




import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


_snake_case            = logging.get_logger(__name__)

# General docstring
_snake_case            = "PoolFormerConfig"

# Base docstring
_snake_case            = "sail/poolformer_s12"
_snake_case            = [1, 512, 7, 7]

# Image classification docstring
_snake_case            = "sail/poolformer_s12"
_snake_case            = "tabby, tabby cat"

_snake_case            = [
    "sail/poolformer_s12",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]







def        lowerCAmelCase_			(  snake_case_,snake_case_ = 0.0,snake_case_ = False  ):
      if drop_prob == 0.0 or not training:
            return input
      _A							:  List[str]    =		1 - drop_prob
      _A							:  Optional[int]    =		(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
      _A							:  str    =		keep_prob + torch.rand(snake_case_,dtype=input.dtype,device=input.device  )
      random_tensor.floor_()  # binarize
      _A							:  List[str]    =		input.div(snake_case_  ) * random_tensor
      return output







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a = None       )		->							None:
           super().__init__()
           _A							:  List[str]    =		drop_prob

     def       a__			(						self	,  _a       )		->							torch.Tensor:
           return drop_path(_a	,  self.drop_prob	,  self.training       )



     def       a__			(						self       )		->							str:
           return "p={}".format(self.drop_prob       )







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a=None       )		->							List[str]:
           super().__init__()
           _A							:  int    =		patch_size if isinstance(_a	,  collections.abc.Iterable       ) else (patch_size, patch_size)
           _A							:  Optional[Any]    =		stride if isinstance(_a	,  collections.abc.Iterable       ) else (stride, stride)
           _A							:  Dict    =		padding if isinstance(_a	,  collections.abc.Iterable       ) else (padding, padding)

           _A							:  Optional[Any]    =		nn.Convad(_a	,  _a	,  kernel_size=_a	,  stride=_a	,  padding=_a       )
           _A							:  str    =		norm_layer(_a       ) if norm_layer else nn.Identity()



     def       a__			(						self	,  _a       )		->							List[Any]:
           _A							:  Any    =		self.projection(_a       )
           _A							:  List[Any]    =		self.norm(_a       )
           return embeddings







class 		lowercase							(     nn.GroupNorm	):

     def __init__(						self	,  _a	,  **_a       )		->							List[Any]:
           super().__init__(1	,  _a	,  **_a       )







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a       )		->							int:
           super().__init__()
           _A							:  Union[str, Any]    =		nn.AvgPoolad(_a	,  stride=1	,  padding=pool_size // 2	,  count_include_pad=_a       )



     def       a__			(						self	,  _a       )		->							Tuple:
           return self.pool(_a       ) - hidden_states







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a	,  _a	,  _a	,  _a       )		->							int:
           super().__init__()
           _A							:  Tuple    =		nn.Convad(_a	,  _a	,  1       )
           _A							:  Tuple    =		nn.Convad(_a	,  _a	,  1       )
           _A							:  Any    =		PoolFormerDropPath(_a       )
           if isinstance(config.hidden_act	,  _a       ):
                 _A							:  Optional[int]    =		ACTaFN[config.hidden_act]
           else:
                 _A							:  Optional[Any]    =		config.hidden_act



     def       a__			(						self	,  _a       )		->							Any:
           _A							:  Tuple    =		self.conva(_a       )
           _A							:  int    =		self.act_fn(_a       )
           _A							:  List[str]    =		self.drop(_a       )
           _A							:  Union[str, Any]    =		self.conva(_a       )
           _A							:  Optional[int]    =		self.drop(_a       )

           return hidden_states







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a       )		->							Union[str, Any]:
           super().__init__()
           _A							:  List[str]    =		PoolFormerPooling(_a       )
           _A							:  int    =		PoolFormerOutput(_a	,  _a	,  _a	,  _a       )
           _A							:  Optional[Any]    =		PoolFormerGroupNorm(_a       )
           _A							:  Optional[Any]    =		PoolFormerGroupNorm(_a       )

           # Useful for training neural nets
           _A							:  List[Any]    =		PoolFormerDropPath(_a       ) if drop_path > 0.0 else nn.Identity()
           _A							:  List[str]    =		config.use_layer_scale
           if config.use_layer_scale:
                 _A							:  Tuple    =		nn.Parameter(
                     config.layer_scale_init_value * torch.ones((_a)       )	,  requires_grad=_a       )
                 _A							:  Tuple    =		nn.Parameter(
                     config.layer_scale_init_value * torch.ones((_a)       )	,  requires_grad=_a       )



     def       a__			(						self	,  _a       )		->							Union[str, Any]:
           if self.use_layer_scale:
                 _A							:  Optional[Any]    =		self.pooling(self.before_norm(_a       )       )
                 _A							:  Union[str, Any]    =		self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * pooling_output
                 # First residual connection
                 _A							:  Union[str, Any]    =		hidden_states + self.drop_path(_a       )
                 _A							:  List[Any]    =		()

                 _A							:  List[str]    =		self.output(self.after_norm(_a       )       )
                 _A							:  Optional[Any]    =		self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * layer_output
                 # Second residual connection
                 _A							:  List[Any]    =		hidden_states + self.drop_path(_a       )

                 _A							:  Union[str, Any]    =		(output,) + outputs
                 return outputs

           else:
                 _A							:  int    =		self.drop_path(self.pooling(self.before_norm(_a       )       )       )
                 # First residual connection
                 _A							:  int    =		pooling_output + hidden_states
                 _A							:  Dict    =		()

                 # Second residual connection inside the PoolFormerOutput block
                 _A							:  int    =		self.drop_path(self.output(self.after_norm(_a       )       )       )
                 _A							:  Union[str, Any]    =		hidden_states + layer_output

                 _A							:  Dict    =		(output,) + outputs
                 return outputs







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a       )		->							Optional[int]:
           super().__init__()
           _A							:  Union[str, Any]    =		config
           # stochastic depth decay rule
           _A							:  Union[str, Any]    =		[x.item() for x in torch.linspace(0	,  config.drop_path_rate	,  sum(config.depths       )       )]

           # patch embeddings
           _A							:  Optional[Any]    =		[]
           for i in range(config.num_encoder_blocks       ):
                 embeddings.append(
                     PoolFormerEmbeddings(
                         patch_size=config.patch_sizes[i]	,  stride=config.strides[i]	,  padding=config.padding[i]	,  num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]	,  hidden_size=config.hidden_sizes[i]	,  )       )
           _A							:  Optional[int]    =		nn.ModuleList(_a       )

           # Transformer blocks
           _A							:  Optional[Any]    =		[]
           _A							:  Tuple    =		0
           for i in range(config.num_encoder_blocks       ):
                 # each block consists of layers
                 _A							:  List[Any]    =		[]
                 if i != 0:
                       cur += config.depths[i - 1]
                 for j in range(config.depths[i]       ):
                       layers.append(
                           PoolFormerLayer(
                               _a	,  num_channels=config.hidden_sizes[i]	,  pool_size=config.pool_size	,  hidden_size=config.hidden_sizes[i]	,  intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio       )	,  drop_path=dpr[cur + j]	,  )       )
                 blocks.append(nn.ModuleList(_a       )       )

           _A							:  List[Any]    =		nn.ModuleList(_a       )



     def       a__			(						self	,  _a	,  _a=False	,  _a=True       )		->							Tuple:
           _A							:  Tuple    =		() if output_hidden_states else None

           _A							:  List[str]    =		pixel_values
           for idx, layers in enumerate(zip(self.patch_embeddings	,  self.block       )       ):
                 _A    ,     _A							:  Union[str, Any]    =		layers
                 # Get patch embeddings from hidden_states
                 _A							:  str    =		embedding_layer(_a       )
                 # Send the embeddings through the blocks
                 for _, blk in enumerate(_a       ):
                       _A							:  Optional[Any]    =		blk(_a       )
                       _A							:  List[str]    =		layer_outputs[0]

                 if output_hidden_states:
                       _A							:  Any    =		all_hidden_states + (hidden_states,)

           if not return_dict:
                 return tuple(v for v in [hidden_states, all_hidden_states] if v is not None       )

           return BaseModelOutputWithNoAttention(last_hidden_state=_a	,  hidden_states=_a       )







class 		lowercase							(     UpperCamelCase__	):
     _a											=		PoolFormerConfig
     _a											=		"poolformer"
     _a											=		"pixel_values"
     _a											=		True

     def       a__			(						self	,  _a       )		->							Tuple:
           if isinstance(_a	,  (nn.Linear, nn.Convad)       ):
                 module.weight.data.normal_(mean=0.0	,  std=self.config.initializer_range       )
                 if module.bias is not None:
                       module.bias.data.zero_()
           elif isinstance(_a	,  nn.LayerNorm       ):
                 module.bias.data.zero_()
                 module.weight.data.fill_(1.0       )



     def       a__			(						self	,  _a	,  _a=False       )		->							Union[str, Any]:
           if isinstance(_a	,  _a       ):
                 _A							:  List[str]    =		value


_snake_case            = r"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

_snake_case            = r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`PoolFormerImageProcessor.__call__`] for details.\n"







@add_start_docstrings(
    "The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.",UpperCamelCase__,)
class 		lowercase							(     UpperCamelCase__	):

     def __init__(						self	,  _a       )		->							List[str]:
           super().__init__(_a       )
           _A							:  Any    =		config

           _A							:  Union[str, Any]    =		PoolFormerEncoder(_a       )

           # Initialize weights and apply final processing
           self.post_init()

     def       a__			(						self       )		->							str:
           return self.embeddings.patch_embeddings



     @add_start_docstrings_to_model_forward(_a       )
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC	,  output_type=_a	,  config_class=_CONFIG_FOR_DOC	,  modality="""vision"""	,  expected_output=_EXPECTED_OUTPUT_SHAPE	,  )
     def       a__			(						self	,  _a = None	,  _a = None	,  _a = None	,  )		->							Union[Tuple, BaseModelOutputWithNoAttention]:
           _A							:  Optional[int]    =		(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           _A							:  List[str]    =		return_dict if return_dict is not None else self.config.use_return_dict

           if pixel_values is None:
                 raise ValueError("""You have to specify pixel_values"""       )

           _A							:  Any    =		self.encoder(
               _a	,  output_hidden_states=_a	,  return_dict=_a	,  )
           _A							:  Optional[int]    =		encoder_outputs[0]

           if not return_dict:
                 return (sequence_output, None) + encoder_outputs[1:]

           return BaseModelOutputWithNoAttention(
               last_hidden_state=_a	,  hidden_states=encoder_outputs.hidden_states	,  )







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a       )		->							Tuple:
           super().__init__()
           _A							:  List[str]    =		nn.Linear(config.hidden_size	,  config.hidden_size       )



     def       a__			(						self	,  _a       )		->							Tuple:
           _A							:  str    =		self.dense(_a       )
           return output







@add_start_docstrings(
    "\n    PoolFormer Model transformer with an image classification head on top\n    ",UpperCamelCase__,)
class 		lowercase							(     UpperCamelCase__	):

     def __init__(						self	,  _a       )		->							List[Any]:
           super().__init__(_a       )
           _A							:  Tuple    =		config.num_labels
           _A							:  Optional[Any]    =		PoolFormerModel(_a       )

           # Final norm
           _A							:  Optional[int]    =		PoolFormerGroupNorm(config.hidden_sizes[-1]       )
           # Classifier head
           _A							:  str    =		(
               nn.Linear(config.hidden_sizes[-1]	,  config.num_labels       ) if config.num_labels > 0 else nn.Identity()
           )

           # Initialize weights and apply final processing
           self.post_init()



     @add_start_docstrings_to_model_forward(_a       )
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT	,  output_type=_a	,  config_class=_CONFIG_FOR_DOC	,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT	,  )
     def       a__			(						self	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  )		->							Union[Tuple, ImageClassifierOutputWithNoAttention]:
           _A							:  Optional[Any]    =		return_dict if return_dict is not None else self.config.use_return_dict

           _A							:  Optional[Any]    =		self.poolformer(
               _a	,  output_hidden_states=_a	,  return_dict=_a	,  )

           _A							:  Tuple    =		outputs[0]

           _A							:  Any    =		self.classifier(self.norm(_a       ).mean([-2, -1]       )       )

           _A							:  Dict    =		None
           if labels is not None:
                 if self.config.problem_type is None:
                       if self.num_labels == 1:
                             _A							:  Union[str, Any]    =		"""regression"""
                       elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                             _A							:  Optional[int]    =		"""single_label_classification"""
                       else:
                             _A							:  List[Any]    =		"""multi_label_classification"""

                 if self.config.problem_type == "regression":
                       _A							:  Union[str, Any]    =		MSELoss()
                       if self.num_labels == 1:
                             _A							:  str    =		loss_fct(logits.squeeze()	,  labels.squeeze()       )
                       else:
                             _A							:  Optional[Any]    =		loss_fct(_a	,  _a       )
                 elif self.config.problem_type == "single_label_classification":
                       _A							:  List[str]    =		CrossEntropyLoss()
                       _A							:  Any    =		loss_fct(logits.view(-1	,  self.num_labels       )	,  labels.view(-1       )       )
                 elif self.config.problem_type == "multi_label_classification":
                       _A							:  List[Any]    =		BCEWithLogitsLoss()
                       _A							:  Dict    =		loss_fct(_a	,  _a       )

           if not return_dict:
                 _A							:  List[str]    =		(logits,) + outputs[2:]
                 return ((loss,) + output) if loss is not None else output

           return ImageClassifierOutputWithNoAttention(loss=_a	,  logits=_a	,  hidden_states=outputs.hidden_states       )







import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case            = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")







@require_sentencepiece
@require_tokenizers
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		PegasusTokenizer
     _a											=		PegasusTokenizerFast
     _a											=		True
     _a											=		True

     def       a__			(						self       )		->							int:
           super().setUp()

           # We have a SentencePiece fixture for testing
           _A							:  List[Any]    =		PegasusTokenizer(_a       )
           tokenizer.save_pretrained(self.tmpdirname       )

     @cached_property
     def       a__			(						self       )		->							int:
           return PegasusTokenizer.from_pretrained("""google/pegasus-large"""       )

     def       a__			(						self	,  **_a       )		->							PegasusTokenizer:
           return PegasusTokenizer.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  _a       )		->							List[Any]:
           return ("This is a test", "This is a test")

     def       a__			(						self       )		->							int:
           _A							:  Dict    =		"""</s>"""
           _A							:  Dict    =		1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a       )	,  _a       )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a       )	,  _a       )

     def       a__			(						self       )		->							Dict:
           _A							:  List[str]    =		list(self.get_tokenizer().get_vocab().keys()       )

           self.assertEqual(vocab_keys[0]	,  """<pad>"""       )
           self.assertEqual(vocab_keys[1]	,  """</s>"""       )
           self.assertEqual(vocab_keys[-1]	,  """v"""       )
           self.assertEqual(len(_a       )	,  1103       )

     def       a__			(						self       )		->							Optional[int]:
           self.assertEqual(self.get_tokenizer().vocab_size	,  1103       )

     def       a__			(						self       )		->							Tuple:
           _A							:  Any    =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  Optional[int]    =		self.tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  int    =		(
               """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
               """ </s> <pad> <pad> <pad>"""
           )
           _A							:  Optional[int]    =		rust_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           _A							:  List[Any]    =		py_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )

     def       a__			(						self       )		->							Any:
           _A							:  str    =		self._large_tokenizer
           # <mask_1> masks whole sentence while <mask_2> masks single word
           _A							:  Optional[int]    =		"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
           _A							:  Union[str, Any]    =		[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
           _A							:  Union[str, Any]    =		tokenizer([raw_input_str]	,  return_tensors=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )

     def       a__			(						self       )		->							List[str]:
           _A							:  Optional[int]    =		self._large_tokenizer
           # The tracebacks for the following asserts are **better** without messages or self.assertEqual
           assert tokenizer.vocab_size == 9_6103
           assert tokenizer.pad_token_id == 0
           assert tokenizer.eos_token_id == 1
           assert tokenizer.offset == 103
           assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
           assert tokenizer.unk_token == "<unk>"
           assert tokenizer.model_max_length == 1024
           _A							:  Any    =		"""To ensure a smooth flow of bank resolutions."""
           _A							:  Optional[int]    =		[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
           _A							:  Optional[Any]    =		tokenizer([raw_input_str]	,  return_tensors=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )
           assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

     @require_torch
     def       a__			(						self       )		->							List[str]:
           _A							:  Union[str, Any]    =		["""This is going to be way too long.""" * 150, """short example"""]
           _A							:  Optional[Any]    =		["""not super long but more than 5 tokens""", """tiny"""]
           _A							:  Union[str, Any]    =		self._large_tokenizer(_a	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )
           _A							:  Tuple    =		self._large_tokenizer(
               text_target=_a	,  max_length=5	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )

           assert batch.input_ids.shape == (2, 1024)
           assert batch.attention_mask.shape == (2, 1024)
           assert targets["input_ids"].shape == (2, 5)
           assert len(_a       ) == 2  # input_ids, attention_mask.



     @slow
     def       a__			(						self       )		->							Optional[Any]:
           # fmt: off
           _A							:  List[Any]    =		{"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=_a	,  model_name="""google/bigbird-pegasus-large-arxiv"""	,  revision="""ba85d0851d708441f91440d509690f1ab6353415"""	,  )







@require_sentencepiece
@require_tokenizers
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		PegasusTokenizer
     _a											=		PegasusTokenizerFast
     _a											=		True
     _a											=		True

     def       a__			(						self       )		->							Optional[Any]:
           super().setUp()

           # We have a SentencePiece fixture for testing
           _A							:  Tuple    =		PegasusTokenizer(_a	,  offset=0	,  mask_token_sent=_a	,  mask_token="""[MASK]"""       )
           tokenizer.save_pretrained(self.tmpdirname       )

     @cached_property
     def       a__			(						self       )		->							Optional[Any]:
           return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""       )

     def       a__			(						self	,  **_a       )		->							PegasusTokenizer:
           return PegasusTokenizer.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  _a       )		->							List[str]:
           return ("This is a test", "This is a test")

     def       a__			(						self       )		->							List[Any]:
           _A							:  List[Any]    =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  Dict    =		self.tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  Dict    =		(
               """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
               """ <pad> <pad> <pad>"""
           )
           _A							:  Optional[Any]    =		rust_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           _A							:  int    =		py_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )

     @require_torch
     def       a__			(						self       )		->							Optional[int]:
           _A							:  Tuple    =		["""This is going to be way too long.""" * 1000, """short example"""]
           _A							:  Optional[Any]    =		["""not super long but more than 5 tokens""", """tiny"""]
           _A							:  Tuple    =		self._large_tokenizer(_a	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )
           _A							:  str    =		self._large_tokenizer(
               text_target=_a	,  max_length=5	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )

           assert batch.input_ids.shape == (2, 4096)
           assert batch.attention_mask.shape == (2, 4096)
           assert targets["input_ids"].shape == (2, 5)
           assert len(_a       ) == 2  # input_ids, attention_mask.



     def       a__			(						self       )		->							Dict:
           _A							:  Optional[int]    =		(
               """This is an example string that is used to test the original TF implementation against the HF"""
               """ implementation"""
           )

           _A							:  Any    =		self._large_tokenizer(_a       ).input_ids

           self.assertListEqual(
               _a	,  [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]	,  )

