




"""simple docstring"""

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 							lowercase_			(	__lowerCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      RobertaTokenizer
 UpperCAmelCase       :	List[Any]     =      RobertaTokenizerFast
 UpperCAmelCase       :	List[str]     =      True
 UpperCAmelCase       :	int     =      {'''cls_token''': '''<s>'''}

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       _A     =  [
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           '\u0120',
           '\u0120l',
           '\u0120n',
           '\u0120lo',
           '\u0120low',
           'er',
           '\u0120lowest',
           '\u0120newer',
           '\u0120wider',
           '<unk>',
       ]
       _A     =  dict(zip(_UpperCAmelCase       ,				range(len(_UpperCAmelCase						)						)						)						)
       _A     =  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
       _A     =  {'unk_token': '<unk>'}

       _A     =  os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['vocab_file']						)
       _A     =  os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['merges_file']						)
       with open(self.vocab_file       ,				'w'       ,				encoding='utf-8'						) as fp:
             fp.write(json.dumps(_UpperCAmelCase						) + '\n'						)
       with open(self.merges_file       ,				'w'       ,				encoding='utf-8'						) as fp:
             fp.write('\n'.join(_UpperCAmelCase						)						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				**_UpperCAmelCase			:			str						):
       kwargs.update(self.special_tokens_map						)
       return self.tokenizer_class.from_pretrained(self.tmpdirname       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			str       ,				**_UpperCAmelCase			:			Optional[Any]						):
       kwargs.update(self.special_tokens_map						)
       return RobertaTokenizerFast.from_pretrained(self.tmpdirname       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[int]       ,				_UpperCAmelCase			:			List[Any]						):
       _A     =  'lower newer'
       _A     =  'lower newer'
       return input_text, output_text

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  self.tokenizer_class(self.vocab_file       ,				self.merges_file       ,				**self.special_tokens_map						)
       _A     =  'lower newer'
       _A     =  ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
       _A     =  tokenizer.tokenize(_UpperCAmelCase						)  # , add_prefix_space=True)
       self.assertListEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)

       _A     =  tokens + [tokenizer.unk_token]
       _A     =  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  self.get_tokenizer()

       self.assertListEqual(tokenizer.encode('Hello world!'       ,				add_special_tokens=_UpperCAmelCase						)       ,				[0, 31_414, 232, 328, 2]						)
       self.assertListEqual(
           tokenizer.encode('Hello world! cécé herlolip 418'       ,				add_special_tokens=_UpperCAmelCase						)       ,				[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]       ,				)

 @slow
 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  self.tokenizer_class.from_pretrained('roberta-base'						)

       _A     =  tokenizer.encode('sequence builders'       ,				add_special_tokens=_UpperCAmelCase						)
       _A     =  tokenizer.encode('multi-sequence build'       ,				add_special_tokens=_UpperCAmelCase						)

       _A     =  tokenizer.encode(
           'sequence builders'       ,				add_special_tokens=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase						)
       _A     =  tokenizer.encode(
           'sequence builders'       ,				'multi-sequence build'       ,				add_special_tokens=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase						)

       _A     =  tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase						)
       _A     =  tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase       ,				_UpperCAmelCase						)

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode

 def      lowerCAmelCase_				(					self			:			List[str]						):
       _A     =  self.get_tokenizer()

       _A     =  'Encode this sequence.'
       _A     =  tokenizer.byte_encoder[' '.encode('utf-8'						)[0]]

       # Testing encoder arguments
       _A     =  tokenizer.encode(_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase						)
       _A     =  tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
       self.assertNotEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)

       _A     =  tokenizer.encode(_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase						)
       _A     =  tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
       self.assertEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)

       tokenizer.add_special_tokens({'bos_token': '<s>'}						)
       _A     =  tokenizer.encode(_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase						)
       _A     =  tokenizer.convert_ids_to_tokens(encoded[1]						)[0]
       self.assertNotEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)

       # Testing spaces after special tokens
       _A     =  '<mask>'
       tokenizer.add_special_tokens(
           {'mask_token': AddedToken(_UpperCAmelCase       ,				lstrip=_UpperCAmelCase       ,				rstrip=_UpperCAmelCase						)}						)  # mask token has a left space
       _A     =  tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)

       _A     =  'Encode <mask> sequence'
       _A     =  'Encode <mask>sequence'

       _A     =  tokenizer.encode(_UpperCAmelCase						)
       _A     =  encoded.index(_UpperCAmelCase						)
       _A     =  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
       self.assertEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)

       _A     =  tokenizer.encode(_UpperCAmelCase						)
       _A     =  encoded.index(_UpperCAmelCase						)
       _A     =  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
       self.assertNotEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			int						):
       pass

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
                   _A     =  self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase       ,				**_UpperCAmelCase						)
                   _A     =  self.tokenizer_class.from_pretrained(_UpperCAmelCase       ,				**_UpperCAmelCase						)
                   _A     =  'A, <mask> AllenNLP sentence.'
                   _A     =  tokenizer_r.encode_plus(_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase       ,				return_token_type_ids=_UpperCAmelCase						)
                   _A     =  tokenizer_p.encode_plus(_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase       ,				return_token_type_ids=_UpperCAmelCase						)

                   # token_type_ids should put 0 everywhere
                   self.assertEqual(sum(tokens_r['token_type_ids']						)       ,				sum(tokens_p['token_type_ids']						)						)

                   # attention_mask should put 1 everywhere, so sum over length should be 1
                   self.assertEqual(
                       sum(tokens_r['attention_mask']						) / len(tokens_r['attention_mask']						)       ,				sum(tokens_p['attention_mask']						) / len(tokens_p['attention_mask']						)       ,				)

                   _A     =  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']						)
                   _A     =  tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']						)

                   # Rust correctly handles the space before the mask while python doesnt
                   self.assertSequenceEqual(tokens_p['input_ids']       ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]						)
                   self.assertSequenceEqual(tokens_r['input_ids']       ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]						)

                   self.assertSequenceEqual(
                       _UpperCAmelCase       ,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']						)
                   self.assertSequenceEqual(
                       _UpperCAmelCase       ,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       for trim_offsets, add_prefix_space in itertools.product([True, False]       ,				repeat=2						):
             _A     =  self.rust_tokenizer_class.from_pretrained(
                 self.tmpdirname       ,				use_fast=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase       ,				trim_offsets=_UpperCAmelCase						)

             _A     =  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()						)
             _A     =  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()						)

             self.assertEqual(pre_tokenizer_state['add_prefix_space']       ,				_UpperCAmelCase						)

             self.assertEqual(post_processor_state['add_prefix_space']       ,				_UpperCAmelCase						)
             self.assertEqual(post_processor_state['trim_offsets']       ,				_UpperCAmelCase						)



 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
       # `trim_offsets`
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
                   _A     =  'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                   _A     =  F'''{text_of_1_token} {text_of_1_token}'''

                   _A     =  self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,				use_fast=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase       ,				trim_offsets=_UpperCAmelCase						)
                   _A     =  tokenizer_r(_UpperCAmelCase       ,				return_offsets_mapping=_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase						)
                   self.assertEqual(encoding.offset_mapping[0]       ,				(0, len(_UpperCAmelCase						))						)
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,				(len(_UpperCAmelCase						) + 1, len(_UpperCAmelCase						) + 1 + len(_UpperCAmelCase						))       ,				)

                   _A     =  self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,				use_fast=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase       ,				trim_offsets=_UpperCAmelCase						)
                   _A     =  tokenizer_r(_UpperCAmelCase       ,				return_offsets_mapping=_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase						)
                   self.assertEqual(encoding.offset_mapping[0]       ,				(0, len(_UpperCAmelCase						))						)
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,				(len(_UpperCAmelCase						) + 1, len(_UpperCAmelCase						) + 1 + len(_UpperCAmelCase						))       ,				)

                   _A     =  self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,				use_fast=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase       ,				trim_offsets=_UpperCAmelCase						)
                   _A     =  tokenizer_r(_UpperCAmelCase       ,				return_offsets_mapping=_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase						)
                   self.assertEqual(encoding.offset_mapping[0]       ,				(0, len(_UpperCAmelCase						))						)
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,				(len(_UpperCAmelCase						), len(_UpperCAmelCase						) + 1 + len(_UpperCAmelCase						))       ,				)

                   _A     =  self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,				use_fast=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase       ,				trim_offsets=_UpperCAmelCase						)
                   _A     =  tokenizer_r(_UpperCAmelCase       ,				return_offsets_mapping=_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase						)
                   self.assertEqual(encoding.offset_mapping[0]       ,				(0, len(_UpperCAmelCase						))						)
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,				(len(_UpperCAmelCase						), len(_UpperCAmelCase						) + 1 + len(_UpperCAmelCase						))       ,				)

                   _A     =  F''' {text}'''

                   # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                   #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                   # )
                   # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                   # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                   # self.assertEqual(
                   #     encoding.offset_mapping[1],
                   #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                   # )

                   _A     =  self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,				use_fast=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase       ,				trim_offsets=_UpperCAmelCase						)
                   _A     =  tokenizer_r(_UpperCAmelCase       ,				return_offsets_mapping=_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase						)
                   self.assertEqual(encoding.offset_mapping[0]       ,				(1, 1 + len(_UpperCAmelCase						))						)
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,				(1 + len(_UpperCAmelCase						) + 1, 1 + len(_UpperCAmelCase						) + 1 + len(_UpperCAmelCase						))       ,				)

                   _A     =  self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,				use_fast=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase       ,				trim_offsets=_UpperCAmelCase						)
                   _A     =  tokenizer_r(_UpperCAmelCase       ,				return_offsets_mapping=_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase						)
                   self.assertEqual(encoding.offset_mapping[0]       ,				(0, 1 + len(_UpperCAmelCase						))						)
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,				(1 + len(_UpperCAmelCase						), 1 + len(_UpperCAmelCase						) + 1 + len(_UpperCAmelCase						))       ,				)

                   _A     =  self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,				use_fast=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase       ,				trim_offsets=_UpperCAmelCase						)
                   _A     =  tokenizer_r(_UpperCAmelCase       ,				return_offsets_mapping=_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase						)
                   self.assertEqual(encoding.offset_mapping[0]       ,				(0, 1 + len(_UpperCAmelCase						))						)
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,				(1 + len(_UpperCAmelCase						), 1 + len(_UpperCAmelCase						) + 1 + len(_UpperCAmelCase						))       ,				)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__      			=						logging.get_logger(__name__)

UpperCAmelCase__      			=						{
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """glpn"""



						def __init__(self:					Tuple ,					__UpperCAmelCase:					Tuple=3 ,					__UpperCAmelCase:					Dict=4 ,					__UpperCAmelCase:					Any=[2, 2, 2, 2] ,					__UpperCAmelCase:					Optional[int]=[8, 4, 2, 1] ,					__UpperCAmelCase:					Dict=[32, 64, 160, 256] ,					__UpperCAmelCase:					List[str]=[7, 3, 3, 3] ,					__UpperCAmelCase:					Dict=[4, 2, 2, 2] ,					__UpperCAmelCase:					Optional[int]=[1, 2, 5, 8] ,					__UpperCAmelCase:					Dict=[4, 4, 4, 4] ,					__UpperCAmelCase:					List[str]="gelu" ,					__UpperCAmelCase:					str=0.0 ,					__UpperCAmelCase:					List[Any]=0.0 ,					__UpperCAmelCase:					Dict=0.02 ,					__UpperCAmelCase:					List[str]=0.1 ,					__UpperCAmelCase:					Union[str, Any]=1E-6 ,					__UpperCAmelCase:					Dict=64 ,					__UpperCAmelCase:					Dict=10 ,					__UpperCAmelCase:					Union[str, Any]=-1 ,					**__UpperCAmelCase:					Dict ,					)    ->     str:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase   )

											__a					:						Optional[Any]      =					num_channels
											__a					:						Tuple      =					num_encoder_blocks
											__a					:						int      =					depths
											__a					:						Union[str, Any]      =					sr_ratios
											__a					:						str      =					hidden_sizes
											__a					:						List[Any]      =					patch_sizes
											__a					:						Optional[Any]      =					strides
											__a					:						Any      =					mlp_ratios
											__a					:						Any      =					num_attention_heads
											__a					:						Any      =					hidden_act
											__a					:						List[Any]      =					hidden_dropout_prob
											__a					:						int      =					attention_probs_dropout_prob
											__a					:						List[str]      =					initializer_range
											__a					:						Optional[int]      =					drop_path_rate
											__a					:						Any      =					layer_norm_eps
											__a					:						List[Any]      =					decoder_hidden_size
											__a					:						Any      =					max_depth
											__a					:						int      =					head_in_index





