



"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TF_MODEL_FOR_PRETRAINING_MAPPING,
		    TFMobileBertForMaskedLM,
		    TFMobileBertForMultipleChoice,
		    TFMobileBertForNextSentencePrediction,
		    TFMobileBertForPreTraining,
		    TFMobileBertForQuestionAnswering,
		    TFMobileBertForSequenceClassification,
		    TFMobileBertForTokenClassification,
		    TFMobileBertModel,
		)

@require_tf
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							(
					    (
					        TFMobileBertModel,
					        TFMobileBertForMaskedLM,
					        TFMobileBertForNextSentencePrediction,
					        TFMobileBertForPreTraining,
					        TFMobileBertForQuestionAnswering,
					        TFMobileBertForSequenceClassification,
					        TFMobileBertForTokenClassification,
					        TFMobileBertForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					_a             =							(
					    {
					        'feature-extraction': TFMobileBertModel,
					        'fill-mask': TFMobileBertForMaskedLM,
					        'question-answering': TFMobileBertForQuestionAnswering,
					        'text-classification': TFMobileBertForSequenceClassification,
					        'token-classification': TFMobileBertForTokenClassification,
					        'zero-shot': TFMobileBertForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					_a             =							False
					_a             =							False



					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  Any,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Optional[int]=False					)-> List[str]:
								lowerCamelCase__		:		List[str]		     =super()._prepare_for_class(lowerCamelCase,  lowerCamelCase,  return_labels=lowerCamelCase					)

								if return_labels:
											if model_class in get_values(lowerCamelCase					):
														lowerCamelCase__		:		Optional[int]		     =tf.zeros(self.model_tester.batch_size,  dtype=tf.intaa					)

								return inputs_dict

					class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


										'''simple docstring'''





										def __init__(	self							:  Any,  lowerCamelCase							:  int,  lowerCamelCase							:  Optional[Any]=13,  lowerCamelCase							:  Union[str, Any]=7,  lowerCamelCase							:  List[str]=True,  lowerCamelCase							:  Optional[Any]=True,  lowerCamelCase							:  str=True,  lowerCamelCase							:  List[str]=True,  lowerCamelCase							:  Any=99,  lowerCamelCase							:  List[str]=32,  lowerCamelCase							:  List[str]=32,  lowerCamelCase							:  List[Any]=2,  lowerCamelCase							:  Optional[Any]=4,  lowerCamelCase							:  List[str]=37,  lowerCamelCase							:  Optional[Any]="gelu",  lowerCamelCase							:  str=0.1,  lowerCamelCase							:  Optional[int]=0.1,  lowerCamelCase							:  Union[str, Any]=512,  lowerCamelCase							:  List[Any]=16,  lowerCamelCase							:  Optional[Any]=2,  lowerCamelCase							:  Optional[Any]=0.02,  lowerCamelCase							:  Any=3,  lowerCamelCase							:  Any=4,  lowerCamelCase							:  List[str]=None,  )-> Any:
													lowerCamelCase__		:		Tuple		     =parent
													lowerCamelCase__		:		List[str]		     =batch_size
													lowerCamelCase__		:		List[str]		     =seq_length
													lowerCamelCase__		:		Optional[Any]		     =is_training
													lowerCamelCase__		:		Union[str, Any]		     =use_input_mask
													lowerCamelCase__		:		Any		     =use_token_type_ids
													lowerCamelCase__		:		Tuple		     =use_labels
													lowerCamelCase__		:		Union[str, Any]		     =vocab_size
													lowerCamelCase__		:		int		     =hidden_size
													lowerCamelCase__		:		List[Any]		     =num_hidden_layers
													lowerCamelCase__		:		Tuple		     =num_attention_heads
													lowerCamelCase__		:		List[Any]		     =intermediate_size
													lowerCamelCase__		:		List[str]		     =hidden_act
													lowerCamelCase__		:		List[str]		     =hidden_dropout_prob
													lowerCamelCase__		:		Optional[Any]		     =attention_probs_dropout_prob
													lowerCamelCase__		:		str		     =max_position_embeddings
													lowerCamelCase__		:		List[str]		     =type_vocab_size
													lowerCamelCase__		:		List[Any]		     =type_sequence_label_size
													lowerCamelCase__		:		Union[str, Any]		     =initializer_range
													lowerCamelCase__		:		int		     =num_labels
													lowerCamelCase__		:		str		     =num_choices
													lowerCamelCase__		:		Tuple		     =scope
													lowerCamelCase__		:		Tuple		     =embedding_size



										def 		snake_case	(	self							:  Union[str, Any]					)-> int:
													lowerCamelCase__		:		List[str]		     =ids_tensor([self.batch_size, self.seq_length],  self.vocab_size					)

													lowerCamelCase__		:		List[str]		     =None
													if self.use_input_mask:
																lowerCamelCase__		:		str		     =random_attention_mask([self.batch_size, self.seq_length]					)

													lowerCamelCase__		:		int		     =None
													if self.use_token_type_ids:
																lowerCamelCase__		:		int		     =ids_tensor([self.batch_size, self.seq_length],  self.type_vocab_size					)

													lowerCamelCase__		:		List[Any]		     =None
													lowerCamelCase__		:		Union[str, Any]		     =None
													lowerCamelCase__		:		Dict		     =None
													if self.use_labels:
																lowerCamelCase__		:		int		     =ids_tensor([self.batch_size],  self.type_sequence_label_size					)
																lowerCamelCase__		:		Union[str, Any]		     =ids_tensor([self.batch_size, self.seq_length],  self.num_labels					)
																lowerCamelCase__		:		Dict		     =ids_tensor([self.batch_size],  self.num_choices					)

													lowerCamelCase__		:		Any		     =MobileBertConfig(
													    vocab_size=self.vocab_size,  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,  max_position_embeddings=self.max_position_embeddings,  type_vocab_size=self.type_vocab_size,  initializer_range=self.initializer_range,  embedding_size=self.embedding_size,  )

													return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



										def 		snake_case	(	self							:  int,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Any,  lowerCamelCase							:  List[str]					)-> Optional[int]:
													lowerCamelCase__		:		str		     =TFMobileBertModel(config=lowerCamelCase					)
													lowerCamelCase__		:		List[Any]		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													lowerCamelCase__		:		List[str]		     =model(lowerCamelCase					)

													lowerCamelCase__		:		List[Any]		     =[input_ids, input_mask]
													lowerCamelCase__		:		Optional[int]		     =model(lowerCamelCase					)

													lowerCamelCase__		:		Optional[int]		     =model(lowerCamelCase					)

													self.parent.assertEqual(
													    result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)					)
													self.parent.assertEqual(result.pooler_output.shape,  (self.batch_size, self.hidden_size)					)



										def 		snake_case	(	self							:  int,  lowerCamelCase							:  str,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  int,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  List[str],  lowerCamelCase							:  int					)-> str:
													lowerCamelCase__		:		Optional[Any]		     =TFMobileBertForMaskedLM(config=lowerCamelCase					)
													lowerCamelCase__		:		str		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													lowerCamelCase__		:		Optional[int]		     =model(lowerCamelCase					)
													self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)					)



										def 		snake_case	(	self							:  str,  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Tuple,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  int,  lowerCamelCase							:  int,  lowerCamelCase							:  int					)-> Any:
													lowerCamelCase__		:		str		     =TFMobileBertForNextSentencePrediction(config=lowerCamelCase					)
													lowerCamelCase__		:		str		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													lowerCamelCase__		:		Optional[int]		     =model(lowerCamelCase					)
													self.parent.assertEqual(result.logits.shape,  (self.batch_size, 2)					)



										def 		snake_case	(	self							:  Tuple,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Tuple,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Optional[Any]					)-> Optional[int]:
													lowerCamelCase__		:		List[Any]		     =TFMobileBertForPreTraining(config=lowerCamelCase					)
													lowerCamelCase__		:		str		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													lowerCamelCase__		:		str		     =model(lowerCamelCase					)
													self.parent.assertEqual(
													    result.prediction_logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)					)
													self.parent.assertEqual(result.seq_relationship_logits.shape,  (self.batch_size, 2)					)



										def 		snake_case	(	self							:  Union[str, Any],  lowerCamelCase							:  Any,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  str,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  int					)-> Dict:
													lowerCamelCase__		:		Any		     =self.num_labels
													lowerCamelCase__		:		Optional[int]		     =TFMobileBertForSequenceClassification(config=lowerCamelCase					)
													lowerCamelCase__		:		str		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													lowerCamelCase__		:		int		     =model(lowerCamelCase					)
													self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)					)



										def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  Any,  lowerCamelCase							:  Dict,  lowerCamelCase							:  int,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  List[str]					)-> Optional[int]:
													lowerCamelCase__		:		Optional[int]		     =self.num_choices
													lowerCamelCase__		:		str		     =TFMobileBertForMultipleChoice(config=lowerCamelCase					)
													lowerCamelCase__		:		Any		     =tf.tile(tf.expand_dims(lowerCamelCase,  1					),  (1, self.num_choices, 1)					)
													lowerCamelCase__		:		Any		     =tf.tile(tf.expand_dims(lowerCamelCase,  1					),  (1, self.num_choices, 1)					)
													lowerCamelCase__		:		str		     =tf.tile(tf.expand_dims(lowerCamelCase,  1					),  (1, self.num_choices, 1)					)
													lowerCamelCase__		:		Optional[Any]		     ={
													    '''input_ids''': multiple_choice_inputs_ids,
													    '''attention_mask''': multiple_choice_input_mask,
													    '''token_type_ids''': multiple_choice_token_type_ids,
													}
													lowerCamelCase__		:		Optional[Any]		     =model(lowerCamelCase					)
													self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_choices)					)



										def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Tuple,  lowerCamelCase							:  str,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Dict					)-> Tuple:
													lowerCamelCase__		:		Dict		     =self.num_labels
													lowerCamelCase__		:		List[Any]		     =TFMobileBertForTokenClassification(config=lowerCamelCase					)
													lowerCamelCase__		:		Tuple		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													lowerCamelCase__		:		Optional[int]		     =model(lowerCamelCase					)
													self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.num_labels)					)



										def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  str,  lowerCamelCase							:  str,  lowerCamelCase							:  int,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Tuple					)-> str:
													lowerCamelCase__		:		List[str]		     =TFMobileBertForQuestionAnswering(config=lowerCamelCase					)
													lowerCamelCase__		:		Union[str, Any]		     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													lowerCamelCase__		:		Optional[int]		     =model(lowerCamelCase					)
													self.parent.assertEqual(result.start_logits.shape,  (self.batch_size, self.seq_length)					)
													self.parent.assertEqual(result.end_logits.shape,  (self.batch_size, self.seq_length)					)




										def 		snake_case	(	self							:  Union[str, Any]					)-> List[str]:
													lowerCamelCase__		:		Optional[int]		     =self.prepare_config_and_inputs()
													(
													    (
													    lowerCamelCase__
													)     ,       (
													    lowerCamelCase__
													)     ,       (
													    lowerCamelCase__
													)     ,       (
													    lowerCamelCase__
													)     ,       (
													    lowerCamelCase__
													)     ,       (
													    lowerCamelCase__
													)     ,       (
													    lowerCamelCase__
													)     ,       
													)		:		int		     =config_and_inputs
													lowerCamelCase__		:		Dict		     ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
													return config, inputs_dict





					def 		snake_case	(	self							:  Dict					)-> Dict:
								lowerCamelCase__		:		Dict		     =TFMobileBertModelTest.TFMobileBertModelTester(self					)
								lowerCamelCase__		:		List[Any]		     =ConfigTester(self,  config_class=lowerCamelCase,  hidden_size=37					)



					def 		snake_case	(	self							:  Union[str, Any]					)-> List[Any]:
								self.config_tester.run_common_tests()



					def 		snake_case	(	self							:  Any					)-> Any:
								lowerCamelCase__		:		Union[str, Any]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*lowerCamelCase					)



					def 		snake_case	(	self							:  Optional[Any]					)-> Tuple:
								lowerCamelCase__		:		Optional[int]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCamelCase					)



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__		:		List[str]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCamelCase					)



					def 		snake_case	(	self							:  Any					)-> Optional[int]:
								lowerCamelCase__		:		Union[str, Any]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCamelCase					)



					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								lowerCamelCase__		:		List[Any]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCamelCase					)



					def 		snake_case	(	self							:  List[Any]					)-> Optional[Any]:
								lowerCamelCase__		:		List[str]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCamelCase					)



					def 		snake_case	(	self							:  List[Any]					)-> List[Any]:
								lowerCamelCase__		:		List[Any]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCamelCase					)



					def 		snake_case	(	self							:  Optional[int]					)-> Dict:
								lowerCamelCase__		:		List[str]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCamelCase					)




					@slow
					def 		snake_case	(	self							:  List[str]					)-> Tuple:
								# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["google/mobilebert-uncased"]:
											lowerCamelCase__		:		int		     =TFMobileBertModel.from_pretrained(lowerCamelCase					)
											self.assertIsNotNone(lowerCamelCase					)





@require_tf
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					@slow
					def 		snake_case	(	self							:  Any					)-> List[Any]:
								lowerCamelCase__		:		Any		     =TFMobileBertForPreTraining.from_pretrained('''google/mobilebert-uncased'''					)
								lowerCamelCase__		:		str		     =tf.constant([[0, 1, 2, 3, 4, 5]]					)
								lowerCamelCase__		:		List[Any]		     =model(lowerCamelCase					)[0]

								lowerCamelCase__		:		Dict		     =[1, 6, 3_0522]
								self.assertEqual(output.shape,  lowerCamelCase					)

								lowerCamelCase__		:		Optional[int]		     =tf.constant(
								    [
								        [
								            [-4.5_919_547, -9.248_295, -9.645_256],
								            [-6.7_306_175, -6.440_284, -6.6_052_837],
								            [-7.2_743_506, -6.7_847_915, -6.024_673],
								        ]
								    ]					)
								tf.debugging.assert_near(output[:, :3, :3],  lowerCamelCase,  atol=1E-4					)









"""simple docstring"""






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
		import jax
		import jax.numpy as jnp
		from flax.jax_utils import replicate
		from flax.training.common_utils import shard

@slow
@require_flax
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Union[str, Any]					)-> Dict:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[Any]		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[int]		     =controlnet_params

								lowerCamelCase__		:		Dict		     ='''bird'''
								lowerCamelCase__		:		List[str]		     =jax.device_count()
								lowerCamelCase__		:		Optional[Any]		     =pipe.prepare_text_inputs([prompts] * num_samples					)

								lowerCamelCase__		:		Dict		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''					)
								lowerCamelCase__		:		List[Any]		     =pipe.prepare_image_inputs([canny_image] * num_samples					)

								lowerCamelCase__		:		Optional[int]		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Dict		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		Tuple		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		Optional[int]		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =pipe(
								    prompt_ids=lowerCamelCase,  image=lowerCamelCase,  params=lowerCamelCase,  prng_seed=lowerCamelCase,  num_inference_steps=50,  jit=lowerCamelCase,  ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								lowerCamelCase__		:		Dict		     =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
								lowerCamelCase__		:		Any		     =images[0, 253:256, 253:256, -1]

								lowerCamelCase__		:		Optional[Any]		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Dict		     =jnp.array(
								    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								lowerCamelCase__     ,       lowerCamelCase__		:		Dict		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[Any]		     =controlnet_params

								lowerCamelCase__		:		int		     ='''Chef in the kitchen'''
								lowerCamelCase__		:		Optional[Any]		     =jax.device_count()
								lowerCamelCase__		:		Any		     =pipe.prepare_text_inputs([prompts] * num_samples					)

								lowerCamelCase__		:		Any		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'''					)
								lowerCamelCase__		:		List[Any]		     =pipe.prepare_image_inputs([pose_image] * num_samples					)

								lowerCamelCase__		:		Tuple		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Optional[Any]		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		int		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		int		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =pipe(
								    prompt_ids=lowerCamelCase,  image=lowerCamelCase,  params=lowerCamelCase,  prng_seed=lowerCamelCase,  num_inference_steps=50,  jit=lowerCamelCase,  ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								lowerCamelCase__		:		Dict		     =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
								lowerCamelCase__		:		List[str]		     =images[0, 253:256, 253:256, -1]

								lowerCamelCase__		:		int		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Any		     =jnp.array(
								    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




