

"""simple docstring"""




import inspect
import unittest

from transformers import RegNetConfig, 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.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor





class  lowerCamelCase    (unittest.TestCase      ):

			def __init__(       self					:    int     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    Optional[Any]=3     ,					__UpperCAmelCase					:    str=3_2     ,					__UpperCAmelCase					:    Any=3     ,					__UpperCAmelCase					:    Any=1_0     ,					__UpperCAmelCase					:    Dict=[1_0, 2_0, 3_0, 4_0]     ,					__UpperCAmelCase					:    str=[1, 1, 2, 1]     ,					__UpperCAmelCase					:    str=True     ,					__UpperCAmelCase					:    Optional[int]=True     ,					__UpperCAmelCase					:    Union[str, Any]="relu"     ,					__UpperCAmelCase					:    List[str]=3     ,					__UpperCAmelCase					:    List[Any]=None     ,					) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   image_size
						SCREAMING_SNAKE_CASE__										=   num_channels
						SCREAMING_SNAKE_CASE__										=   embeddings_size
						SCREAMING_SNAKE_CASE__										=   hidden_sizes
						SCREAMING_SNAKE_CASE__										=   depths
						SCREAMING_SNAKE_CASE__										=   is_training
						SCREAMING_SNAKE_CASE__										=   use_labels
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   num_labels
						SCREAMING_SNAKE_CASE__										=   scope
						SCREAMING_SNAKE_CASE__										=   len(__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     str:
						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						SCREAMING_SNAKE_CASE__										=   self.get_config()

						return config, pixel_values

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Tuple:
						return RegNetConfig(
						    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        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   FlaxRegNetModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)

						# Output shape (b, c, h, w)
						self.parent.assertEqual(
						    result.last_hidden_state.shape     ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    Any							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   self.num_labels
						SCREAMING_SNAKE_CASE__										=   FlaxRegNetForImageClassification(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   config_and_inputs
						SCREAMING_SNAKE_CASE__										=   {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_flax
class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		str       		=				(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

			lowerCamelCase__ :		Optional[int]       		=				False
			lowerCamelCase__ :		Optional[int]       		=				False
			lowerCamelCase__ :		Tuple       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     None:
						SCREAMING_SNAKE_CASE__										=   FlaxRegNetModelTester(self							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Any:
						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        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Dict:
						return

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase							)

			@unittest.skip(reason="""RegNet does not use inputs_embeds"""							)
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Tuple:
						pass

			@unittest.skip(reason="""RegNet does not support input and output embeddings"""							)
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     List[str]:
						pass

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE__										=   ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Union[str, Any]:
						def check_hidden_states_output(__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    Union[str, Any]							):
									SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)

									SCREAMING_SNAKE_CASE__										=   model(**self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase							)							)

									SCREAMING_SNAKE_CASE__										=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									SCREAMING_SNAKE_CASE__										=   self.model_tester.num_stages
									self.assertEqual(len(__UpperCAmelCase							)     ,					expected_num_stages + 1							)

						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   True
									check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE__										=   True

									check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     List[str]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_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__							):
												SCREAMING_SNAKE_CASE__										=   self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)

												@jax.jit
												def model_jitted(__UpperCAmelCase					:    List[str]     ,					**__UpperCAmelCase					:    Dict							):
															return model(pixel_values=__UpperCAmelCase     ,					**__UpperCAmelCase							)

												with self.subTest("""JIT Enabled"""							):
															SCREAMING_SNAKE_CASE__										=   model_jitted(**__UpperCAmelCase							).to_tuple()

												with self.subTest("""JIT Disabled"""							):
															with jax.disable_jit():
																		SCREAMING_SNAKE_CASE__										=   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 						A							(  ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
			return image





@require_flax
class  lowerCamelCase    (unittest.TestCase      ):

			@cached_property
			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Dict:
						return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""							) if is_vision_available() else None





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""							)

						SCREAMING_SNAKE_CASE__										=   self.default_image_processor
						SCREAMING_SNAKE_CASE__										=   prepare_img()
						SCREAMING_SNAKE_CASE__										=   image_processor(images=__UpperCAmelCase     ,					return_tensors="""np"""							)

						SCREAMING_SNAKE_CASE__										=   model(**__UpperCAmelCase							)

						# verify the logits
						SCREAMING_SNAKE_CASE__										=   (1, 1_0_0_0)
						self.assertEqual(outputs.logits.shape     ,					__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   jnp.array([-0.4_180, -1.5_051, -3.4_836]							)

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





"""simple docstring"""




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


A_				:      List[Any]           =			logging.get_logger(__name__)

A_				:      Optional[int]           =			{
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[int]       		=				'dpr'

			def __init__(       self					:    Optional[int]     ,					__UpperCAmelCase					:    List[str]=3_0_5_2_2     ,					__UpperCAmelCase					:    int=7_6_8     ,					__UpperCAmelCase					:    Optional[Any]=1_2     ,					__UpperCAmelCase					:    Dict=1_2     ,					__UpperCAmelCase					:    Any=3_0_7_2     ,					__UpperCAmelCase					:    int="gelu"     ,					__UpperCAmelCase					:    int=0.1     ,					__UpperCAmelCase					:    Optional[Any]=0.1     ,					__UpperCAmelCase					:    Any=5_1_2     ,					__UpperCAmelCase					:    Union[str, Any]=2     ,					__UpperCAmelCase					:    Any=0.02     ,					__UpperCAmelCase					:    Dict=1e-12     ,					__UpperCAmelCase					:    Optional[Any]=0     ,					__UpperCAmelCase					:    Union[str, Any]="absolute"     ,					__UpperCAmelCase					:    int = 0     ,					**__UpperCAmelCase					:    Dict     ,					) ->     Tuple:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   type_vocab_size
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   projection_dim
						SCREAMING_SNAKE_CASE__										=   position_embedding_type


