






"""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 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     Optional[int],       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     int=1_00,       _lowerCamelCase :     List[Any]=13,       _lowerCamelCase :     Tuple=30,       _lowerCamelCase :     Any=2,       _lowerCamelCase :     List[Any]=3,       _lowerCamelCase :     Optional[int]=True,       _lowerCamelCase :     List[Any]=True,       _lowerCamelCase :     Any=32,       _lowerCamelCase :     List[str]=5,       _lowerCamelCase :     Optional[Any]=4,       _lowerCamelCase :     Tuple=37,       _lowerCamelCase :     Dict="gelu",       _lowerCamelCase :     int=0.1,       _lowerCamelCase :     str=0.1,       _lowerCamelCase :     Union[str, Any]=10,       _lowerCamelCase :     Any=0.02,       _lowerCamelCase :     Any=3,       ):

     '''simple docstring'''
     __A									= parent
     __A									= vocab_size
     __A									= batch_size
     __A									= image_size
     __A									= patch_size
     __A									= num_channels
     __A									= is_training
     __A									= use_labels
     __A									= hidden_size
     __A									= num_hidden_layers
     __A									= num_attention_heads
     __A									= intermediate_size
     __A									= hidden_act
     __A									= hidden_dropout_prob
     __A									= attention_probs_dropout_prob
     __A									= type_sequence_label_size
     __A									= initializer_range

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


   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     __A									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     int,       _lowerCamelCase :     str,       _lowerCamelCase :     List[str]  ):

     '''simple docstring'''
     __A									= FlaxBeitModel(config=__UpperCAmelCase  )
     __A									= model(__UpperCAmelCase  )
     self.parent.assertEqual(result.last_hidden_state.shape,       (self.batch_size, self.seq_length, self.hidden_size)  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     Dict,       _lowerCamelCase :     List[Any],       _lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= FlaxBeitForMaskedImageModeling(config=__UpperCAmelCase  )
     __A									= model(__UpperCAmelCase  )
     self.parent.assertEqual(result.logits.shape,       (self.batch_size, self.seq_length - 1, self.vocab_size)  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     Tuple,       _lowerCamelCase :     str,       _lowerCamelCase :     str  ):

     '''simple docstring'''
     __A									= self.type_sequence_label_size
     __A									= FlaxBeitForImageClassification(config=__UpperCAmelCase  )
     __A									= model(__UpperCAmelCase  )
     self.parent.assertEqual(result.logits.shape,       (self.batch_size, self.type_sequence_label_size)  )

     # test greyscale images
     __A									= 1
     __A									= FlaxBeitForImageClassification(__UpperCAmelCase  )

     __A									= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
     __A									= model(__UpperCAmelCase  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= self.prepare_config_and_inputs()
     (
         (
         __A     
     )			,    (
         __A     
     )			,    (
         __A     
     )			,         
     )									= config_and_inputs
     __A									= {'''pixel_values''': pixel_values}
     return config, inputs_dict





@require_flax
class 					snake_case		(							__A   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	List[Any]						 = (
       (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
   )


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

     '''simple docstring'''
     __A									= FlaxBeitModelTester(self  )
     __A									= ConfigTester(self,       config_class=__UpperCAmelCase,       has_text_modality=__UpperCAmelCase,       hidden_size=37  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     self.config_tester.run_common_tests()


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A			,    __A									= self.model_tester.prepare_config_and_inputs_for_common()

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

       __A									= ['''pixel_values''']
       self.assertListEqual(arg_names[:1],       __UpperCAmelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A			,    __A									= self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__  ):
         __A									= self._prepare_for_class(__UpperCAmelCase,       __UpperCAmelCase  )
         __A									= model_class(__UpperCAmelCase  )

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

         with self.subTest('''JIT Enabled'''  ):
           __A									= model_jitted(**__UpperCAmelCase  ).to_tuple()

         with self.subTest('''JIT Disabled'''  ):
           with jax.disable_jit():
             __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__UpperCAmelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase  )






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     for model_class_name in self.all_model_classes:
       __A									= model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''  )
       __A									= model(np.ones((1, 3, 2_24, 2_24)  )  )
       self.assertIsNotNone(__UpperCAmelCase  )





def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
  return image





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


   '''simple docstring'''

   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''  ) if is_vision_available() else None


   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''  )

     __A									= self.default_image_processor
     __A									= prepare_img()
     __A									= image_processor(images=__UpperCAmelCase,       return_tensors='''np'''  ).pixel_values

     # prepare bool_masked_pos
     __A									= np.ones((1, 1_96),       dtype=__UpperCAmelCase  )

     # forward pass
     __A									= model(pixel_values=__UpperCAmelCase,       bool_masked_pos=__UpperCAmelCase  )
     __A									= outputs.logits

     # verify the logits
     __A									= (1, 1_96, 81_92)
     self.assertEqual(logits.shape,       __UpperCAmelCase  )

     __A									= 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],       __UpperCAmelCase,       atol=1e-2  )  )


   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''  )

     __A									= self.default_image_processor
     __A									= prepare_img()
     __A									= image_processor(images=__UpperCAmelCase,       return_tensors='''np'''  )

     # forward pass
     __A									= model(**__UpperCAmelCase  )
     __A									= outputs.logits

     # verify the logits
     __A									= (1, 10_00)
     self.assertEqual(logits.shape,       __UpperCAmelCase  )

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

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

     __A									= 2_81
     self.assertEqual(logits.argmax(-1  ).item(),       __UpperCAmelCase  )






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''  )

     __A									= self.default_image_processor
     __A									= prepare_img()
     __A									= image_processor(images=__UpperCAmelCase,       return_tensors='''np'''  )

     # forward pass
     __A									= model(**__UpperCAmelCase  )
     __A									= outputs.logits

     # verify the logits
     __A									= (1, 2_18_41)
     self.assertEqual(logits.shape,       __UpperCAmelCase  )

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

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

     __A									= 23_96
     self.assertEqual(logits.argmax(-1  ).item(),       __UpperCAmelCase  )





"""simple docstring"""




from typing import TYPE_CHECKING

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


__lowerCamelCase							 =     {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ["ChineseCLIPFeatureExtractor"]
				__lowerCamelCase							 =     ["ChineseCLIPImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ChineseCLIPModel",
				    "ChineseCLIPPreTrainedModel",
				    "ChineseCLIPTextModel",
				    "ChineseCLIPVisionModel",
				]

if TYPE_CHECKING:
				from .configuration_chinese_clip import (
				    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ChineseCLIPConfig,
				    ChineseCLIPOnnxConfig,
				    ChineseCLIPTextConfig,
				    ChineseCLIPVisionConfig,
				)
				from .processing_chinese_clip import ChineseCLIPProcessor

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_chinese_clip import (
								    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ChineseCLIPModel,
								    ChineseCLIPPreTrainedModel,
								    ChineseCLIPTextModel,
								    ChineseCLIPVisionModel,
								)

else:
				import sys

				__lowerCamelCase							 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





