





import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTMAEForPreTraining, ViTMAEModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class 							__lowercase			:






      def __init__(		self			,							lowercase_			,							lowercase_=1_3			,							lowercase_=3_0			,							lowercase_=2			,							lowercase_=3			,							lowercase_=True			,							lowercase_=True			,							lowercase_=3_2			,							lowercase_=5			,							lowercase_=4			,							lowercase_=3_7			,							lowercase_="gelu"			,							lowercase_=0.1			,							lowercase_=0.1			,							lowercase_=1_0			,							lowercase_=0.02			,							lowercase_=3			,							lowercase_=0.6			,							lowercase_=None			,							)     ->       str:
       __snake_case         =			parent
       __snake_case         =			batch_size
       __snake_case         =			image_size
       __snake_case         =			patch_size
       __snake_case         =			num_channels
       __snake_case         =			is_training
       __snake_case         =			use_labels
       __snake_case         =			hidden_size
       __snake_case         =			num_hidden_layers
       __snake_case         =			num_attention_heads
       __snake_case         =			intermediate_size
       __snake_case         =			hidden_act
       __snake_case         =			hidden_dropout_prob
       __snake_case         =			attention_probs_dropout_prob
       __snake_case         =			type_sequence_label_size
       __snake_case         =			initializer_range
       __snake_case         =			mask_ratio
       __snake_case         =			scope

       # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
       # (we add 1 for the [CLS] token)
       __snake_case         =			(image_size // patch_size) ** 2
       __snake_case         =			int(math.ceil((1 - mask_ratio) * (num_patches + 1)))






      def 			_a						(		self)     ->       Optional[Any]:
       __snake_case         =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

       __snake_case         =			None
       if self.use_labels:
        __snake_case         =			ids_tensor([self.batch_size]			,							self.type_sequence_label_size)

       __snake_case         =			self.get_config()

       return config, pixel_values, labels






      def 			_a						(		self)     ->       List[Any]:
       return ViTMAEConfig(
           image_size=self.image_size			,							patch_size=self.patch_size			,							num_channels=self.num_channels			,							hidden_size=self.hidden_size			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							intermediate_size=self.intermediate_size			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							is_decoder=lowercase__			,							initializer_range=self.initializer_range			,							mask_ratio=self.mask_ratio			,							)






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_)     ->       int:
       __snake_case         =			ViTMAEModel(config=lowercase__)
       model.to(lowercase__)
       model.eval()
       __snake_case         =			model(lowercase__)
       self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size))






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_)     ->       Union[str, Any]:
       __snake_case         =			ViTMAEForPreTraining(lowercase__)
       model.to(lowercase__)
       model.eval()
       __snake_case         =			model(lowercase__)
       __snake_case         =			(self.image_size // self.patch_size) ** 2
       __snake_case         =			self.patch_size**2 * self.num_channels
       self.parent.assertEqual(result.logits.shape			,							(self.batch_size, num_patches, expected_num_channels))

       # test greyscale images
       __snake_case         =			1
       __snake_case         =			ViTMAEForPreTraining(lowercase__)
       model.to(lowercase__)
       model.eval()
       __snake_case         =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
       __snake_case         =			model(lowercase__)
       __snake_case         =			self.patch_size**2
       self.parent.assertEqual(result.logits.shape			,							(self.batch_size, num_patches, expected_num_channels))







      def 			_a						(		self)     ->       str:
       __snake_case         =			self.prepare_config_and_inputs()
       __snake_case         =			config_and_inputs
       __snake_case         =			{"""pixel_values""": pixel_values}
       return config, inputs_dict






@require_torch
class 							__lowercase			(     snake_case_   ,							snake_case_   ,							unittest.TestCase       ):
      __UpperCAmelCase		      =						(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      __UpperCAmelCase		      =						{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

      __UpperCAmelCase		      =						False
      __UpperCAmelCase		      =						False
      __UpperCAmelCase		      =						False
      __UpperCAmelCase		      =						False






      def 			_a						(		self)     ->       Any:
       __snake_case         =			ViTMAEModelTester(self)
       __snake_case         =			ConfigTester(self			,							config_class=lowercase__			,							has_text_modality=lowercase__			,							hidden_size=3_7)






      def 			_a						(		self)     ->       Optional[int]:
       self.config_tester.run_common_tests()






      @unittest.skip(reason='ViTMAE does not use inputs_embeds')
      def 			_a						(		self)     ->       Optional[Any]:
       pass






      def 			_a						(		self)     ->       Optional[Any]:
       __snake_case         =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __snake_case         =			model_class(lowercase__)
        self.assertIsInstance(model.get_input_embeddings()			,							(nn.Module))
        __snake_case         =			model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowercase__			,							nn.Linear))






      def 			_a						(		self)     ->       List[str]:
       __snake_case         =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __snake_case         =			model_class(lowercase__)
        __snake_case         =			inspect.signature(model.forward)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __snake_case         =			[*signature.parameters.keys()]

        __snake_case         =			["""pixel_values"""]
        self.assertListEqual(arg_names[:1]			,							lowercase__)






      def 			_a						(		self)     ->       Dict:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase__)






      def 			_a						(		self)     ->       int:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*lowercase__)






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_)     ->       Tuple:
       np.random.seed(2)

       __snake_case         =			int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
       __snake_case         =			np.random.uniform(size=(self.model_tester.batch_size, num_patches))
       __snake_case         =			torch.from_numpy(lowercase__)

       # Add `noise` argument.
       # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
       __snake_case         =			pt_noise

       super().check_pt_tf_models(lowercase__			,							lowercase__			,							lowercase__)






      def 			_a						(		self)     ->       Tuple:
       __snake_case         =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __snake_case         =			model_class(lowercase__)
        model.to(lowercase__)
        model.eval()
        # make random mask reproducible
        torch.manual_seed(2)
        with torch.no_grad():
         __snake_case         =			model(**self._prepare_for_class(lowercase__			,							lowercase__))

        __snake_case         =			outputs[0].cpu().numpy()
        __snake_case         =			0

        with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(lowercase__)
         __snake_case         =			model_class.from_pretrained(lowercase__)
         model.to(lowercase__)
         # make random mask reproducible
         torch.manual_seed(2)
         with torch.no_grad():
          __snake_case         =			model(**self._prepare_for_class(lowercase__			,							lowercase__))

         # Make sure we don't have nans
         __snake_case         =			after_outputs[0].cpu().numpy()
         __snake_case         =			0
         __snake_case         =			np.amax(np.abs(out_a - out_a))
         self.assertLessEqual(lowercase__			,							1e-5)






      @unittest.skip(
          reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
      def 			_a						(		self)     ->       Union[str, Any]:
       pass






      @unittest.skip(
          reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
      def 			_a						(		self)     ->       List[Any]:
       pass






      @unittest.skip(
          reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
      def 			_a						(		self)     ->       List[Any]:
       pass






      @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load')
      def 			_a						(		self)     ->       List[str]:
       pass






      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
      def 			_a						(		self)     ->       Any:
       pass







      @slow
      def 			_a						(		self)     ->       str:

       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __snake_case         =			ViTMAEModel.from_pretrained(lowercase__)
        self.assertIsNotNone(lowercase__)






def  A							(			)					->				Tuple:
 '''simple docstring'''
 __snake_case         =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
 return image







@require_torch
@require_vision
class 							__lowercase			(     unittest.TestCase       ):






      @cached_property
      def 			_a						(		self)     ->       Optional[Any]:
       return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None







      @slow
      def 			_a						(		self)     ->       Optional[Any]:
       np.random.seed(2)

       __snake_case         =			ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(lowercase__)

       __snake_case         =			self.default_image_processor
       __snake_case         =			prepare_img()
       __snake_case         =			image_processor(images=lowercase__			,							return_tensors='pt').to(lowercase__)

       # prepare a noise vector that will be also used for testing the TF model
       # (this way we can ensure that the PT and TF models operate on the same inputs)
       __snake_case         =			ViTMAEConfig()
       __snake_case         =			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
       __snake_case         =			np.random.uniform(size=(1, num_patches))

       # forward pass
       with torch.no_grad():
        __snake_case         =			model(**lowercase__			,							noise=torch.from_numpy(lowercase__).to(device=lowercase__))

       # verify the logits
       __snake_case         =			torch.Size((1, 1_9_6, 7_6_8))
       self.assertEqual(outputs.logits.shape			,							lowercase__)

       __snake_case         =			torch.tensor(
           [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]])

       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,							expected_slice.to(lowercase__)			,							atol=1e-4))







'''simple docstring'''


import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowerCAmelCase_      :  int				     =  logging.get_logger(__name__)


lowerCAmelCase_      :  Optional[Any]				     =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_      :  List[str]				     =  {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_      :  Tuple				     =  {
    'allenai/led-base-16384': 1_6384,
}







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       str   							=					VOCAB_FILES_NAMES
      __magic_name__  :       str   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       List[Any]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       Optional[int]   							=					LEDTokenizer
      __magic_name__  :       Optional[int]   							=					['''input_ids''', '''attention_mask''']






      def __init__( self   :  str						,						lowercase__   :  int=None						,						lowercase__   :  Any=None						,						lowercase__   :  Optional[int]=None						,						lowercase__   :  List[Any]="replace"						,						lowercase__   :  Optional[Any]="<s>"						,						lowercase__   :  Optional[Any]="</s>"						,						lowercase__   :  Dict="</s>"						,						lowercase__   :  List[str]="<s>"						,						lowercase__   :  str="<unk>"						,						lowercase__   :  str="<pad>"						,						lowercase__   :  str="<mask>"						,						lowercase__   :  Union[str, Any]=False						,						lowercase__   :  Optional[int]=True						,						**lowercase__   :  str						,						):


           '''simple docstring'''

           super().__init__(
               lowercase__						,						lowercase__						,						tokenizer_file=lowercase__						,						errors=lowercase__						,						bos_token=lowercase__						,						eos_token=lowercase__						,						sep_token=lowercase__						,						cls_token=lowercase__						,						unk_token=lowercase__						,						pad_token=lowercase__						,						mask_token=lowercase__						,						add_prefix_space=lowercase__						,						trim_offsets=lowercase__						,						**lowercase__						,						)

           a_     :							str     						=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
           if pre_tok_state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                a_     :							Optional[int]     						=  getattr(lowercase__						,						pre_tok_state.pop("""type"""       )       )
                a_     :							List[str]     						=  add_prefix_space
                a_     :							Union[str, Any]     						=  pre_tok_class(**lowercase__       )

           a_     :							Union[str, Any]     						=  add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           a_     :							Any     						=  """post_processor"""
           a_     :							int     						=  getattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )
           if tokenizer_component_instance:
                a_     :							Optional[int]     						=  json.loads(tokenizer_component_instance.__getstate__()       )

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                     a_     :							int     						=  tuple(state["""sep"""]       )
                if "cls" in state:
                     a_     :							Optional[int]     						=  tuple(state["""cls"""]       )

                a_     :							List[Any]     						=  False

                if state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                     a_     :							Optional[Any]     						=  add_prefix_space
                     a_     :							Dict     						=  True

                if state.get("""trim_offsets"""						,						lowercase__       ) != trim_offsets:
                     a_     :							List[Any]     						=  trim_offsets
                     a_     :							Optional[Any]     						=  True

                if changes_to_apply:
                     a_     :							Optional[int]     						=  getattr(lowercase__						,						state.pop("""type"""       )       )
                     a_     :							Any     						=  component_class(**lowercase__       )
                     setattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )






      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           if self._mask_token is None:
                if self.verbose:
                     logger.error("""Using mask_token, but it is not set yet."""       )
                return None
           return str(self._mask_token       )






      @mask_token.setter
      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else value
           a_     :							Optional[int]     						=  value






      def 							lowercase_						( self   :  Optional[int]						,						*lowercase__   :  Any						,						**lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Any     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )

           if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                    """to use it with pretokenized inputs."""       )

           return super()._batch_encode_plus(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  List[Any]						,						*lowercase__   :  str						,						**lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Dict     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )

           if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                    """to use it with pretokenized inputs."""       )

           return super()._encode_plus(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  int						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self._tokenizer.model.save(lowercase__						,						name=lowercase__       )
           return tuple(lowercase__       )






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[str]						,						lowercase__   :  int=None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  [self.sep_token_id]
           a_     :							int     						=  [self.cls_token_id]

           if token_ids_a is None:
                return len(cls + token_ids_a + sep       ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  Union[Dict[str, EncodedInput], BatchEncoding]						,						lowercase__   :  Optional[int] = None						,						lowercase__   :  PaddingStrategy = PaddingStrategy.DO_NOT_PAD						,						lowercase__   :  Optional[int] = None						,						lowercase__   :  Optional[bool] = None						,						):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  super()._pad(
               encoded_inputs=lowercase__						,						max_length=lowercase__						,						padding_strategy=lowercase__						,						pad_to_multiple_of=lowercase__						,						return_attention_mask=lowercase__						,						)

           # Load from model defaults
           if return_attention_mask is None:
                a_     :							Tuple     						=  """attention_mask""" in self.model_input_names

           if return_attention_mask and "global_attention_mask" in encoded_inputs:
                a_     :							Tuple     						=  encoded_inputs[self.model_input_names[0]]
                # `global_attention_mask` need to have the same length as other (sequential) inputs.
                a_     :							Optional[int]     						=  len(encoded_inputs["""global_attention_mask"""]       ) != len(lowercase__       )

                if needs_to_be_padded:
                     a_     :							int     						=  len(lowercase__       ) - len(encoded_inputs["""global_attention_mask"""]       )

                     if self.padding_side == "right":
                          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                          a_     :							Optional[int]     						=  (
                              encoded_inputs["""global_attention_mask"""] + [-1] * difference
                          )
                     elif self.padding_side == "left":
                          a_     :							Dict     						=  [-1] * difference + encoded_inputs[
                              """global_attention_mask"""
                          ]
                     else:
                          raise ValueError("""Invalid padding strategy:""" + str(self.padding_side       )       )

           return encoded_inputs
