




import inspect
import unittest

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

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


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor







class       UpperCAmelCase(     unittest.TestCase     ):



	"""simple docstring"""






	def __init__(     self       ,      lowerCamelCase       ,      lowerCamelCase=3       ,      lowerCamelCase=32       ,      lowerCamelCase=3       ,      lowerCamelCase=10       ,      lowerCamelCase=[10, 20, 30, 40]       ,      lowerCamelCase=[1, 1, 2, 1]       ,      lowerCamelCase=True       ,      lowerCamelCase=True       ,      lowerCamelCase="relu"       ,      lowerCamelCase=3       ,      lowerCamelCase=None       ,      )					->			int:





								"""simple docstring"""


								lowercase__  :       Optional[int]   					=							parent
								lowercase__  :       List[str]   					=							batch_size
								lowercase__  :       Tuple   					=							image_size
								lowercase__  :       List[Any]   					=							num_channels
								lowercase__  :       List[Any]   					=							embeddings_size
								lowercase__  :       List[Any]   					=							hidden_sizes
								lowercase__  :       Any   					=							depths
								lowercase__  :       Any   					=							is_training
								lowercase__  :       Dict   					=							use_labels
								lowercase__  :       str   					=							hidden_act
								lowercase__  :       List[Any]   					=							num_labels
								lowercase__  :       int   					=							scope
								lowercase__  :       List[Any]   					=							len(snake_case__				)

	def     __a	(     self				)					->			List[Any]:





								"""simple docstring"""


								lowercase__  :       int   					=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

								lowercase__  :       int   					=							self.get_config()

								return config, pixel_values

	def     __a	(     self				)					->			Dict:





								"""simple docstring"""


								return ResNetConfig(
								    num_channels=self.num_channels       ,      embeddings_size=self.embeddings_size       ,      hidden_sizes=self.hidden_sizes       ,      depths=self.depths       ,      hidden_act=self.hidden_act       ,      num_labels=self.num_labels       ,      image_size=self.image_size       ,      )

	def     __a	(     self       ,      lowerCamelCase       ,      lowerCamelCase				)					->			Tuple:





								"""simple docstring"""


								lowercase__  :       int   					=							FlaxResNetModel(config=snake_case__				)
								lowercase__  :       List[str]   					=							model(snake_case__				)

								# Output shape (b, c, h, w)
								self.parent.assertEqual(
								    result.last_hidden_state.shape       ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,      )

	def     __a	(     self       ,      lowerCamelCase       ,      lowerCamelCase				)					->			Any:





								"""simple docstring"""


								lowercase__  :       List[str]   					=							self.num_labels
								lowercase__  :       Any   					=							FlaxResNetForImageClassification(config=snake_case__				)
								lowercase__  :       Tuple   					=							model(snake_case__				)
								self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)				)







	def     __a	(     self				)					->			Optional[int]:





								"""simple docstring"""


								lowercase__  :       Any   					=							self.prepare_config_and_inputs()
								lowercase__					,   lowercase__  :       Any   					=							config_and_inputs
								lowercase__  :       Union[str, Any]   					=							{"pixel_values": pixel_values}
								return config, inputs_dict







@require_flax
class       UpperCAmelCase(     a_    ,	unittest.TestCase     ):



	"""simple docstring"""






	a	:					Tuple         =     (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

	a	:					List[str]         =     False
	a	:					Optional[Any]         =     False
	a	:					List[str]         =     False

	def     __a	(     self				)					->			None:





								"""simple docstring"""


								lowercase__  :       Optional[Any]   					=							FlaxResNetModelTester(self				)
								lowercase__  :       Optional[Any]   					=							ConfigTester(self       ,      config_class=snake_case__       ,      has_text_modality=snake_case__				)

	def     __a	(     self				)					->			Any:





								"""simple docstring"""


								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()

	def     __a	(     self				)					->			int:





								"""simple docstring"""


								return

	def     __a	(     self				)					->			Any:





								"""simple docstring"""


								lowercase__  :       List[str]   					=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case__				)

	def     __a	(     self				)					->			str:





								"""simple docstring"""


								lowercase__  :       Dict   					=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*snake_case__				)

	@unittest.skip(reason="ResNet does not use inputs_embeds"				)
	def     __a	(     self				)					->			int:





								"""simple docstring"""


								pass

	@unittest.skip(reason="ResNet does not support input and output embeddings"				)
	def     __a	(     self				)					->			List[Any]:





								"""simple docstring"""


								pass

	def     __a	(     self				)					->			List[str]:





								"""simple docstring"""


								lowercase__					,   lowercase__  :       Dict   					=							self.model_tester.prepare_config_and_inputs_for_common()

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

															lowercase__  :       int   					=							["pixel_values"]
															self.assertListEqual(arg_names[:1]       ,      snake_case__				)

	def     __a	(     self				)					->			List[Any]:





								"""simple docstring"""


								def check_hidden_states_output(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase				):
															lowercase__  :       int   					=							model_class(snake_case__				)

															lowercase__  :       Union[str, Any]   					=							model(**self._prepare_for_class(snake_case__       ,      snake_case__				)				)

															lowercase__  :       int   					=							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															lowercase__  :       Optional[Any]   					=							self.model_tester.num_stages
															self.assertEqual(len(snake_case__				)       ,      expected_num_stages + 1				)

	@unittest.skip(reason="ResNet does not use feedforward chunking"				)
	def     __a	(     self				)					->			Optional[Any]:





								"""simple docstring"""


								pass







	def     __a	(     self				)					->			int:





								"""simple docstring"""


								lowercase__					,   lowercase__  :       Tuple   					=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__				):
																						lowercase__  :       List[str]   					=							self._prepare_for_class(snake_case__       ,      snake_case__				)
																						lowercase__  :       List[Any]   					=							model_class(snake_case__				)

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

																						with self.subTest("JIT Enabled"				):
																													lowercase__  :       Any   					=							model_jitted(**snake_case__				).to_tuple()

																						with self.subTest("JIT Disabled"				):
																													with jax.disable_jit():
																																				lowercase__  :       Optional[int]   					=							model_jitted(**snake_case__				).to_tuple()

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

def 					snake_case_     (		)			-> Optional[int]:
							lowercase__  :       int   					=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
							return image







@require_flax
class       UpperCAmelCase(     unittest.TestCase     ):



	"""simple docstring"""






	@cached_property
	def     __a	(     self				)					->			Any:





								"""simple docstring"""


								return AutoImageProcessor.from_pretrained("microsoft/resnet-50"				) if is_vision_available() else None







	@slow
	def     __a	(     self				)					->			Tuple:





								"""simple docstring"""


								lowercase__  :       List[Any]   					=							FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50"				)

								lowercase__  :       str   					=							self.default_image_processor
								lowercase__  :       Dict   					=							prepare_img()
								lowercase__  :       List[str]   					=							image_processor(images=snake_case__       ,      return_tensors="np"				)

								lowercase__  :       List[str]   					=							model(**snake_case__				)

								# verify the logits
								lowercase__  :       List[str]   					=							(1, 1000)
								self.assertEqual(outputs.logits.shape       ,      snake_case__				)

								lowercase__  :       Any   					=							jnp.array([-11.10_69, -9.78_77, -8.37_77]				)

								self.assertTrue(jnp.allclose(outputs.logits[0, :3]       ,      snake_case__       ,      atol=1E-4				)				)







"""simple docstring"""







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


lowerCAmelCase_		:			str				   =					logging.get_logger(__name__)

lowerCAmelCase_		:			Optional[int]				   =					{
    '''google/reformer-crime-and-punishment''': (
        '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'''
    ),
    '''google/reformer-enwik8''': '''https://huggingface.co/google/reformer-enwik8/resolve/main/config.json''',
}





class      UpperCamelCase_       (				a_     ):
  _A				:    int =		'reformer'
  _A				:    Any =		['past_buckets_states']
  _A				:    Optional[Any] =		{}



  def __init__(					self       ,						snake_case__=64       ,						snake_case__=["local", "lsh", "local", "lsh", "local", "lsh"]       ,						snake_case__=1.0       ,						snake_case__=True       ,						snake_case__=[64, 64]       ,						snake_case__=[64, 1_92]       ,						snake_case__=0       ,						snake_case__=2       ,						snake_case__=5_12       ,						snake_case__=None       ,						snake_case__="relu"       ,						snake_case__=0.05       ,						snake_case__=2_56       ,						snake_case__=0.02       ,						snake_case__=False       ,						snake_case__=1e-12       ,						snake_case__=1       ,						snake_case__=0       ,						snake_case__=0.05       ,						snake_case__=64       ,						snake_case__=64       ,						snake_case__=0.0       ,						snake_case__=1       ,						snake_case__=0       ,						snake_case__=40_96       ,						snake_case__=12       ,						snake_case__=None       ,						snake_case__=1       ,						snake_case__=0       ,						snake_case__=3_20       ,						snake_case__=False       ,						snake_case__=True       ,						snake_case__=None       ,						**snake_case__       ,						)	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	hash_seed
         UpperCAmelCase					=	vocab_size
         UpperCAmelCase					=	attention_head_size
         UpperCAmelCase					=	hidden_size
         UpperCAmelCase					=	num_attention_heads
         UpperCAmelCase					=	num_hashes
         UpperCAmelCase					=	len(snake_case__ )
         UpperCAmelCase					=	tuple(snake_case__ ) if isinstance(snake_case__       ,						snake_case__ ) else num_buckets
         UpperCAmelCase					=	lsh_attn_chunk_length
         UpperCAmelCase					=	local_attn_chunk_length
         UpperCAmelCase					=	lsh_num_chunks_after
         UpperCAmelCase					=	lsh_num_chunks_before
         UpperCAmelCase					=	local_num_chunks_after
         UpperCAmelCase					=	local_num_chunks_before
         UpperCAmelCase					=	hidden_act
         UpperCAmelCase					=	feed_forward_size
         UpperCAmelCase					=	hidden_dropout_prob
         UpperCAmelCase					=	lsh_attention_probs_dropout_prob
         UpperCAmelCase					=	local_attention_probs_dropout_prob
         UpperCAmelCase					=	max_position_embeddings
         UpperCAmelCase					=	initializer_range
         UpperCAmelCase					=	layer_norm_eps
         UpperCAmelCase					=	axial_pos_embds
         UpperCAmelCase					=	tuple(snake_case__ )
         UpperCAmelCase					=	tuple(snake_case__ )
         UpperCAmelCase					=	axial_norm_std
         UpperCAmelCase					=	chunk_size_lm_head
         UpperCAmelCase					=	attn_layers
         UpperCAmelCase					=	use_cache
         UpperCAmelCase					=	classifier_dropout
         super().__init__(
             pad_token_id=snake_case__       ,						eos_token_id=snake_case__       ,						is_decoder=snake_case__       ,						tie_word_embeddings=snake_case__       ,						**snake_case__       ,						)
