




import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
    )
    from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST







class __snake_case			:







   def __init__(      self		:						List[str]					,						_snake_case		:						Any					,						_snake_case		:						Tuple=13					,						_snake_case		:						Tuple=7					,						_snake_case		:						Optional[int]=True					,						_snake_case		:						Optional[int]=True					,						_snake_case		:						List[str]=True					,						_snake_case		:						Dict=True					,						_snake_case		:						Any=99					,						_snake_case		:						str=32					,						_snake_case		:						Optional[Any]=5					,						_snake_case		:						Any=4					,						_snake_case		:						Tuple=37					,						_snake_case		:						Optional[int]="gelu"					,						_snake_case		:						int=0.1					,						_snake_case		:						Optional[Any]=0.1					,						_snake_case		:						Any=128					,						_snake_case		:						List[str]=32					,						_snake_case		:						str=16					,						_snake_case		:						str=2					,						_snake_case		:						List[str]=0.0_2					,						_snake_case		:						str=3					,						_snake_case		:						Optional[Any]=4					,						_snake_case		:						Optional[int]=None					,						):

        """simple docstring"""


        UpperCAmelCase_          =							parent
        UpperCAmelCase_          =							batch_size
        UpperCAmelCase_          =							seq_length
        UpperCAmelCase_          =							is_training
        UpperCAmelCase_          =							use_input_mask
        UpperCAmelCase_          =							use_token_type_ids
        UpperCAmelCase_          =							use_labels
        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							hidden_size
        UpperCAmelCase_          =							num_hidden_layers
        UpperCAmelCase_          =							num_attention_heads
        UpperCAmelCase_          =							intermediate_size
        UpperCAmelCase_          =							hidden_act
        UpperCAmelCase_          =							hidden_dropout_prob
        UpperCAmelCase_          =							attention_probs_dropout_prob
        UpperCAmelCase_          =							max_position_embeddings
        UpperCAmelCase_          =							type_vocab_size
        UpperCAmelCase_          =							type_sequence_label_size
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							num_labels
        UpperCAmelCase_          =							num_choices
        UpperCAmelCase_          =							scope







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size)

        UpperCAmelCase_          =							None
        if self.use_input_mask:
             UpperCAmelCase_          =							random_attention_mask([self.batch_size, self.seq_length])

        UpperCAmelCase_          =							None
        if self.use_token_type_ids:
             UpperCAmelCase_          =							ids_tensor([self.batch_size, self.seq_length]					,						self.type_vocab_size)

        UpperCAmelCase_          =							None
        UpperCAmelCase_          =							None
        UpperCAmelCase_          =							None
        if self.use_labels:
             UpperCAmelCase_          =							ids_tensor([self.batch_size]					,						self.type_sequence_label_size)
             UpperCAmelCase_          =							ids_tensor([self.batch_size, self.seq_length]					,						self.num_labels)
             UpperCAmelCase_          =							ids_tensor([self.batch_size]					,						self.num_choices)

        UpperCAmelCase_          =							self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        return NezhaConfig(
            vocab_size=self.vocab_size					,						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					,						max_position_embeddings=self.max_position_embeddings					,						type_vocab_size=self.type_vocab_size					,						is_decoder=_snake_case					,						initializer_range=self.initializer_range					,						)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        (
            (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       
        )          =							self.prepare_config_and_inputs()

        UpperCAmelCase_          =							True
        UpperCAmelCase_          =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
        UpperCAmelCase_          =							ids_tensor([self.batch_size, self.seq_length]					,						vocab_size=2)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						int					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[str]					,						_snake_case		:						List[Any]					,						_snake_case		:						List[Any]					,						_snake_case		:						Optional[int]					,						_snake_case		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaModel(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case)
        UpperCAmelCase_          =							model(_snake_case					,						token_type_ids=_snake_case)
        UpperCAmelCase_          =							model(_snake_case)
        self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape					,						(self.batch_size, self.hidden_size))







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						str					,						_snake_case		:						Optional[int]					,						_snake_case		:						Any					,						_snake_case		:						Tuple					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Dict					,						_snake_case		:						List[str]					,						_snake_case		:						Optional[Any]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							True
        UpperCAmelCase_          =							NezhaModel(_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						encoder_hidden_states=_snake_case					,						encoder_attention_mask=_snake_case					,						)
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						encoder_hidden_states=_snake_case					,						)
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case)
        self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape					,						(self.batch_size, self.hidden_size))







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						str					,						_snake_case		:						Tuple					,						_snake_case		:						Optional[int]					,						_snake_case		:						str					,						_snake_case		:						int					,						_snake_case		:						Tuple					,						_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaForMaskedLM(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size))







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Any					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Any					,						_snake_case		:						Dict					,						_snake_case		:						int					,						_snake_case		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaForNextSentencePrediction(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case					,						)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, 2))







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Tuple					,						_snake_case		:						Dict					,						_snake_case		:						Tuple					,						_snake_case		:						Any					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Any					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaForPreTraining(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case					,						next_sentence_label=_snake_case					,						)
        self.parent.assertEqual(result.prediction_logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size))
        self.parent.assertEqual(result.seq_relationship_logits.shape					,						(self.batch_size, 2))







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						str					,						_snake_case		:						int					,						_snake_case		:						Tuple					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Tuple					,						_snake_case		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaForQuestionAnswering(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						start_positions=_snake_case					,						end_positions=_snake_case					,						)
        self.parent.assertEqual(result.start_logits.shape					,						(self.batch_size, self.seq_length))
        self.parent.assertEqual(result.end_logits.shape					,						(self.batch_size, self.seq_length))







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						List[Any]					,						_snake_case		:						Tuple					,						_snake_case		:						str					,						_snake_case		:						List[Any]					,						_snake_case		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.num_labels
        UpperCAmelCase_          =							NezhaForSequenceClassification(_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_labels))







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						Optional[int]					,						_snake_case		:						Tuple					,						_snake_case		:						Tuple					,						_snake_case		:						Optional[int]					,						_snake_case		:						Dict					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.num_labels
        UpperCAmelCase_          =							NezhaForTokenClassification(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels))







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						Optional[int]					,						_snake_case		:						int					,						_snake_case		:						Optional[Any]					,						_snake_case		:						int					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						str					,						_snake_case		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.num_choices
        UpperCAmelCase_          =							NezhaForMultipleChoice(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							input_ids.unsqueeze(1).expand(-1					,						self.num_choices					,						-1).contiguous()
        UpperCAmelCase_          =							token_type_ids.unsqueeze(1).expand(-1					,						self.num_choices					,						-1).contiguous()
        UpperCAmelCase_          =							input_mask.unsqueeze(1).expand(-1					,						self.num_choices					,						-1).contiguous()
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case					,						)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_choices))







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							self.prepare_config_and_inputs()
        (
            (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       
        )          =							config_and_inputs
        UpperCAmelCase_          =							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict







@require_torch
class __snake_case			(			a ,  a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    List[str]          =  (
       (
           NezhaModel,
           NezhaForMaskedLM,
           NezhaForMultipleChoice,
           NezhaForNextSentencePrediction,
           NezhaForPreTraining,
           NezhaForQuestionAnswering,
           NezhaForSequenceClassification,
           NezhaForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   UpperCAmelCase__  :    str          =  (
       {
           '''feature-extraction''': NezhaModel,
           '''fill-mask''': NezhaForMaskedLM,
           '''question-answering''': NezhaForQuestionAnswering,
           '''text-classification''': NezhaForSequenceClassification,
           '''token-classification''': NezhaForTokenClassification,
           '''zero-shot''': NezhaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   UpperCAmelCase__  :    Union[str, Any]          =  True







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Any					,						_snake_case		:						int					,						_snake_case		:						List[str]=False):

        """simple docstring"""


        UpperCAmelCase_          =							super()._prepare_for_class(_snake_case					,						_snake_case					,						return_labels=_snake_case)

        if return_labels:
             if model_class in get_values(_snake_case):
                  UpperCAmelCase_          =							torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.seq_length)					,						dtype=torch.long					,						device=_snake_case)
                  UpperCAmelCase_          =							torch.zeros(
                      self.model_tester.batch_size					,						dtype=torch.long					,						device=_snake_case)
        return inputs_dict







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaModelTester(self)
        UpperCAmelCase_          =							ConfigTester(self					,						config_class=_snake_case					,						hidden_size=37)







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        self.config_tester.run_common_tests()







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_snake_case)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        (
            (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       
        )          =							self.model_tester.prepare_config_and_inputs_for_decoder()

        UpperCAmelCase_          =							None

        self.model_tester.create_and_check_model_as_decoder(
            _snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_snake_case)







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*_snake_case)







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_next_sequence_prediction(*_snake_case)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*_snake_case)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_snake_case)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_snake_case)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_snake_case)







   @slow
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase_          =							NezhaModel.from_pretrained(_snake_case)
             self.assertIsNotNone(_snake_case)







   @slow
   @require_torch_gpu
   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
             # NezhaForMultipleChoice behaves incorrectly in JIT environments.
             if model_class == NezhaForMultipleChoice:
                  return

             UpperCAmelCase_          =							True
             UpperCAmelCase_          =							model_class(config=_snake_case)

             UpperCAmelCase_          =							self._prepare_for_class(_snake_case					,						_snake_case)
             UpperCAmelCase_          =							torch.jit.trace(
                 _snake_case					,						(inputs_dict['''input_ids'''].to('''cpu'''), inputs_dict['''attention_mask'''].to('''cpu''')))

             with tempfile.TemporaryDirectory() as tmp:
                  torch.jit.save(_snake_case					,						os.path.join(_snake_case					,						'''bert.pt'''))
                  UpperCAmelCase_          =							torch.jit.load(os.path.join(_snake_case					,						'''bert.pt''')					,						map_location=_snake_case)
                  loaded(inputs_dict['''input_ids'''].to(_snake_case)					,						inputs_dict['''attention_mask'''].to(_snake_case))







@require_torch
class __snake_case			(			unittest.TestCase		):







   @slow
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaModel.from_pretrained('''sijunhe/nezha-cn-base''')
        UpperCAmelCase_          =							torch.tensor([[0, 1, 2, 3, 4, 5]])
        UpperCAmelCase_          =							torch.tensor([[0, 1, 1, 1, 1, 1]])
        with torch.no_grad():
             UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case)[0]
        UpperCAmelCase_          =							torch.Size((1, 6, 768))
        self.assertEqual(output.shape					,						_snake_case)
        UpperCAmelCase_          =							torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]])

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,						_snake_case					,						atol=1e-4))







   @slow
   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base''')
        UpperCAmelCase_          =							torch.tensor([[0, 1, 2, 3, 4, 5]])
        UpperCAmelCase_          =							torch.tensor([[1, 1, 1, 1, 1, 1]])
        with torch.no_grad():
             UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case)[0]
        UpperCAmelCase_          =							torch.Size((1, 6, 21128))
        self.assertEqual(output.shape					,						_snake_case)
        UpperCAmelCase_          =							torch.tensor(
            [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]])

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,						_snake_case					,						atol=1e-4))









"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
   import jax

   from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor




class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def __init__(			self							:			str,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			int=1_0_0,					UpperCAmelCase__							:			Any=1_3,					UpperCAmelCase__							:			List[Any]=3_0,					UpperCAmelCase__							:			Dict=2,					UpperCAmelCase__							:			Any=3,					UpperCAmelCase__							:			Optional[Any]=True,					UpperCAmelCase__							:			List[str]=True,					UpperCAmelCase__							:			Optional[Any]=3_2,					UpperCAmelCase__							:			Any=5,					UpperCAmelCase__							:			Any=4,					UpperCAmelCase__							:			Any=3_7,					UpperCAmelCase__							:			Optional[int]="gelu",					UpperCAmelCase__							:			Dict=0.1,					UpperCAmelCase__							:			Optional[int]=0.1,					UpperCAmelCase__							:			Dict=1_0,					UpperCAmelCase__							:			Tuple=0.02,					UpperCAmelCase__							:			List[Any]=3,					):
    __lowercase								=						parent
    __lowercase								=						vocab_size
    __lowercase								=						batch_size
    __lowercase								=						image_size
    __lowercase								=						patch_size
    __lowercase								=						num_channels
    __lowercase								=						is_training
    __lowercase								=						use_labels
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						type_sequence_label_size
    __lowercase								=						initializer_range

    # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
    __lowercase								=						(image_size // patch_size) ** 2
    __lowercase								=						num_patches + 1


 def 					_lowercase (			self							:			int	):
    __lowercase								=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

    __lowercase								=						BeitConfig(
        vocab_size=self.vocab_size,					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=UpperCAmelCase__,					initializer_range=self.initializer_range,					)

    return config, pixel_values, labels


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						FlaxBeitModel(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						FlaxBeitForMaskedImageModeling(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length - 1, self.vocab_size)	)


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Union[str, Any]	):
    __lowercase								=						self.type_sequence_label_size
    __lowercase								=						FlaxBeitForImageClassification(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.type_sequence_label_size)	)

    # test greyscale images
    __lowercase								=						1
    __lowercase								=						FlaxBeitForImageClassification(UpperCAmelCase__	)

    __lowercase								=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
    __lowercase								=						model(UpperCAmelCase__	)



 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.prepare_config_and_inputs()
    (
        (
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,
    )								=						config_and_inputs
    __lowercase								=						{"pixel_values": pixel_values}
    return config, inputs_dict







@require_flax
class 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	(
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )


 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						FlaxBeitModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					has_text_modality=UpperCAmelCase__,					hidden_size=3_7	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowercase								=						["pixel_values"]
       self.assertListEqual(arg_names[:1],					UpperCAmelCase__	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__	):
          __lowercase								=						self._prepare_for_class(UpperCAmelCase__,					UpperCAmelCase__	)
          __lowercase								=						model_class(UpperCAmelCase__	)

          @jax.jit
          def model_jitted(UpperCAmelCase__							:			str,					**UpperCAmelCase__							:			Dict	):
             return model(pixel_values=UpperCAmelCase__,					**UpperCAmelCase__	)

          with self.subTest("JIT Enabled"	):
             __lowercase								=						model_jitted(**UpperCAmelCase__	).to_tuple()

          with self.subTest("JIT Disabled"	):
             with jax.disable_jit():
                __lowercase								=						model_jitted(**UpperCAmelCase__	).to_tuple()

          self.assertEqual(len(UpperCAmelCase__	),					len(UpperCAmelCase__	)	)
          for jitted_output, output in zip(UpperCAmelCase__,					UpperCAmelCase__	):
             self.assertEqual(jitted_output.shape,					output.shape	)


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			Union[str, Any]	):
    for model_class_name in self.all_model_classes:
       __lowercase								=						model_class_name.from_pretrained("microsoft/beit-base-patch16-224"	)
       __lowercase								=						model(np.ones((1, 3, 2_2_4, 2_2_4)	)	)
       self.assertIsNotNone(UpperCAmelCase__	)







def   _A  (  )	->						str:
   '''simple docstring'''


   __lowercase								=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
   return image




@require_vision
@require_flax
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @cached_property
 def 					_lowercase (			self							:			Optional[int]	):
    return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"	) if is_vision_available() else None


 @slow
 def 					_lowercase (			self							:			Any	):
    __lowercase								=						FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	).pixel_values

    # prepare bool_masked_pos
    __lowercase								=						np.ones((1, 1_9_6),					dtype=UpperCAmelCase__	)

    # forward pass
    __lowercase								=						model(pixel_values=UpperCAmelCase__,					bool_masked_pos=UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 1_9_6, 8_1_9_2)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						np.array(
        [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]	)

    self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3],					UpperCAmelCase__,					atol=1E-2	)	)


 @slow
 def 					_lowercase (			self							:			Any	):
    __lowercase								=						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	)

    # forward pass
    __lowercase								=						model(**UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 1_0_0_0)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						np.array([-1.2_385, -1.0_987, -1.0_108]	)

    self.assertTrue(np.allclose(logits[0, :3],					UpperCAmelCase__,					atol=1E-4	)	)

    __lowercase								=						2_8_1
    self.assertEqual(logits.argmax(-1	).item(),					UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	)

    # forward pass
    __lowercase								=						model(**UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 2_1_8_4_1)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						np.array([1.6_881, -0.2_787, 0.5_901]	)

    self.assertTrue(np.allclose(logits[0, :3],					UpperCAmelCase__,					atol=1E-4	)	)

    __lowercase								=						2_3_9_6
    self.assertEqual(logits.argmax(-1	).item(),					UpperCAmelCase__	)




