




from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
  import timm


if is_torch_available():
  from torch import Tensor


class 							UpperCamelCase_       (					UpperCAmelCase__						,   UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''pixel_values'''
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = TimmBackboneConfig


    def __init__(     self :      Dict   ,					UpperCAmelCase__ :      Optional[int]   ,					**UpperCAmelCase__ :      Tuple)		->Optional[Any]:





           '''simple docstring'''

           requires_backends(self   ,					'''timm''')
           super().__init__(UpperCAmelCase__)
           A__ 					=       config

           if config.backbone is None:
                  raise ValueError('''backbone is not set in the config. Please set it to a timm model name.''')

           if config.backbone not in timm.list_models():
                  raise ValueError(f"""backbone {config.backbone} is not supported by timm.""")

           if hasattr(UpperCAmelCase__   ,					'''out_features''') and config.out_features is not None:
                  raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.''')

           A__ 					=       getattr(UpperCAmelCase__   ,					'''use_pretrained_backbone'''   ,					UpperCAmelCase__)
           if pretrained is None:
                  raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.''')

           # We just take the final layer by default. This matches the default for the transformers models.
           A__ 					=       config.out_indices if getattr(UpperCAmelCase__   ,					'''out_indices'''   ,					UpperCAmelCase__) is not None else (-1,)

           A__ 					=       timm.create_model(
               config.backbone   ,					pretrained=UpperCAmelCase__   ,					features_only=config.features_only   ,					in_chans=config.num_channels   ,					out_indices=UpperCAmelCase__   ,					**UpperCAmelCase__   ,					)
           # These are used to control the output of the model when called. If output_hidden_states is True, then
           # return_layers is modified to include all layers.
           A__ 					=       self._backbone.return_layers
           A__ 					=       {layer['''module''']: str(UpperCAmelCase__) for i, layer in enumerate(self._backbone.feature_info.info)}
           super()._init_backbone(UpperCAmelCase__)


    @classmethod
    def 			SCREAMING_SNAKE_CASE				(     cls :      Any   ,					UpperCAmelCase__ :      str   ,					*UpperCAmelCase__ :      List[Any]   ,					**UpperCAmelCase__ :      Union[str, Any])		->Union[str, Any]:





           '''simple docstring'''

           requires_backends(cls   ,					['''vision''', '''timm'''])
           from ...models.timm_backbone import TimmBackboneConfig

           A__ 					=       kwargs.pop('''config'''   ,					TimmBackboneConfig())

           A__ 					=       kwargs.pop('''use_timm_backbone'''   ,					UpperCAmelCase__)
           if not use_timm:
                  raise ValueError('''use_timm_backbone must be True for timm backbones''')

           A__ 					=       kwargs.pop('''num_channels'''   ,					config.num_channels)
           A__ 					=       kwargs.pop('''features_only'''   ,					config.features_only)
           A__ 					=       kwargs.pop('''use_pretrained_backbone'''   ,					config.use_pretrained_backbone)
           A__ 					=       kwargs.pop('''out_indices'''   ,					config.out_indices)
           A__ 					=       TimmBackboneConfig(
               backbone=UpperCAmelCase__   ,					num_channels=UpperCAmelCase__   ,					features_only=UpperCAmelCase__   ,					use_pretrained_backbone=UpperCAmelCase__   ,					out_indices=UpperCAmelCase__   ,					)
           return super()._from_config(UpperCAmelCase__   ,					**UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any]   ,					UpperCAmelCase__ :      List[str])		->int:





           '''simple docstring'''

           pass





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      Union[str, Any]=None   ,					UpperCAmelCase__ :      Any=None   ,					UpperCAmelCase__ :      Any=None   ,					**UpperCAmelCase__ :      List[str])		->Union[BackboneOutput, Tuple[Tensor, ...]]:





           '''simple docstring'''

           A__ 					=       return_dict if return_dict is not None else self.config.use_return_dict
           A__ 					=       (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           A__ 					=       output_attentions if output_attentions is not None else self.config.output_attentions

           if output_attentions:
                  raise ValueError('''Cannot output attentions for timm backbones at the moment''')

           if output_hidden_states:
                  # We modify the return layers to include all the stages of the backbone
                  A__ 					=       self._all_layers
                  A__ 					=       self._backbone(UpperCAmelCase__   ,					**UpperCAmelCase__)
                  A__ 					=       self._return_layers
                  A__ 					=       tuple(hidden_states[i] for i in self.out_indices)
           else:
                  A__ 					=       self._backbone(UpperCAmelCase__   ,					**UpperCAmelCase__)
                  A__ 					=       None

           A__ 					=       tuple(UpperCAmelCase__)
           A__ 					=       tuple(UpperCAmelCase__) if hidden_states is not None else None

           if not return_dict:
                  A__ 					=       (feature_maps,)
                  if output_hidden_states:
                         A__ 					=       output + (hidden_states,)
                  return output

           return BackboneOutput(feature_maps=UpperCAmelCase__   ,					hidden_states=UpperCAmelCase__   ,					attentions=UpperCAmelCase__)








"""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 lowerCAmelCase__      (	lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     RobertaTokenizer
			lowerCamelCase__ =     RobertaTokenizerFast
			lowerCamelCase__ =     True
			lowerCamelCase__ =     {"""cls_token""": """<s>"""}
			def    A_       (    self    ):
							super().setUp()

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

							_lowerCamelCase	:     Optional[int]       =       os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES['vocab_file']    )
							_lowerCamelCase	:     Optional[Any]       =       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(lowercase    ) + '\n'    )
							with open(self.merges_file    , 'w'    , encoding='utf-8'    ) as fp:
											fp.write('\n'.join(lowercase    )    )
			def    A_       (    self    , **lowercase    ):
							kwargs.update(self.special_tokens_map    )
							return self.tokenizer_class.from_pretrained(self.tmpdirname    , **lowercase    )
			def    A_       (    self    , **lowercase    ):
							kwargs.update(self.special_tokens_map    )
							return RobertaTokenizerFast.from_pretrained(self.tmpdirname    , **lowercase    )
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     Any       =       'lower newer'
							_lowerCamelCase	:     Union[str, Any]       =       'lower newer'
							return input_text, output_text
			def    A_       (    self    ):
							_lowerCamelCase	:     Dict       =       self.tokenizer_class(self.vocab_file    , self.merges_file    , **self.special_tokens_map    )
							_lowerCamelCase	:     List[str]       =       'lower newer'
							_lowerCamelCase	:     Tuple       =       ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
							_lowerCamelCase	:     List[str]       =       tokenizer.tokenize(lowercase    )  # , add_prefix_space=True)
							self.assertListEqual(lowercase    , lowercase    )

							_lowerCamelCase	:     List[Any]       =       tokens + [tokenizer.unk_token]
							_lowerCamelCase	:     str       =       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase    )    , lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       self.get_tokenizer()

							self.assertListEqual(tokenizer.encode('Hello world!'    , add_special_tokens=lowercase    )    , [0, 31414, 232, 328, 2]    )
							self.assertListEqual(
							    tokenizer.encode('Hello world! cécé herlolip 418'    , add_special_tokens=lowercase    )    , [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]    , )
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     Dict       =       self.tokenizer_class.from_pretrained('roberta-base'    )

							_lowerCamelCase	:     Tuple       =       tokenizer.encode('sequence builders'    , add_special_tokens=lowercase    )
							_lowerCamelCase	:     int       =       tokenizer.encode('multi-sequence build'    , add_special_tokens=lowercase    )

							_lowerCamelCase	:     List[Any]       =       tokenizer.encode(
							    'sequence builders'    , add_special_tokens=lowercase    , add_prefix_space=lowercase    )
							_lowerCamelCase	:     List[str]       =       tokenizer.encode(
							    'sequence builders'    , 'multi-sequence build'    , add_special_tokens=lowercase    , add_prefix_space=lowercase    )

							_lowerCamelCase	:     Dict       =       tokenizer.build_inputs_with_special_tokens(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       tokenizer.build_inputs_with_special_tokens(lowercase    , lowercase    )

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode
			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       self.get_tokenizer()

							_lowerCamelCase	:     str       =       'Encode this sequence.'
							_lowerCamelCase	:     Optional[int]       =       tokenizer.byte_encoder[' '.encode('utf-8'    )[0]]

							# Testing encoder arguments
							_lowerCamelCase	:     Optional[int]       =       tokenizer.encode(lowercase    , add_special_tokens=lowercase    , add_prefix_space=lowercase    )
							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
							self.assertNotEqual(lowercase    , lowercase    )

							_lowerCamelCase	:     List[str]       =       tokenizer.encode(lowercase    , add_special_tokens=lowercase    , add_prefix_space=lowercase    )
							_lowerCamelCase	:     List[Any]       =       tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
							self.assertEqual(lowercase    , lowercase    )

							tokenizer.add_special_tokens({'bos_token': '<s>'}    )
							_lowerCamelCase	:     Optional[Any]       =       tokenizer.encode(lowercase    , add_special_tokens=lowercase    )
							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.convert_ids_to_tokens(encoded[1]    )[0]
							self.assertNotEqual(lowercase    , lowercase    )

							# Testing spaces after special tokens
							_lowerCamelCase	:     List[Any]       =       '<mask>'
							tokenizer.add_special_tokens(
							    {'mask_token': AddedToken(lowercase    , lstrip=lowercase    , rstrip=lowercase    )}    )  # mask token has a left space
							_lowerCamelCase	:     List[str]       =       tokenizer.convert_tokens_to_ids(lowercase    )

							_lowerCamelCase	:     Optional[int]       =       'Encode <mask> sequence'
							_lowerCamelCase	:     int       =       'Encode <mask>sequence'

							_lowerCamelCase	:     str       =       tokenizer.encode(lowercase    )
							_lowerCamelCase	:     Tuple       =       encoded.index(lowercase    )
							_lowerCamelCase	:     int       =       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
							self.assertEqual(lowercase    , lowercase    )

							_lowerCamelCase	:     Any       =       tokenizer.encode(lowercase    )
							_lowerCamelCase	:     Union[str, Any]       =       encoded.index(lowercase    )
							_lowerCamelCase	:     int       =       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
							self.assertNotEqual(lowercase    , lowercase    )
			def    A_       (    self    ):
							pass
			def    A_       (    self    ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
															_lowerCamelCase	:     Optional[Any]       =       self.rust_tokenizer_class.from_pretrained(lowercase    , **lowercase    )
															_lowerCamelCase	:     Dict       =       self.tokenizer_class.from_pretrained(lowercase    , **lowercase    )
															_lowerCamelCase	:     List[str]       =       'A, <mask> AllenNLP sentence.'
															_lowerCamelCase	:     List[Any]       =       tokenizer_r.encode_plus(lowercase    , add_special_tokens=lowercase    , return_token_type_ids=lowercase    )
															_lowerCamelCase	:     Union[str, Any]       =       tokenizer_p.encode_plus(lowercase    , add_special_tokens=lowercase    , return_token_type_ids=lowercase    )

															# 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']    )    , )

															_lowerCamelCase	:     List[Any]       =       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']    )
															_lowerCamelCase	:     Tuple       =       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, 50264, 3823, 487, 21992, 3645, 4, 2]    )
															self.assertSequenceEqual(tokens_r['input_ids']    , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]    )

															self.assertSequenceEqual(
															    lowercase    , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']    )
															self.assertSequenceEqual(
															    lowercase    , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']    )
			def    A_       (    self    ):
							for trim_offsets, add_prefix_space in itertools.product([True, False]    , repeat=2    ):
											_lowerCamelCase	:     Union[str, Any]       =       self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname    , use_fast=lowercase    , add_prefix_space=lowercase    , trim_offsets=lowercase    )

											_lowerCamelCase	:     Optional[int]       =       json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()    )
											_lowerCamelCase	:     Dict       =       json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()    )

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

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






			def    A_       (    self    ):
							# 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})'''    ):
															_lowerCamelCase	:     Dict       =       'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
															_lowerCamelCase	:     Tuple       =       F'''{text_of_1_token} {text_of_1_token}'''

															_lowerCamelCase	:     Dict       =       self.rust_tokenizer_class.from_pretrained(
															    lowercase    , use_fast=lowercase    , add_prefix_space=lowercase    , trim_offsets=lowercase    )
															_lowerCamelCase	:     List[Any]       =       tokenizer_r(lowercase    , return_offsets_mapping=lowercase    , add_special_tokens=lowercase    )
															self.assertEqual(encoding.offset_mapping[0]    , (0, len(lowercase    ))    )
															self.assertEqual(
															    encoding.offset_mapping[1]    , (len(lowercase    ) + 1, len(lowercase    ) + 1 + len(lowercase    ))    , )

															_lowerCamelCase	:     int       =       self.rust_tokenizer_class.from_pretrained(
															    lowercase    , use_fast=lowercase    , add_prefix_space=lowercase    , trim_offsets=lowercase    )
															_lowerCamelCase	:     Union[str, Any]       =       tokenizer_r(lowercase    , return_offsets_mapping=lowercase    , add_special_tokens=lowercase    )
															self.assertEqual(encoding.offset_mapping[0]    , (0, len(lowercase    ))    )
															self.assertEqual(
															    encoding.offset_mapping[1]    , (len(lowercase    ) + 1, len(lowercase    ) + 1 + len(lowercase    ))    , )

															_lowerCamelCase	:     Union[str, Any]       =       self.rust_tokenizer_class.from_pretrained(
															    lowercase    , use_fast=lowercase    , add_prefix_space=lowercase    , trim_offsets=lowercase    )
															_lowerCamelCase	:     List[str]       =       tokenizer_r(lowercase    , return_offsets_mapping=lowercase    , add_special_tokens=lowercase    )
															self.assertEqual(encoding.offset_mapping[0]    , (0, len(lowercase    ))    )
															self.assertEqual(
															    encoding.offset_mapping[1]    , (len(lowercase    ), len(lowercase    ) + 1 + len(lowercase    ))    , )

															_lowerCamelCase	:     Dict       =       self.rust_tokenizer_class.from_pretrained(
															    lowercase    , use_fast=lowercase    , add_prefix_space=lowercase    , trim_offsets=lowercase    )
															_lowerCamelCase	:     int       =       tokenizer_r(lowercase    , return_offsets_mapping=lowercase    , add_special_tokens=lowercase    )
															self.assertEqual(encoding.offset_mapping[0]    , (0, len(lowercase    ))    )
															self.assertEqual(
															    encoding.offset_mapping[1]    , (len(lowercase    ), len(lowercase    ) + 1 + len(lowercase    ))    , )

															_lowerCamelCase	:     int       =       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)),
															# )

															_lowerCamelCase	:     Optional[Any]       =       self.rust_tokenizer_class.from_pretrained(
															    lowercase    , use_fast=lowercase    , add_prefix_space=lowercase    , trim_offsets=lowercase    )
															_lowerCamelCase	:     Optional[int]       =       tokenizer_r(lowercase    , return_offsets_mapping=lowercase    , add_special_tokens=lowercase    )
															self.assertEqual(encoding.offset_mapping[0]    , (1, 1 + len(lowercase    ))    )
															self.assertEqual(
															    encoding.offset_mapping[1]    , (1 + len(lowercase    ) + 1, 1 + len(lowercase    ) + 1 + len(lowercase    ))    , )

															_lowerCamelCase	:     Optional[int]       =       self.rust_tokenizer_class.from_pretrained(
															    lowercase    , use_fast=lowercase    , add_prefix_space=lowercase    , trim_offsets=lowercase    )
															_lowerCamelCase	:     Union[str, Any]       =       tokenizer_r(lowercase    , return_offsets_mapping=lowercase    , add_special_tokens=lowercase    )
															self.assertEqual(encoding.offset_mapping[0]    , (0, 1 + len(lowercase    ))    )
															self.assertEqual(
															    encoding.offset_mapping[1]    , (1 + len(lowercase    ), 1 + len(lowercase    ) + 1 + len(lowercase    ))    , )

															_lowerCamelCase	:     Union[str, Any]       =       self.rust_tokenizer_class.from_pretrained(
															    lowercase    , use_fast=lowercase    , add_prefix_space=lowercase    , trim_offsets=lowercase    )
															_lowerCamelCase	:     Optional[Any]       =       tokenizer_r(lowercase    , return_offsets_mapping=lowercase    , add_special_tokens=lowercase    )
															self.assertEqual(encoding.offset_mapping[0]    , (0, 1 + len(lowercase    ))    )
															self.assertEqual(
															    encoding.offset_mapping[1]    , (1 + len(lowercase    ), 1 + len(lowercase    ) + 1 + len(lowercase    ))    , )