
"""simple docstring"""


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   __a  (      _lowerCAmelCase    ,     _lowerCAmelCase	):
       UpperCamelCase_		:					Optional[Any]   =			'''pixel_values'''
       UpperCamelCase_		:					Tuple   =			False
       UpperCamelCase_		:					Any   =			TimmBackboneConfig






       def __init__(  self :							Optional[Any]       ,      UpperCAmelCase_ :							Dict       ,      **UpperCAmelCase_ :							str			)->  List[str]:







              """simple docstring"""
              requires_backends(self       ,      "timm"			)
              super().__init__(UpperCAmelCase_			)
              UpperCamelCase					 =		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."			)

              UpperCamelCase					 =		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.
              UpperCamelCase					 =		config.out_indices if getattr(UpperCAmelCase_       ,      "out_indices"       ,      UpperCAmelCase_			) is not None else (-1,)

              UpperCamelCase					 =		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.
              UpperCamelCase					 =		self._backbone.return_layers
              UpperCamelCase					 =		{layer["module"]: str(UpperCAmelCase_			) for i, layer in enumerate(self._backbone.feature_info.info			)}
              super()._init_backbone(UpperCAmelCase_			)






       @classmethod
       def 	_SCREAMING_SNAKE_CASE       (  cls :							List[str]       ,      UpperCAmelCase_ :							Optional[Any]       ,      *UpperCAmelCase_ :							Dict       ,      **UpperCAmelCase_ :							Optional[Any]			)->  Optional[Any]:







              """simple docstring"""
              requires_backends(cls       ,      ["vision", "timm"]			)
              from ...models.timm_backbone import TimmBackboneConfig

              UpperCamelCase					 =		kwargs.pop("config"       ,      TimmBackboneConfig()			)

              UpperCamelCase					 =		kwargs.pop("use_timm_backbone"       ,      UpperCAmelCase_			)
              if not use_timm:
                     raise ValueError("use_timm_backbone must be True for timm backbones"			)

              UpperCamelCase					 =		kwargs.pop("num_channels"       ,      config.num_channels			)
              UpperCamelCase					 =		kwargs.pop("features_only"       ,      config.features_only			)
              UpperCamelCase					 =		kwargs.pop("use_pretrained_backbone"       ,      config.use_pretrained_backbone			)
              UpperCamelCase					 =		kwargs.pop("out_indices"       ,      config.out_indices			)
              UpperCamelCase					 =		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_ :							Dict			)->  Union[str, Any]:







              """simple docstring"""
              pass






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







              """simple docstring"""
              UpperCamelCase					 =		return_dict if return_dict is not None else self.config.use_return_dict
              UpperCamelCase					 =		(
                  output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
              )
              UpperCamelCase					 =		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
                     UpperCamelCase					 =		self._all_layers
                     UpperCamelCase					 =		self._backbone(UpperCAmelCase_       ,      **UpperCAmelCase_			)
                     UpperCamelCase					 =		self._return_layers
                     UpperCamelCase					 =		tuple(hidden_states[i] for i in self.out_indices			)
              else:
                     UpperCamelCase					 =		self._backbone(UpperCAmelCase_       ,      **UpperCAmelCase_			)
                     UpperCamelCase					 =		None

              UpperCamelCase					 =		tuple(UpperCAmelCase_			)
              UpperCamelCase					 =		tuple(UpperCAmelCase_			) if hidden_states is not None else None

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

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







"""simple docstring"""


import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					str   =			FunnelTokenizer
       UpperCamelCase_		:					Any   =			FunnelTokenizerFast
       UpperCamelCase_		:					Union[str, Any]   =			True
       UpperCamelCase_		:					Tuple   =			True






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[int]:







              """simple docstring"""
              super().setUp()

              UpperCamelCase					 =		[
                  "<unk>",
                  "<cls>",
                  "<sep>",
                  "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 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      **UpperCAmelCase_ :							Optional[Any]			)->  str:







              """simple docstring"""
              return FunnelTokenizer.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






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







              """simple docstring"""
              return FunnelTokenizerFast.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							Tuple			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		"UNwant\u00E9d,running"
              UpperCamelCase					 =		"unwanted, running"
              return input_text, output_text






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		self.tokenizer_class(self.vocab_file			)

              UpperCamelCase					 =		tokenizer.tokenize("UNwant\u00E9d,running"			)
              self.assertListEqual(UpperCAmelCase_       ,      ["un", "##want", "##ed", ",", "runn", "##ing"]			)
              self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_			)       ,      [7, 4, 5, 10, 8, 9]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		self.get_tokenizers(do_lower_case=UpperCAmelCase_			)
              for tokenizer in tokenizers:
                     UpperCamelCase					 =		tokenizer("UNwant\u00E9d,running"			)
                     UpperCamelCase					 =		len(inputs["input_ids"]			) - 1
                     self.assertListEqual(inputs["token_type_ids"]       ,      [2] + [0] * sentence_len			)

                     UpperCamelCase					 =		tokenizer("UNwant\u00E9d,running"       ,      "UNwant\u00E9d,running"			)
                     self.assertListEqual(inputs["token_type_ids"]       ,      [2] + [0] * sentence_len + [1] * sentence_len			)





