





import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY







@is_pipeline_test
class 			__A       (      unittest.TestCase  ):




						"""simple docstring"""







						UpperCAmelCase__						=      MODEL_FOR_MASKED_LM_MAPPING
						UpperCAmelCase__						=      TF_MODEL_FOR_MASKED_LM_MAPPING


						def 				__snake_case			(		self):



													"""simple docstring"""





													super().tearDown()
													# clean-up as much as possible GPU memory occupied by PyTorch
													gc.collect()
													if is_torch_available():
																				import torch

																				torch.cuda.empty_cache()


						@require_tf
						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Dict	=		pipeline(task='''fill-mask'''      ,							model='''sshleifer/tiny-distilroberta-base'''      ,							top_k=2      ,							framework='''tf''')
													_lowerCamelCase							:     int	=		unmasker('''My name is <mask>''')
													self.assertEqual(
													    nested_simplify(a__      ,							decimals=6)      ,							[
													        {'''sequence''': '''My name is grouped''', '''score''': 2.1e-05, '''token''': 3_8015, '''token_str''': ''' grouped'''},
													        {'''sequence''': '''My name is accuser''', '''score''': 2.1e-05, '''token''': 2_5506, '''token_str''': ''' accuser'''},
													    ]      ,							)

													_lowerCamelCase							:     List[str]	=		unmasker('''The largest city in France is <mask>''')
													self.assertEqual(
													    nested_simplify(a__      ,							decimals=6)      ,							[
													        {
													            '''sequence''': '''The largest city in France is grouped''',
													            '''score''': 2.1e-05,
													            '''token''': 3_8015,
													            '''token_str''': ''' grouped''',
													        },
													        {
													            '''sequence''': '''The largest city in France is accuser''',
													            '''score''': 2.1e-05,
													            '''token''': 2_5506,
													            '''token_str''': ''' accuser''',
													        },
													    ]      ,							)

													_lowerCamelCase							:     str	=		unmasker('''My name is <mask>'''      ,							targets=[''' Patrick''', ''' Clara''', ''' Teven''']      ,							top_k=3)
													self.assertEqual(
													    nested_simplify(a__      ,							decimals=6)      ,							[
													        {'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 1_3606, '''token_str''': ''' Clara'''},
													        {'''sequence''': '''My name is Patrick''', '''score''': 2e-05, '''token''': 3499, '''token_str''': ''' Patrick'''},
													        {'''sequence''': '''My name is Te''', '''score''': 1.9e-05, '''token''': 2941, '''token_str''': ''' Te'''},
													    ]      ,							)


						@require_torch
						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Union[str, Any]	=		pipeline(task='''fill-mask'''      ,							model='''sshleifer/tiny-distilroberta-base'''      ,							top_k=2      ,							framework='''pt''')

													_lowerCamelCase							:     Optional[int]	=		unmasker('''My name is <mask>''')
													self.assertEqual(
													    nested_simplify(a__      ,							decimals=6)      ,							[
													        {'''sequence''': '''My name is Maul''', '''score''': 2.2e-05, '''token''': 3_5676, '''token_str''': ''' Maul'''},
													        {'''sequence''': '''My name isELS''', '''score''': 2.2e-05, '''token''': 1_6416, '''token_str''': '''ELS'''},
													    ]      ,							)

													_lowerCamelCase							:     List[str]	=		unmasker('''The largest city in France is <mask>''')
													self.assertEqual(
													    nested_simplify(a__      ,							decimals=6)      ,							[
													        {
													            '''sequence''': '''The largest city in France is Maul''',
													            '''score''': 2.2e-05,
													            '''token''': 3_5676,
													            '''token_str''': ''' Maul''',
													        },
													        {'''sequence''': '''The largest city in France isELS''', '''score''': 2.2e-05, '''token''': 1_6416, '''token_str''': '''ELS'''},
													    ]      ,							)

													_lowerCamelCase							:     Optional[Any]	=		unmasker('''My name is <mask>'''      ,							targets=[''' Patrick''', ''' Clara''', ''' Teven''']      ,							top_k=3)
													self.assertEqual(
													    nested_simplify(a__      ,							decimals=6)      ,							[
													        {'''sequence''': '''My name is Patrick''', '''score''': 2.1e-05, '''token''': 3499, '''token_str''': ''' Patrick'''},
													        {'''sequence''': '''My name is Te''', '''score''': 2e-05, '''token''': 2941, '''token_str''': ''' Te'''},
													        {'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 1_3606, '''token_str''': ''' Clara'''},
													    ]      ,							)

													_lowerCamelCase							:     Union[str, Any]	=		unmasker('''My name is <mask> <mask>'''      ,							top_k=2)

													self.assertEqual(
													    nested_simplify(a__      ,							decimals=6)      ,							[
													        [
													            {
													                '''score''': 2.2e-05,
													                '''token''': 3_5676,
													                '''token_str''': ''' Maul''',
													                '''sequence''': '''<s>My name is Maul<mask></s>''',
													            },
													            {'''score''': 2.2e-05, '''token''': 1_6416, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
													        ],
													        [
													            {
													                '''score''': 2.2e-05,
													                '''token''': 3_5676,
													                '''token_str''': ''' Maul''',
													                '''sequence''': '''<s>My name is<mask> Maul</s>''',
													            },
													            {'''score''': 2.2e-05, '''token''': 1_6416, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
													        ],
													    ]      ,							)


						@require_torch_gpu
						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     int	=		pipeline('''fill-mask'''      ,							model='''hf-internal-testing/tiny-random-distilbert'''      ,							device=0      ,							framework='''pt''')

													# convert model to fp16
													pipe.model.half()

													_lowerCamelCase							:     Tuple	=		pipe('''Paris is the [MASK] of France.''')
													# We actually don't care about the result, we just want to make sure
													# it works, meaning the float16 tensor got casted back to float32
													# for postprocessing.
													self.assertIsInstance(a__      ,							a__)


						@slow
						@require_torch
						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[int]	=		pipeline(task='''fill-mask'''      ,							model='''distilroberta-base'''      ,							top_k=2      ,							framework='''pt''')
													self.run_large_test(a__)


						@slow
						@require_tf
						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[int]	=		pipeline(task='''fill-mask'''      ,							model='''distilroberta-base'''      ,							top_k=2      ,							framework='''tf''')
													self.run_large_test(a__)


						def 				__snake_case			(		self      ,							a__):



													"""simple docstring"""





													_lowerCamelCase							:     Any	=		unmasker('''My name is <mask>''')
													self.assertEqual(
													    nested_simplify(a__)      ,							[
													        {'''sequence''': '''My name is John''', '''score''': 0.008, '''token''': 610, '''token_str''': ''' John'''},
													        {'''sequence''': '''My name is Chris''', '''score''': 0.007, '''token''': 1573, '''token_str''': ''' Chris'''},
													    ]      ,							)
													_lowerCamelCase							:     Union[str, Any]	=		unmasker('''The largest city in France is <mask>''')
													self.assertEqual(
													    nested_simplify(a__)      ,							[
													        {
													            '''sequence''': '''The largest city in France is Paris''',
													            '''score''': 0.251,
													            '''token''': 2201,
													            '''token_str''': ''' Paris''',
													        },
													        {
													            '''sequence''': '''The largest city in France is Lyon''',
													            '''score''': 0.214,
													            '''token''': 1_2790,
													            '''token_str''': ''' Lyon''',
													        },
													    ]      ,							)

													_lowerCamelCase							:     List[Any]	=		unmasker('''My name is <mask>'''      ,							targets=[''' Patrick''', ''' Clara''', ''' Teven''']      ,							top_k=3)
													self.assertEqual(
													    nested_simplify(a__)      ,							[
													        {'''sequence''': '''My name is Patrick''', '''score''': 0.005, '''token''': 3499, '''token_str''': ''' Patrick'''},
													        {'''sequence''': '''My name is Clara''', '''score''': 0.000, '''token''': 1_3606, '''token_str''': ''' Clara'''},
													        {'''sequence''': '''My name is Te''', '''score''': 0.000, '''token''': 2941, '''token_str''': ''' Te'''},
													    ]      ,							)


						@require_torch
						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     int	=		pipeline(task='''fill-mask'''      ,							model='''sshleifer/tiny-distilroberta-base'''      ,							framework='''pt''')
													_lowerCamelCase							:     List[str]	=		None
													_lowerCamelCase							:     Any	=		None
													self.run_pipeline_test(a__      ,							[])


						@require_tf
						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[int]	=		pipeline(task='''fill-mask'''      ,							model='''sshleifer/tiny-distilroberta-base'''      ,							framework='''tf''')
													_lowerCamelCase							:     Optional[Any]	=		None
													_lowerCamelCase							:     int	=		None
													self.run_pipeline_test(a__      ,							[])


						def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



													"""simple docstring"""





													if tokenizer is None or tokenizer.mask_token_id is None:
																				self.skipTest('''The provided tokenizer has no mask token, (probably reformer or wav2vec2)''')

													_lowerCamelCase							:     str	=		FillMaskPipeline(model=a__      ,							tokenizer=a__)
													_lowerCamelCase							:     Optional[int]	=		[
													    F"""This is another {tokenizer.mask_token} test""",
													]
													return fill_masker, examples


						def 				__snake_case			(		self      ,							a__      ,							a__):



													"""simple docstring"""





													_lowerCamelCase							:     List[Any]	=		fill_masker.tokenizer
													_lowerCamelCase							:     List[str]	=		fill_masker.model

													_lowerCamelCase							:     Union[str, Any]	=		fill_masker(
													    F"""This is a {tokenizer.mask_token}"""      ,							)
													self.assertEqual(
													    a__      ,							[
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													    ]      ,							)

													_lowerCamelCase							:     Optional[int]	=		fill_masker([F"""This is a {tokenizer.mask_token}"""])
													self.assertEqual(
													    a__      ,							[
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													    ]      ,							)

													_lowerCamelCase							:     Any	=		fill_masker([F"""This is a {tokenizer.mask_token}""", F"""Another {tokenizer.mask_token} great test."""])
													self.assertEqual(
													    a__      ,							[
													        [
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        ],
													        [
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        ],
													    ]      ,							)

													with self.assertRaises(a__):
																				fill_masker([None])
													# No mask_token is not supported
													with self.assertRaises(a__):
																				fill_masker('''This is''')

													self.run_test_top_k(a__      ,							a__)
													self.run_test_targets(a__      ,							a__)
													self.run_test_top_k_targets(a__      ,							a__)
													self.fill_mask_with_duplicate_targets_and_top_k(a__      ,							a__)
													self.fill_mask_with_multiple_masks(a__      ,							a__)


						def 				__snake_case			(		self      ,							a__      ,							a__):



													"""simple docstring"""





													_lowerCamelCase							:     str	=		tokenizer.get_vocab()
													_lowerCamelCase							:     Optional[int]	=		sorted(vocab.keys())[:2]
													# Pipeline argument
													_lowerCamelCase							:     List[str]	=		FillMaskPipeline(model=a__      ,							tokenizer=a__      ,							targets=a__)
													_lowerCamelCase							:     Any	=		fill_masker(F"""This is a {tokenizer.mask_token}""")
													self.assertEqual(
													    a__      ,							[
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													    ]      ,							)
													_lowerCamelCase							:     Optional[int]	=		{vocab[el] for el in targets}
													self.assertEqual({el['''token'''] for el in outputs}      ,							a__)
													_lowerCamelCase							:     Dict	=		[tokenizer.decode([x]) for x in target_ids]
													self.assertEqual({el['''token_str'''] for el in outputs}      ,							set(a__))

													# Call argument
													_lowerCamelCase							:     Optional[int]	=		FillMaskPipeline(model=a__      ,							tokenizer=a__)
													_lowerCamelCase							:     int	=		fill_masker(F"""This is a {tokenizer.mask_token}"""      ,							targets=a__)
													self.assertEqual(
													    a__      ,							[
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													    ]      ,							)
													_lowerCamelCase							:     List[Any]	=		{vocab[el] for el in targets}
													self.assertEqual({el['''token'''] for el in outputs}      ,							a__)
													_lowerCamelCase							:     Dict	=		[tokenizer.decode([x]) for x in target_ids]
													self.assertEqual({el['''token_str'''] for el in outputs}      ,							set(a__))

													# Score equivalence
													_lowerCamelCase							:     Optional[int]	=		fill_masker(F"""This is a {tokenizer.mask_token}"""      ,							targets=a__)
													_lowerCamelCase							:     Dict	=		[top_mask['''token_str'''] for top_mask in outputs]
													_lowerCamelCase							:     str	=		[top_mask['''score'''] for top_mask in outputs]

													# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
													if set(a__) == set(a__):
																				_lowerCamelCase							:     List[Any]	=		fill_masker(F"""This is a {tokenizer.mask_token}"""      ,							targets=a__)
																				_lowerCamelCase							:     List[Any]	=		[top_mask['''score'''] for top_mask in unmasked_targets]
																				self.assertEqual(nested_simplify(a__)      ,							nested_simplify(a__))

													# Raises with invalid
													with self.assertRaises(a__):
																				_lowerCamelCase							:     Any	=		fill_masker(F"""This is a {tokenizer.mask_token}"""      ,							targets=[])
													# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
													if "" not in tokenizer.get_vocab():
																				with self.assertRaises(a__):
																											_lowerCamelCase							:     str	=		fill_masker(F"""This is a {tokenizer.mask_token}"""      ,							targets=[''''''])
																				with self.assertRaises(a__):
																											_lowerCamelCase							:     Union[str, Any]	=		fill_masker(F"""This is a {tokenizer.mask_token}"""      ,							targets='''''')


						def 				__snake_case			(		self      ,							a__      ,							a__):



													"""simple docstring"""





													_lowerCamelCase							:     Any	=		FillMaskPipeline(model=a__      ,							tokenizer=a__      ,							top_k=2)
													_lowerCamelCase							:     Union[str, Any]	=		fill_masker(F"""This is a {tokenizer.mask_token}""")
													self.assertEqual(
													    a__      ,							[
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													    ]      ,							)

													_lowerCamelCase							:     Tuple	=		FillMaskPipeline(model=a__      ,							tokenizer=a__)
													_lowerCamelCase							:     List[Any]	=		fill_masker(F"""This is a {tokenizer.mask_token}"""      ,							top_k=2)
													self.assertEqual(
													    a__      ,							[
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													    ]      ,							)
													self.assertEqual(nested_simplify(a__)      ,							nested_simplify(a__))


						def 				__snake_case			(		self      ,							a__      ,							a__):



													"""simple docstring"""





													_lowerCamelCase							:     Dict	=		tokenizer.get_vocab()
													_lowerCamelCase							:     Any	=		FillMaskPipeline(model=a__      ,							tokenizer=a__)

													# top_k=2, ntargets=3
													_lowerCamelCase							:     List[Any]	=		sorted(vocab.keys())[:3]
													_lowerCamelCase							:     Tuple	=		fill_masker(F"""This is a {tokenizer.mask_token}"""      ,							top_k=2      ,							targets=a__)

													# If we use the most probably targets, and filter differently, we should still
													# have the same results
													_lowerCamelCase							:     Union[str, Any]	=		[el['''token_str'''] for el in sorted(a__      ,							key=lambda a__: x["score"]      ,							reverse=a__)]
													# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
													if set(a__).issubset(a__):
																				_lowerCamelCase							:     Tuple	=		fill_masker(F"""This is a {tokenizer.mask_token}"""      ,							top_k=3      ,							targets=a__)
																				# They should yield exactly the same result
																				self.assertEqual(nested_simplify(a__)      ,							nested_simplify(a__))


						def 				__snake_case			(		self      ,							a__      ,							a__):



													"""simple docstring"""





													_lowerCamelCase							:     str	=		FillMaskPipeline(model=a__      ,							tokenizer=a__)
													_lowerCamelCase							:     Any	=		tokenizer.get_vocab()
													# String duplicates + id duplicates
													_lowerCamelCase							:     Any	=		sorted(vocab.keys())[:3]
													_lowerCamelCase							:     str	=		[targets[0], targets[1], targets[0], targets[2], targets[1]]
													_lowerCamelCase							:     str	=		fill_masker(F"""My name is {tokenizer.mask_token}"""      ,							targets=a__      ,							top_k=10)

													# The target list contains duplicates, so we can't output more
													# than them
													self.assertEqual(len(a__)      ,							3)



						def 				__snake_case			(		self      ,							a__      ,							a__):



													"""simple docstring"""





													_lowerCamelCase							:     Tuple	=		FillMaskPipeline(model=a__      ,							tokenizer=a__)

													_lowerCamelCase							:     Union[str, Any]	=		fill_masker(
													    F"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""      ,							top_k=2)
													self.assertEqual(
													    a__      ,							[
													        [
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        ],
													        [
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        ],
													        [
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													            {'''sequence''': ANY(a__), '''score''': ANY(a__), '''token''': ANY(a__), '''token_str''': ANY(a__)},
													        ],
													    ]      ,							)








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 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             super().tearDown()
             gc.collect()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		FlaxControlNetModel.from_pretrained(
                 '''lllyasviel/sd-controlnet-canny'''      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 '''runwayml/stable-diffusion-v1-5'''      ,							controlnet=a__      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase							:     Union[str, Any]	=		controlnet_params

             _lowerCamelCase							:     str	=		'''bird'''
             _lowerCamelCase							:     Union[str, Any]	=		jax.device_count()
             _lowerCamelCase							:     Dict	=		pipe.prepare_text_inputs([prompts] * num_samples)

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

             _lowerCamelCase							:     Union[str, Any]	=		jax.random.PRNGKey(0)
             _lowerCamelCase							:     Tuple	=		jax.random.split(a__      ,							jax.device_count())

             _lowerCamelCase							:     Dict	=		replicate(a__)
             _lowerCamelCase							:     Tuple	=		shard(a__)
             _lowerCamelCase							:     Union[str, Any]	=		shard(a__)

             _lowerCamelCase							:     Optional[Any]	=		pipe(
                 prompt_ids=a__      ,							image=a__      ,							params=a__      ,							prng_seed=a__      ,							num_inference_steps=50      ,							jit=a__      ,							).images
             assert images.shape == (jax.device_count(), 1, 768, 512, 3)

             _lowerCamelCase							:     Optional[int]	=		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
             _lowerCamelCase							:     List[Any]	=		images[0, 253:256, 253:256, -1]

             _lowerCamelCase							:     Union[str, Any]	=		jnp.asarray(jax.device_get(image_slice.flatten()))
             _lowerCamelCase							:     Optional[int]	=		jnp.array(
                 [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078])
             print(F"""output_slice: {output_slice}""")
             assert jnp.abs(output_slice - expected_slice).max() < 1e-2



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Any	=		FlaxControlNetModel.from_pretrained(
                 '''lllyasviel/sd-controlnet-openpose'''      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase,  _lowerCamelCase							:     Any	=		FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 '''runwayml/stable-diffusion-v1-5'''      ,							controlnet=a__      ,							from_pt=a__      ,							dtype=jnp.bfloataa)
             _lowerCamelCase							:     Optional[Any]	=		controlnet_params

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

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

             _lowerCamelCase							:     Optional[Any]	=		jax.random.PRNGKey(0)
             _lowerCamelCase							:     List[str]	=		jax.random.split(a__      ,							jax.device_count())

             _lowerCamelCase							:     str	=		replicate(a__)
             _lowerCamelCase							:     Union[str, Any]	=		shard(a__)
             _lowerCamelCase							:     List[Any]	=		shard(a__)

             _lowerCamelCase							:     Any	=		pipe(
                 prompt_ids=a__      ,							image=a__      ,							params=a__      ,							prng_seed=a__      ,							num_inference_steps=50      ,							jit=a__      ,							).images
             assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

             _lowerCamelCase							:     Optional[int]	=		jnp.asarray(jax.device_get(image_slice.flatten()))
             _lowerCamelCase							:     Optional[int]	=		jnp.array(
                 [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]])
             print(F"""output_slice: {output_slice}""")
             assert jnp.abs(output_slice - expected_slice).max() < 1e-2

