






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 						UpperCAmelCase__ (							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__=0.02       ,):
						_A	:				Dict   =      parent
						_A	:				List[str]   =      batch_size
						_A	:				List[Any]   =      image_size
						_A	:				Tuple   =      patch_size
						_A	:				Optional[Any]   =      num_channels
						_A	:				Tuple   =      is_training
						_A	:				Optional[int]   =      use_labels
						_A	:				Any   =      hidden_size
						_A	:				Union[str, Any]   =      num_hidden_layers
						_A	:				Dict   =      num_attention_heads
						_A	:				Optional[int]   =      intermediate_size
						_A	:				List[str]   =      hidden_act
						_A	:				Optional[int]   =      hidden_dropout_prob
						_A	:				Union[str, Any]   =      attention_probs_dropout_prob
						_A	:				str   =      type_sequence_label_size
						_A	:				str   =      initializer_range

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







		def  A__      (					self		):
						_A	:				Optional[Any]   =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						_A	:				str   =      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=A__       ,initializer_range=self.initializer_range       ,)

						return config, pixel_values







		def  A__      (					self       ,A__       ,A__		):
						_A	:				Optional[int]   =      FlaxViTModel(config=A__		)
						_A	:				Any   =      model(A__		)
						# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
						_A	:				List[Any]   =      (self.image_size, self.image_size)
						_A	:				int   =      (self.patch_size, self.patch_size)
						_A	:				Any   =      (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  A__      (					self       ,A__       ,A__		):
						_A	:				List[Any]   =      self.type_sequence_label_size
						_A	:				Optional[int]   =      FlaxViTForImageClassification(config=A__		)
						_A	:				Tuple   =      model(A__		)
						self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.type_sequence_label_size)		)

						# test greyscale images
						_A	:				int   =      1
						_A	:				Tuple   =      FlaxViTForImageClassification(A__		)

						_A	:				Optional[int]   =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						_A	:				Any   =      model(A__		)







		def  A__      (					self		):
						_A	:				int   =      self.prepare_config_and_inputs()
						(
						    (
						    _A
						)  ,				(
						    _A
						)  ,				
						)	:				Any   =      config_and_inputs
						_A	:				Optional[int]   =      {'''pixel_values''': pixel_values}
						return config, inputs_dict






@require_flax
class 						UpperCAmelCase__ (							__snake_case     ,				unittest.TestCase							):
		__snake_case      :       Any     =   (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







		def  A__      (					self		):
						_A	:				Any   =      FlaxViTModelTester(self		)
						_A	:				Tuple   =      ConfigTester(self       ,config_class=A__       ,has_text_modality=A__       ,hidden_size=37		)







		def  A__      (					self		):
						self.config_tester.run_common_tests()







		def  A__      (					self		):
						_A	:				str   =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__		)







		def  A__      (					self		):
						_A	:				List[str]   =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__		)







		def  A__      (					self		):
						_A  ,				_A	:				Dict   =      self.model_tester.prepare_config_and_inputs_for_common()

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

										_A	:				Optional[Any]   =      ['''pixel_values''']
										self.assertListEqual(arg_names[:1]       ,A__		)







		def  A__      (					self		):
						_A  ,				_A	:				Union[str, Any]   =      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__		):
														_A	:				Optional[int]   =      self._prepare_for_class(A__       ,A__		)
														_A	:				int   =      model_class(A__		)

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

														with self.subTest('''JIT Enabled'''		):
																		_A	:				str   =      model_jitted(**A__		).to_tuple()

														with self.subTest('''JIT Disabled'''		):
																		with jax.disable_jit():
																						_A	:				List[str]   =      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		)







		@slow
		def  A__      (					self		):
						for model_class_name in self.all_model_classes:
										_A	:				Tuple   =      model_class_name.from_pretrained('''google/vit-base-patch16-224'''		)
										_A	:				Optional[int]   =      model(np.ones((1, 3, 224, 224)		)		)
										self.assertIsNotNone(A__		)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	Optional[int]								=logging.get_logger(__name__)

_UpperCamelCase			:	Tuple								={
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Any     =   "canine"







		def __init__(					self       ,A__=768       ,A__=12       ,A__=12       ,A__=3072       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=16384       ,A__=16       ,A__=0.02       ,A__=1E-12       ,A__=0       ,A__=0xE_000       ,A__=0xE_001       ,A__=4       ,A__=4       ,A__=8       ,A__=16384       ,A__=128       ,**A__       ,):
						super().__init__(pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__		)

						_A	:				int   =      max_position_embeddings
						_A	:				Dict   =      hidden_size
						_A	:				List[str]   =      num_hidden_layers
						_A	:				Optional[Any]   =      num_attention_heads
						_A	:				Optional[Any]   =      intermediate_size
						_A	:				Optional[Any]   =      hidden_act
						_A	:				str   =      hidden_dropout_prob
						_A	:				Optional[int]   =      attention_probs_dropout_prob
						_A	:				Union[str, Any]   =      initializer_range
						_A	:				Dict   =      type_vocab_size
						_A	:				Optional[Any]   =      layer_norm_eps

						# Character config:
						_A	:				Union[str, Any]   =      downsampling_rate
						_A	:				List[str]   =      upsampling_kernel_size
						_A	:				str   =      num_hash_functions
						_A	:				Optional[Any]   =      num_hash_buckets
						_A	:				Optional[Any]   =      local_transformer_stride



