






import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							__UpperCamelCase					:		List[str]	  =						"""platform"""
							import jax
							import jax.numpy as jnp
							import numpy as np

							from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel


@require_flax
class   __SCREAMING_SNAKE_CASE:
							_UpperCAmelCase     =       PegasusConfig
							_UpperCAmelCase     =       {}
							_UpperCAmelCase     =       "gelu"
							def __init__(     self:      int       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Union[str, Any]=13       ,	UpperCamelCase:      int=7       ,	UpperCamelCase:      int=True       ,	UpperCamelCase:      Union[str, Any]=False       ,	UpperCamelCase:      str=99       ,	UpperCamelCase:      Tuple=32       ,	UpperCamelCase:      List[str]=5       ,	UpperCamelCase:      List[str]=4       ,	UpperCamelCase:      Optional[int]=37       ,	UpperCamelCase:      Optional[int]=0.1       ,	UpperCamelCase:      Optional[Any]=0.1       ,	UpperCamelCase:      str=20       ,	UpperCamelCase:      str=2       ,	UpperCamelCase:      Dict=1       ,	UpperCamelCase:      int=0       ,	)		->		Tuple:
											snake_case__									=     parent
											snake_case__									=     batch_size
											snake_case__									=     seq_length
											snake_case__									=     is_training
											snake_case__									=     use_labels
											snake_case__									=     vocab_size
											snake_case__									=     hidden_size
											snake_case__									=     num_hidden_layers
											snake_case__									=     num_attention_heads
											snake_case__									=     intermediate_size

											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     max_position_embeddings
											snake_case__									=     eos_token_id
											snake_case__									=     pad_token_id
											snake_case__									=     bos_token_id
							def lowerCAmelCase_ (     self:      Any						)		->		Dict:
											snake_case__									=     ids_tensor([self.batch_size, self.seq_length - 1]       ,	self.vocab_size						).clip(3       ,	self.vocab_size						)
											snake_case__									=     np.expand_dims(np.array([self.eos_token_id] * self.batch_size						)       ,	1						)
											snake_case__									=     np.concatenate([input_ids, eos_tensor]       ,	axis=1						)

											snake_case__									=     ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size						)

											snake_case__									=     self.config_cls(
											    vocab_size=self.vocab_size       ,	d_model=self.hidden_size       ,	encoder_layers=self.num_hidden_layers       ,	decoder_layers=self.num_hidden_layers       ,	encoder_attention_heads=self.num_attention_heads       ,	decoder_attention_heads=self.num_attention_heads       ,	encoder_ffn_dim=self.intermediate_size       ,	decoder_ffn_dim=self.intermediate_size       ,	dropout=self.hidden_dropout_prob       ,	attention_dropout=self.attention_probs_dropout_prob       ,	max_position_embeddings=self.max_position_embeddings       ,	eos_token_ids=[2]       ,	bos_token_id=self.bos_token_id       ,	pad_token_id=self.pad_token_id       ,	decoder_start_token_id=self.pad_token_id       ,	**self.config_updates       ,	)
											snake_case__									=     prepare_pegasus_inputs_dict(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)
											return config, inputs_dict
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Dict						)		->		Dict:
											snake_case__									=     20
											snake_case__									=     model_class_name(UpperCamelCase						)

											snake_case__									=     model.encode(inputs_dict['input_ids']						)

											snake_case__							,						snake_case__									=     (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											snake_case__									=     model.init_cache(decoder_input_ids.shape[0]       ,	UpperCamelCase       ,	UpperCamelCase						)
											snake_case__									=     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,	dtype='i4'						)

											snake_case__									=     jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]       ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,	)
											snake_case__									=     model.decode(
											    decoder_input_ids[:, :-1]       ,	UpperCamelCase       ,	decoder_attention_mask=UpperCamelCase       ,	past_key_values=UpperCamelCase       ,	decoder_position_ids=UpperCamelCase       ,	)

											snake_case__									=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,	dtype='i4'						)
											snake_case__									=     model.decode(
											    decoder_input_ids[:, -1:]       ,	UpperCamelCase       ,	decoder_attention_mask=UpperCamelCase       ,	past_key_values=outputs_cache.past_key_values       ,	decoder_position_ids=UpperCamelCase       ,	)

											snake_case__									=     model.decode(UpperCamelCase       ,	UpperCamelCase						)

											snake_case__									=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
											self.parent.assertTrue(diff < 1e-3       ,	msg=F'''Max diff is {diff}'''						)


							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Dict						)		->		List[Any]:
											snake_case__									=     20
											snake_case__									=     model_class_name(UpperCamelCase						)

											snake_case__									=     model.encode(inputs_dict['input_ids']						)

											snake_case__							,						snake_case__									=     (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											snake_case__									=     jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
											    ]       ,	axis=-1       ,	)

											snake_case__									=     model.init_cache(decoder_input_ids.shape[0]       ,	UpperCamelCase       ,	UpperCamelCase						)
											snake_case__									=     jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]       ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,	)

											snake_case__									=     model.decode(
											    decoder_input_ids[:, :-1]       ,	UpperCamelCase       ,	decoder_attention_mask=UpperCamelCase       ,	past_key_values=UpperCamelCase       ,	decoder_position_ids=UpperCamelCase       ,	)
											snake_case__									=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,	dtype='i4'						)
											snake_case__									=     model.decode(
											    decoder_input_ids[:, -1:]       ,	UpperCamelCase       ,	past_key_values=outputs_cache.past_key_values       ,	decoder_attention_mask=UpperCamelCase       ,	decoder_position_ids=UpperCamelCase       ,	)

											snake_case__									=     model.decode(UpperCamelCase       ,	UpperCamelCase       ,	decoder_attention_mask=UpperCamelCase						)

											snake_case__									=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
											self.parent.assertTrue(diff < 1e-3       ,	msg=F'''Max diff is {diff}'''						)


def      a_ (      _A      ,   _A      ,   _A      ,   _A=None      ,   _A=None      ,   )			->			Optional[Any]:







				"""simple docstring"""
				if attention_mask is None:
								snake_case__									=     np.not_equal(_A      ,   config.pad_token_id  ).astype(np.inta  )
				if decoder_attention_mask is None:
								snake_case__									=     np.concatenate(
								    [
								        np.ones(decoder_input_ids[:, :1].shape      ,   dtype=np.inta  ),
								        np.not_equal(decoder_input_ids[:, 1:]      ,   config.pad_token_id  ).astype(np.inta  ),
								    ]      ,   axis=-1      ,   )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				}


@require_flax
class   __SCREAMING_SNAKE_CASE(	a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       (
							    (
							        FlaxPegasusForConditionalGeneration,
							        FlaxPegasusModel,
							    )
							    if is_flax_available()
							    else ()
							)
							_UpperCAmelCase     =       (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      str						)		->		Union[str, Any]:
											snake_case__									=     FlaxPegasusModelTester(self						)
											snake_case__									=     ConfigTester(self       ,	config_class=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		str:
											self.config_tester.run_common_tests()
							def lowerCAmelCase_ (     self:      Tuple						)		->		Any:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      str						)		->		List[Any]:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		Dict:
											snake_case__							,						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__						):
																			snake_case__									=     self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)
																			snake_case__									=     model_class(UpperCamelCase						)

																			@jax.jit
																			def encode_jitted(UpperCamelCase:      int       ,	UpperCamelCase:      Optional[int]=None       ,	**UpperCamelCase:      Optional[Any]						):
																							return model.encode(input_ids=UpperCamelCase       ,	attention_mask=UpperCamelCase						)

																			with self.subTest('JIT Enabled'						):
																							snake_case__									=     encode_jitted(**UpperCamelCase						).to_tuple()

																			with self.subTest('JIT Disabled'						):
																							with jax.disable_jit():
																											snake_case__									=     encode_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 lowerCAmelCase_ (     self:      Tuple						)		->		Optional[Any]:
											snake_case__							,						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__						):
																			snake_case__									=     model_class(UpperCamelCase						)
																			snake_case__									=     model.encode(inputs_dict['input_ids']       ,	inputs_dict['attention_mask']						)

																			snake_case__									=     {
																			    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																			    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																			    'encoder_outputs': encoder_outputs,
																			}

																			@jax.jit
																			def decode_jitted(UpperCamelCase:      Dict       ,	UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      int						):
																							return model.decode(
																							    decoder_input_ids=UpperCamelCase       ,	decoder_attention_mask=UpperCamelCase       ,	encoder_outputs=UpperCamelCase       ,	)

																			with self.subTest('JIT Enabled'						):
																							snake_case__									=     decode_jitted(**UpperCamelCase						).to_tuple()

																			with self.subTest('JIT Disabled'						):
																							with jax.disable_jit():
																											snake_case__									=     decode_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						)
							@slow
							def lowerCAmelCase_ (     self:      Tuple						)		->		Tuple:
											for model_class_name in self.all_model_classes:
															snake_case__									=     model_class_name.from_pretrained('google/pegasus-large'       ,	from_pt=UpperCamelCase						)
															snake_case__									=     np.ones((1, 1)						)
															snake_case__									=     model(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)


							@slow
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Optional[int]:
											snake_case__									=     FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'						)
											snake_case__									=     PegasusTokenizer.from_pretrained('google/pegasus-xsum'						)

											snake_case__									=     [
											    ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
											    ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
											]

											snake_case__									=     [
											    'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
											    'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
											]

											snake_case__									=     tokenizer(UpperCamelCase       ,	return_tensors='np'       ,	truncation=UpperCamelCase       ,	max_length=5_12       ,	padding=UpperCamelCase						)
											snake_case__									=     model.generate(**UpperCamelCase       ,	num_beams=2						).sequences
											snake_case__									=     tokenizer.batch_decode(UpperCamelCase       ,	skip_special_tokens=UpperCamelCase						)
											assert tgt_text == decoded












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   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def __init__(     self:      List[str]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Optional[int]=3       ,	UpperCamelCase:      Union[str, Any]=32       ,	UpperCamelCase:      Optional[int]=3       ,	UpperCamelCase:      Any=10       ,	UpperCamelCase:      List[str]=[10, 20, 30, 40]       ,	UpperCamelCase:      Optional[Any]=[1, 1, 2, 1]       ,	UpperCamelCase:      Tuple=True       ,	UpperCamelCase:      List[str]=True       ,	UpperCamelCase:      Optional[int]="relu"       ,	UpperCamelCase:      List[str]=3       ,	UpperCamelCase:      Dict=None       ,	)		->		Optional[Any]:
											snake_case__									=     parent
											snake_case__									=     batch_size
											snake_case__									=     image_size
											snake_case__									=     num_channels
											snake_case__									=     embeddings_size
											snake_case__									=     hidden_sizes
											snake_case__									=     depths
											snake_case__									=     is_training
											snake_case__									=     use_labels
											snake_case__									=     hidden_act
											snake_case__									=     num_labels
											snake_case__									=     scope
											snake_case__									=     len(UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											snake_case__									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											snake_case__									=     self.get_config()

											return config, pixel_values
							def lowerCAmelCase_ (     self:      int						)		->		Optional[Any]:
											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 lowerCAmelCase_ (     self:      Optional[Any]       ,	UpperCamelCase:      List[Any]       ,	UpperCamelCase:      List[Any]						)		->		Optional[Any]:
											snake_case__									=     FlaxRegNetModel(config=UpperCamelCase						)
											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 // 32, self.image_size // 32)       ,	)
							def lowerCAmelCase_ (     self:      List[str]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Optional[Any]						)		->		List[str]:
											snake_case__									=     self.num_labels
											snake_case__									=     FlaxRegNetForImageClassification(config=UpperCamelCase						)
											snake_case__									=     model(UpperCamelCase						)
											self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)						)


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


@require_flax
class   __SCREAMING_SNAKE_CASE(	a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      Dict						)		->		None:
											snake_case__									=     FlaxRegNetModelTester(self						)
											snake_case__									=     ConfigTester(self       ,	config_class=UpperCamelCase       ,	has_text_modality=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      str						)		->		Optional[int]:
											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 lowerCAmelCase_ (     self:      List[Any]						)		->		List[str]:
											return
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Union[str, Any]:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		List[Any]:
											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 lowerCAmelCase_ (     self:      Optional[Any]						)		->		Dict:
											pass
							@unittest.skip(reason='RegNet does not support input and output embeddings'						)
							def lowerCAmelCase_ (     self:      List[str]						)		->		Dict:
											pass
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case__									=     ['pixel_values']
															self.assertListEqual(arg_names[:1]       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		int:
											def check_hidden_states_output(UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      List[Any]						):
															snake_case__									=     model_class(UpperCamelCase						)

															snake_case__									=     model(**self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)						)

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

															snake_case__									=     self.model_tester.num_stages
															self.assertEqual(len(UpperCamelCase						)       ,	expected_num_stages + 1						)

											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															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"]
															snake_case__									=     True

															check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)


							def lowerCAmelCase_ (     self:      str						)		->		str:
											snake_case__							,						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__						):
																			snake_case__									=     self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)
																			snake_case__									=     model_class(UpperCamelCase						)

																			@jax.jit
																			def model_jitted(UpperCamelCase:      str       ,	**UpperCamelCase:      Tuple						):
																							return model(pixel_values=UpperCamelCase       ,	**UpperCamelCase						)

																			with self.subTest('JIT Enabled'						):
																							snake_case__									=     model_jitted(**UpperCamelCase						).to_tuple()

																			with self.subTest('JIT Disabled'						):
																							with jax.disable_jit():
																											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_ (      )			->			Optional[int]:







				"""simple docstring"""
				snake_case__									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
				return image


@require_flax
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							@cached_property
							def lowerCAmelCase_ (     self:      str						)		->		Any:
											return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'						) if is_vision_available() else None


							@slow
							def lowerCAmelCase_ (     self:      List[str]						)		->		Optional[Any]:
											snake_case__									=     FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'						)

											snake_case__									=     self.default_image_processor
											snake_case__									=     prepare_img()
											snake_case__									=     image_processor(images=UpperCamelCase       ,	return_tensors='np'						)

											snake_case__									=     model(**UpperCamelCase						)

											# verify the logits
											snake_case__									=     (1, 10_00)
											self.assertEqual(outputs.logits.shape       ,	UpperCamelCase						)

											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						)						)




