"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowercase		:		Optional[Any]														=							{
    "configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
    "tokenization_mvp": ["MvpTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Optional[Any]														=							["MvpTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Optional[Any]														=							[
           "MVP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MvpForCausalLM",
           "MvpForConditionalGeneration",
           "MvpForQuestionAnswering",
           "MvpForSequenceClassification",
           "MvpModel",
           "MvpPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

       __lowercase		:		List[Any]														=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
"""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      _A	(						unittest.TestCase					):




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]	,      A_							:     List[str]	,      A_							:     List[Any]=100	,      A_							:     Any=13	,      A_							:     Dict=30	,      A_							:     Optional[int]=2	,      A_							:     str=3	,      A_							:     Tuple=True	,      A_							:     str=True	,      A_							:     Union[str, Any]=32	,      A_							:     int=5	,      A_							:     List[Any]=4	,      A_							:     Optional[Any]=37	,      A_							:     Any="gelu"	,      A_							:     List[str]=0.1	,      A_							:     int=0.1	,      A_							:     Tuple=10	,      A_							:     int=0.02	,      A_							:     Tuple=3	,      )    ->       Optional[int]:
         __snake_case				=							parent
         __snake_case				=							vocab_size
         __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

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __snake_case				=							(image_size // patch_size) ** 2
         __snake_case				=							num_patches + 1
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[str]:
         __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				=							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=A_	,      initializer_range=self.initializer_range	,      )

         return config, pixel_values, labels
    def  lowercase					(		self							:     Optional[int]	,      A_							:     str	,      A_							:     str	,      A_							:     List[str]    )    ->       List[Any]:
         __snake_case				=							FlaxBeitModel(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)    )
    def  lowercase					(		self							:     List[str]	,      A_							:     Any	,      A_							:     Any	,      A_							:     str    )    ->       str:
         __snake_case				=							FlaxBeitForMaskedImageModeling(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length - 1, self.vocab_size)    )
    def  lowercase					(		self							:     str	,      A_							:     List[Any]	,      A_							:     Optional[int]	,      A_							:     List[str]    )    ->       str:
         __snake_case				=							self.type_sequence_label_size
         __snake_case				=							FlaxBeitForImageClassification(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)    )

         # test greyscale images
         __snake_case				=							1
         __snake_case				=							FlaxBeitForImageClassification(A_    )

         __snake_case				=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
         __snake_case				=							model(A_    )







    def  lowercase					(		self							:     Dict    )    ->       List[str]:
         __snake_case				=							self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )		,	(
             __snake_case
         )		,	(
             __snake_case
         )		,	
         )				=							config_and_inputs
         __snake_case				=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					(
        (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
    )
    def  lowercase					(		self							:     List[Any]    )    ->       None:
         __snake_case				=							FlaxBeitModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=A_	,      has_text_modality=A_	,      hidden_size=37    )
    def  lowercase					(		self							:     Tuple    )    ->       Union[str, Any]:
         self.config_tester.run_common_tests()
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __snake_case				=							model_class(A_    )
              __snake_case				=							inspect.signature(model.__call__    )
              # 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]	,      A_    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       int:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__    ):
                   __snake_case				=							self._prepare_for_class(A_	,      A_    )
                   __snake_case				=							model_class(A_    )

                   @jax.jit
                   def model_jitted(A_							:     Union[str, Any]	,      **A_							:     Union[str, Any]    ):
                        return model(pixel_values=A_	,      **A_    )

                   with self.subTest('''JIT Enabled'''    ):
                        __snake_case				=							model_jitted(**A_    ).to_tuple()

                   with self.subTest('''JIT Disabled'''    ):
                        with jax.disable_jit():
                             __snake_case				=							model_jitted(**A_    ).to_tuple()

                   self.assertEqual(len(A_    )	,      len(A_    )    )
                   for jitted_output, output in zip(A_	,      A_    ):
                        self.assertEqual(jitted_output.shape	,      output.shape    )
    def  lowercase					(		self							:     str    )    ->       List[Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       Union[str, Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A_    )
    def  lowercase					(		self							:     str    )    ->       int:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A_    )







    @slow
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         for model_class_name in self.all_model_classes:
              __snake_case				=							model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''    )
              __snake_case				=							model(np.ones((1, 3, 224, 224)    )    )
              self.assertIsNotNone(A_    )






def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
     return image




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




    """simple docstring"""




    @cached_property
    def  lowercase					(		self							:     str    )    ->       str:
         return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''    ) if is_vision_available() else None
    @slow
    def  lowercase					(		self							:     Union[str, Any]    )    ->       List[str]:
         __snake_case				=							FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=A_	,      return_tensors='''np'''    ).pixel_values

         # prepare bool_masked_pos
         __snake_case				=							np.ones((1, 196)	,      dtype=A_    )

         # forward pass
         __snake_case				=							model(pixel_values=A_	,      bool_masked_pos=A_    )
         __snake_case				=							outputs.logits

         # verify the logits
         __snake_case				=							(1, 196, 8_192)
         self.assertEqual(logits.shape	,      A_    )

         __snake_case				=							np.array(
             [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]    )

         self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]	,      A_	,      atol=1E-2    )    )
    @slow
    def  lowercase					(		self							:     List[Any]    )    ->       List[str]:
         __snake_case				=							FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=A_	,      return_tensors='''np'''    )

         # forward pass
         __snake_case				=							model(**A_    )
         __snake_case				=							outputs.logits

         # verify the logits
         __snake_case				=							(1, 1_000)
         self.assertEqual(logits.shape	,      A_    )

         __snake_case				=							np.array([-1.23_85, -1.09_87, -1.01_08]    )

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

         __snake_case				=							281
         self.assertEqual(logits.argmax(-1    ).item()	,      A_    )







    @slow
    def  lowercase					(		self							:     int    )    ->       str:
         __snake_case				=							FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=A_	,      return_tensors='''np'''    )

         # forward pass
         __snake_case				=							model(**A_    )
         __snake_case				=							outputs.logits

         # verify the logits
         __snake_case				=							(1, 21_841)
         self.assertEqual(logits.shape	,      A_    )

         __snake_case				=							np.array([1.68_81, -0.27_87, 0.59_01]    )

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

         __snake_case				=							2_396
         self.assertEqual(logits.argmax(-1    ).item()	,      A_    )