		# Generate dummy inputs according to compute batch and sequence
																		UpperCamelCase   			=     [' '.join(['a']     ) * seq_length] * batch_size
																		UpperCamelCase   			=     dict(preprocessor(A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     inputs.pop('input_ids'     )
																		return inputs
											elif isinstance(A_						,				A_     ) and preprocessor.model_input_names[0] == "pixel_values":
																		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																		UpperCamelCase   			=     compute_effective_axis_dimension(A_						,				fixed_dimension=OnnxConfig.default_fixed_batch     )
																		UpperCamelCase   			=     self._generate_dummy_images(A_						,				A_						,				A_						,				A_     )
																		UpperCamelCase   			=     dict(preprocessor(images=A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     inputs.pop('pixel_values'     )
																		return inputs
											else:
																		raise ValueError(
																		    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'     )




