
"""simple docstring"""






import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
				import jax

				from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			def __init__(					self       ,   __A       ,   __A=13       ,   __A=30       ,   __A=2       ,   __A=3       ,   __A=True       ,   __A=True       ,   __A=32       ,   __A=5       ,   __A=4       ,   __A=37       ,   __A="gelu"       ,   __A=0.1       ,   __A=0.1       ,   __A=10       ,   __A=0.0_2       ,   )			->     Dict:
						lowerCAmelCase_    :List[str]			     =  parent
						lowerCAmelCase_    :Optional[int]			     =  batch_size
						lowerCAmelCase_    :Any			     =  image_size
						lowerCAmelCase_    :Any			     =  patch_size
						lowerCAmelCase_    :List[str]			     =  num_channels
						lowerCAmelCase_    :int			     =  is_training
						lowerCAmelCase_    :int			     =  use_labels
						lowerCAmelCase_    :Union[str, Any]			     =  hidden_size
						lowerCAmelCase_    :Any			     =  num_hidden_layers
						lowerCAmelCase_    :Any			     =  num_attention_heads
						lowerCAmelCase_    :Any			     =  intermediate_size
						lowerCAmelCase_    :List[str]			     =  hidden_act
						lowerCAmelCase_    :Dict			     =  hidden_dropout_prob
						lowerCAmelCase_    :List[str]			     =  attention_probs_dropout_prob
						lowerCAmelCase_    :Optional[int]			     =  type_sequence_label_size
						lowerCAmelCase_    :str			     =  initializer_range

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






			def  __lowerCAmelCase (					self       )			->     Union[str, Any]:
						lowerCAmelCase_    :Optional[Any]			     =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

						lowerCAmelCase_    :Optional[Any]			     =  ViTConfig(
						    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=lowerCamelCase__       ,   initializer_range=self.initializer_range       ,   )

						return config, pixel_values






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       )			->     Optional[int]:
						lowerCAmelCase_    :int			     =  FlaxViTModel(config=lowerCamelCase__       )
						lowerCAmelCase_    :List[Any]			     =  model(lowerCamelCase__       )
						# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
						lowerCAmelCase_    :List[Any]			     =  (self.image_size, self.image_size)
						lowerCAmelCase_    :List[Any]			     =  (self.patch_size, self.patch_size)
						lowerCAmelCase_    :str			     =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
						self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, num_patches + 1, self.hidden_size)       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       )			->     Dict:
						lowerCAmelCase_    :Optional[Any]			     =  self.type_sequence_label_size
						lowerCAmelCase_    :Any			     =  FlaxViTForImageClassification(config=lowerCamelCase__       )
						lowerCAmelCase_    :Optional[int]			     =  model(lowerCamelCase__       )
						self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.type_sequence_label_size)       )

						# test greyscale images
						lowerCAmelCase_    :Optional[int]			     =  1
						lowerCAmelCase_    :List[Any]			     =  FlaxViTForImageClassification(lowerCamelCase__       )

						lowerCAmelCase_    :Optional[Any]			     =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
						lowerCAmelCase_    :Optional[Any]			     =  model(lowerCamelCase__       )






			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :Dict			     =  self.prepare_config_and_inputs()
						(
						    lowerCAmelCase_							
						)    :Tuple			     =  config_and_inputs
						lowerCAmelCase_    :List[str]			     =  {"""pixel_values""": pixel_values}
						return config, inputs_dict




@require_flax
class 			_SCREAMING_SNAKE_CASE					(			SCREAMING_SNAKE_CASE__	, unittest.TestCase							):
			UpperCAmelCase_  :List[Any]				 =						(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :Union[str, Any]			     =  FlaxViTModelTester(self       )
						lowerCAmelCase_    :Optional[Any]			     =  ConfigTester(self       ,   config_class=lowerCamelCase__       ,   has_text_modality=lowerCamelCase__       ,   hidden_size=37       )






			def  __lowerCAmelCase (					self       )			->     Any:
						self.config_tester.run_common_tests()






			def  __lowerCAmelCase (					self       )			->     Union[str, Any]:
						lowerCAmelCase_    :Any			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase__       )






			def  __lowerCAmelCase (					self       )			->     Dict:
						lowerCAmelCase_    :str			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__       )






			def  __lowerCAmelCase (					self       )			->     Any:
						lowerCAmelCase_    :List[str]			     =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									lowerCAmelCase_    :Optional[int]			     =  model_class(lowerCamelCase__       )
									lowerCAmelCase_    :Union[str, Any]			     =  inspect.signature(model.__call__       )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									lowerCAmelCase_    :str			     =  [*signature.parameters.keys()]

									lowerCAmelCase_    :Optional[Any]			     =  ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]       ,   lowerCamelCase__       )






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :Dict			     =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__       ):
												lowerCAmelCase_    :Tuple			     =  self._prepare_for_class(lowerCamelCase__       ,   lowerCamelCase__       )
												lowerCAmelCase_    :List[str]			     =  model_class(lowerCamelCase__       )

												@jax.jit
												def model_jitted(__A       ,   **__A       ):
															return model(pixel_values=lowerCamelCase__       ,   **lowerCamelCase__       )

												with self.subTest("""JIT Enabled"""       ):
															lowerCAmelCase_    :List[Any]			     =  model_jitted(**lowerCamelCase__       ).to_tuple()

												with self.subTest("""JIT Disabled"""       ):
															with jax.disable_jit():
																		lowerCAmelCase_    :List[Any]			     =  model_jitted(**lowerCamelCase__       ).to_tuple()

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






			@slow
			def  __lowerCAmelCase (					self       )			->     List[Any]:
						for model_class_name in self.all_model_classes:
									lowerCAmelCase_    :Any			     =  model_class_name.from_pretrained("""google/vit-base-patch16-224"""       )
									lowerCAmelCase_    :int			     =  model(np.ones((1, 3, 224, 224)       )       )
									self.assertIsNotNone(lowerCamelCase__       )











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:Any             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      "xmod"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=False,		lowerCamelCase__=2,		lowerCamelCase__=False,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=("en_XX",),		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       int          =   vocab_size
        A :       int          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[str]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Dict          =   intermediate_size
        A :       Optional[int]          =   hidden_dropout_prob
        A :       Union[str, Any]          =   attention_probs_dropout_prob
        A :       int          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Union[str, Any]          =   position_embedding_type
        A :       Any          =   use_cache
        A :       int          =   classifier_dropout
        A :       int          =   pre_norm
        A :       List[str]          =   adapter_reduction_factor
        A :       Any          =   adapter_layer_norm
        A :       Any          =   adapter_reuse_layer_norm
        A :       str          =   ln_before_adapter
        A :       Dict          =   list(lowerCamelCase__		)
        A :       Union[str, Any]          =   default_language






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       Any          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       int          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


