





import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class   lowerCAmelCase				(   __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						RobertaTokenizer
 UpperCAmelCase__						=						RobertaTokenizerFast
 UpperCAmelCase__						=						True
 UpperCAmelCase__						=						{"""cls_token""": """<s>"""}
 def 	A_      ( self    : Dict      )     ->      Optional[int]:
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     lowerCamelCase__       : str   					=						[
         'l',
         'o',
         'w',
         'e',
         'r',
         's',
         't',
         'i',
         'd',
         'n',
         '\u0120',
         '\u0120l',
         '\u0120n',
         '\u0120lo',
         '\u0120low',
         'er',
         '\u0120lowest',
         '\u0120newer',
         '\u0120wider',
         '<unk>',
     ]
     lowerCamelCase__       : str   					=						dict(zip(UpperCAmelCase     ,	range(len(UpperCAmelCase      )      )      )      )
     lowerCamelCase__       : Any   					=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
     lowerCamelCase__       : Any   					=						{'unk_token': '<unk>'}

     lowerCamelCase__       : Optional[Any]   					=						os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['vocab_file']      )
     lowerCamelCase__       : Tuple   					=						os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['merges_file']      )
     with open(self.vocab_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
         fp.write(json.dumps(UpperCAmelCase      ) + '\n'      )
     with open(self.merges_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
         fp.write('\n'.join(UpperCAmelCase      )      )
 def 	A_      ( self    : Tuple     ,	**UpperCAmelCase    : Union[str, Any]      )     ->      List[Any]:
     kwargs.update(self.special_tokens_map      )
     return self.tokenizer_class.from_pretrained(self.tmpdirname     ,	**UpperCAmelCase      )
 def 	A_      ( self    : str     ,	**UpperCAmelCase    : Any      )     ->      str:
     kwargs.update(self.special_tokens_map      )
     return RobertaTokenizerFast.from_pretrained(self.tmpdirname     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : Tuple      )     ->      List[Any]:
     lowerCamelCase__       : Dict   					=						'lower newer'
     lowerCamelCase__       : List[str]   					=						'lower newer'
     return input_text, output_text
 def 	A_      ( self    : Any      )     ->      Optional[int]:
     lowerCamelCase__       : Union[str, Any]   					=						self.tokenizer_class(self.vocab_file     ,	self.merges_file     ,	**self.special_tokens_map      )
     lowerCamelCase__       : List[str]   					=						'lower newer'
     lowerCamelCase__       : Optional[int]   					=						['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
     lowerCamelCase__       : Tuple   					=						tokenizer.tokenize(UpperCAmelCase      )  # , add_prefix_space=True)
     self.assertListEqual(UpperCAmelCase     ,	UpperCAmelCase      )

     lowerCamelCase__       : Union[str, Any]   					=						tokens + [tokenizer.unk_token]
     lowerCamelCase__       : List[str]   					=						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase      )     ,	UpperCAmelCase      )
 def 	A_      ( self    : List[str]      )     ->      Optional[int]:
     lowerCamelCase__       : Any   					=						self.get_tokenizer()

     self.assertListEqual(tokenizer.encode('Hello world!'     ,	add_special_tokens=UpperCAmelCase      )     ,	[0, 31414, 232, 328, 2]      )
     self.assertListEqual(
         tokenizer.encode('Hello world! cécé herlolip 418'     ,	add_special_tokens=UpperCAmelCase      )     ,	[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]     ,	)
 @slow
 def 	A_      ( self    : str      )     ->      List[str]:
     lowerCamelCase__       : int   					=						self.tokenizer_class.from_pretrained('roberta-base'      )

     lowerCamelCase__       : Union[str, Any]   					=						tokenizer.encode('sequence builders'     ,	add_special_tokens=UpperCAmelCase      )
     lowerCamelCase__       : Dict   					=						tokenizer.encode('multi-sequence build'     ,	add_special_tokens=UpperCAmelCase      )

     lowerCamelCase__       : Any   					=						tokenizer.encode(
         'sequence builders'     ,	add_special_tokens=UpperCAmelCase     ,	add_prefix_space=UpperCAmelCase      )
     lowerCamelCase__       : Tuple   					=						tokenizer.encode(
         'sequence builders'     ,	'multi-sequence build'     ,	add_special_tokens=UpperCAmelCase     ,	add_prefix_space=UpperCAmelCase      )

     lowerCamelCase__       : Optional[int]   					=						tokenizer.build_inputs_with_special_tokens(UpperCAmelCase      )
     lowerCamelCase__       : Tuple   					=						tokenizer.build_inputs_with_special_tokens(UpperCAmelCase     ,	UpperCAmelCase      )

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode
 def 	A_      ( self    : int      )     ->      str:
     lowerCamelCase__       : List[str]   					=						self.get_tokenizer()

     lowerCamelCase__       : Optional[int]   					=						'Encode this sequence.'
     lowerCamelCase__       : Tuple   					=						tokenizer.byte_encoder[' '.encode('utf-8'      )[0]]

     # Testing encoder arguments
     lowerCamelCase__       : Union[str, Any]   					=						tokenizer.encode(UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase     ,	add_prefix_space=UpperCAmelCase      )
     lowerCamelCase__       : Optional[Any]   					=						tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
     self.assertNotEqual(UpperCAmelCase     ,	UpperCAmelCase      )

     lowerCamelCase__       : Any   					=						tokenizer.encode(UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase     ,	add_prefix_space=UpperCAmelCase      )
     lowerCamelCase__       : str   					=						tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
     self.assertEqual(UpperCAmelCase     ,	UpperCAmelCase      )

     tokenizer.add_special_tokens({'bos_token': '<s>'}      )
     lowerCamelCase__       : Optional[Any]   					=						tokenizer.encode(UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
     self.assertNotEqual(UpperCAmelCase     ,	UpperCAmelCase      )

     # Testing spaces after special tokens
     lowerCamelCase__       : List[Any]   					=						'<mask>'
     tokenizer.add_special_tokens(
         {'mask_token': AddedToken(UpperCAmelCase     ,	lstrip=UpperCAmelCase     ,	rstrip=UpperCAmelCase      )}      )  # mask token has a left space
     lowerCamelCase__       : Optional[Any]   					=						tokenizer.convert_tokens_to_ids(UpperCAmelCase      )

     lowerCamelCase__       : Optional[int]   					=						'Encode <mask> sequence'
     lowerCamelCase__       : Optional[int]   					=						'Encode <mask>sequence'

     lowerCamelCase__       : List[str]   					=						tokenizer.encode(UpperCAmelCase      )
     lowerCamelCase__       : Optional[int]   					=						encoded.index(UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
     self.assertEqual(UpperCAmelCase     ,	UpperCAmelCase      )

     lowerCamelCase__       : Any   					=						tokenizer.encode(UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						encoded.index(UpperCAmelCase      )
     lowerCamelCase__       : Optional[Any]   					=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
     self.assertNotEqual(UpperCAmelCase     ,	UpperCAmelCase      )
 def 	A_      ( self    : Any      )     ->      List[Any]:
     pass
 def 	A_      ( self    : Optional[Any]      )     ->      Dict:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
             lowerCamelCase__       : Optional[int]   					=						self.rust_tokenizer_class.from_pretrained(UpperCAmelCase     ,	**UpperCAmelCase      )
             lowerCamelCase__       : Dict   					=						self.tokenizer_class.from_pretrained(UpperCAmelCase     ,	**UpperCAmelCase      )
             lowerCamelCase__       : Tuple   					=						'A, <mask> AllenNLP sentence.'
             lowerCamelCase__       : Dict   					=						tokenizer_r.encode_plus(UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase     ,	return_token_type_ids=UpperCAmelCase      )
             lowerCamelCase__       : Optional[Any]   					=						tokenizer_p.encode_plus(UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase     ,	return_token_type_ids=UpperCAmelCase      )

             # token_type_ids should put 0 everywhere
             self.assertEqual(sum(tokens_r['token_type_ids']      )     ,	sum(tokens_p['token_type_ids']      )      )

             # attention_mask should put 1 everywhere, so sum over length should be 1
             self.assertEqual(
                 sum(tokens_r['attention_mask']      ) / len(tokens_r['attention_mask']      )     ,	sum(tokens_p['attention_mask']      ) / len(tokens_p['attention_mask']      )     ,	)

             lowerCamelCase__       : Union[str, Any]   					=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
             lowerCamelCase__       : Optional[int]   					=						tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )

             # Rust correctly handles the space before the mask while python doesnt
             self.assertSequenceEqual(tokens_p['input_ids']     ,	[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]      )
             self.assertSequenceEqual(tokens_r['input_ids']     ,	[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]      )

             self.assertSequenceEqual(
                 UpperCAmelCase     ,	['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
             self.assertSequenceEqual(
                 UpperCAmelCase     ,	['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
 def 	A_      ( self    : Any      )     ->      int:
     for trim_offsets, add_prefix_space in itertools.product([True, False]     ,	repeat=2      ):
         lowerCamelCase__       : Tuple   					=						self.rust_tokenizer_class.from_pretrained(
             self.tmpdirname     ,	use_fast=UpperCAmelCase     ,	add_prefix_space=UpperCAmelCase     ,	trim_offsets=UpperCAmelCase      )

         lowerCamelCase__       : Tuple   					=						json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
         lowerCamelCase__       : List[Any]   					=						json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()      )

         self.assertEqual(pre_tokenizer_state['add_prefix_space']     ,	UpperCAmelCase      )

         self.assertEqual(post_processor_state['add_prefix_space']     ,	UpperCAmelCase      )
         self.assertEqual(post_processor_state['trim_offsets']     ,	UpperCAmelCase      )







 def 	A_      ( self    : Optional[Any]      )     ->      Optional[Any]:
     # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
     # `trim_offsets`
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
             lowerCamelCase__       : List[Any]   					=						'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
             lowerCamelCase__       : int   					=						F"""{text_of_1_token} {text_of_1_token}"""

             lowerCamelCase__       : Optional[int]   					=						self.rust_tokenizer_class.from_pretrained(
                 UpperCAmelCase     ,	use_fast=UpperCAmelCase     ,	add_prefix_space=UpperCAmelCase     ,	trim_offsets=UpperCAmelCase      )
             lowerCamelCase__       : Tuple   					=						tokenizer_r(UpperCAmelCase     ,	return_offsets_mapping=UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase      )
             self.assertEqual(encoding.offset_mapping[0]     ,	(0, len(UpperCAmelCase      ))      )
             self.assertEqual(
                 encoding.offset_mapping[1]     ,	(len(UpperCAmelCase      ) + 1, len(UpperCAmelCase      ) + 1 + len(UpperCAmelCase      ))     ,	)

             lowerCamelCase__       : str   					=						self.rust_tokenizer_class.from_pretrained(
                 UpperCAmelCase     ,	use_fast=UpperCAmelCase     ,	add_prefix_space=UpperCAmelCase     ,	trim_offsets=UpperCAmelCase      )
             lowerCamelCase__       : Tuple   					=						tokenizer_r(UpperCAmelCase     ,	return_offsets_mapping=UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase      )
             self.assertEqual(encoding.offset_mapping[0]     ,	(0, len(UpperCAmelCase      ))      )
             self.assertEqual(
                 encoding.offset_mapping[1]     ,	(len(UpperCAmelCase      ) + 1, len(UpperCAmelCase      ) + 1 + len(UpperCAmelCase      ))     ,	)

             lowerCamelCase__       : str   					=						self.rust_tokenizer_class.from_pretrained(
                 UpperCAmelCase     ,	use_fast=UpperCAmelCase     ,	add_prefix_space=UpperCAmelCase     ,	trim_offsets=UpperCAmelCase      )
             lowerCamelCase__       : str   					=						tokenizer_r(UpperCAmelCase     ,	return_offsets_mapping=UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase      )
             self.assertEqual(encoding.offset_mapping[0]     ,	(0, len(UpperCAmelCase      ))      )
             self.assertEqual(
                 encoding.offset_mapping[1]     ,	(len(UpperCAmelCase      ), len(UpperCAmelCase      ) + 1 + len(UpperCAmelCase      ))     ,	)

             lowerCamelCase__       : Tuple   					=						self.rust_tokenizer_class.from_pretrained(
                 UpperCAmelCase     ,	use_fast=UpperCAmelCase     ,	add_prefix_space=UpperCAmelCase     ,	trim_offsets=UpperCAmelCase      )
             lowerCamelCase__       : Union[str, Any]   					=						tokenizer_r(UpperCAmelCase     ,	return_offsets_mapping=UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase      )
             self.assertEqual(encoding.offset_mapping[0]     ,	(0, len(UpperCAmelCase      ))      )
             self.assertEqual(
                 encoding.offset_mapping[1]     ,	(len(UpperCAmelCase      ), len(UpperCAmelCase      ) + 1 + len(UpperCAmelCase      ))     ,	)

             lowerCamelCase__       : Optional[int]   					=						F""" {text}"""

             # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
             #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
             # )
             # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
             # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
             # self.assertEqual(
             #     encoding.offset_mapping[1],
             #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
             # )

             lowerCamelCase__       : Optional[int]   					=						self.rust_tokenizer_class.from_pretrained(
                 UpperCAmelCase     ,	use_fast=UpperCAmelCase     ,	add_prefix_space=UpperCAmelCase     ,	trim_offsets=UpperCAmelCase      )
             lowerCamelCase__       : int   					=						tokenizer_r(UpperCAmelCase     ,	return_offsets_mapping=UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase      )
             self.assertEqual(encoding.offset_mapping[0]     ,	(1, 1 + len(UpperCAmelCase      ))      )
             self.assertEqual(
                 encoding.offset_mapping[1]     ,	(1 + len(UpperCAmelCase      ) + 1, 1 + len(UpperCAmelCase      ) + 1 + len(UpperCAmelCase      ))     ,	)

             lowerCamelCase__       : int   					=						self.rust_tokenizer_class.from_pretrained(
                 UpperCAmelCase     ,	use_fast=UpperCAmelCase     ,	add_prefix_space=UpperCAmelCase     ,	trim_offsets=UpperCAmelCase      )
             lowerCamelCase__       : int   					=						tokenizer_r(UpperCAmelCase     ,	return_offsets_mapping=UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase      )
             self.assertEqual(encoding.offset_mapping[0]     ,	(0, 1 + len(UpperCAmelCase      ))      )
             self.assertEqual(
                 encoding.offset_mapping[1]     ,	(1 + len(UpperCAmelCase      ), 1 + len(UpperCAmelCase      ) + 1 + len(UpperCAmelCase      ))     ,	)

             lowerCamelCase__       : Tuple   					=						self.rust_tokenizer_class.from_pretrained(
                 UpperCAmelCase     ,	use_fast=UpperCAmelCase     ,	add_prefix_space=UpperCAmelCase     ,	trim_offsets=UpperCAmelCase      )
             lowerCamelCase__       : List[str]   					=						tokenizer_r(UpperCAmelCase     ,	return_offsets_mapping=UpperCAmelCase     ,	add_special_tokens=UpperCAmelCase      )
             self.assertEqual(encoding.offset_mapping[0]     ,	(0, 1 + len(UpperCAmelCase      ))      )
             self.assertEqual(
                 encoding.offset_mapping[1]     ,	(1 + len(UpperCAmelCase      ), 1 + len(UpperCAmelCase      ) + 1 + len(UpperCAmelCase      ))     ,	)












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   lowerCAmelCase				(   unittest.TestCase     ):
 def 	A_      ( self    : Any      )     ->      int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
 def 	A_      ( self    : Optional[Any]      )     ->      Optional[int]:
     lowerCamelCase__					,				lowerCamelCase__       : str   					=						FlaxControlNetModel.from_pretrained(
         'lllyasviel/sd-controlnet-canny'     ,	from_pt=UpperCAmelCase     ,	dtype=jnp.bfloataa      )
     lowerCamelCase__					,				lowerCamelCase__       : int   					=						FlaxStableDiffusionControlNetPipeline.from_pretrained(
         'runwayml/stable-diffusion-v1-5'     ,	controlnet=UpperCAmelCase     ,	from_pt=UpperCAmelCase     ,	dtype=jnp.bfloataa      )
     lowerCamelCase__       : int   					=						controlnet_params

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

     lowerCamelCase__       : Optional[Any]   					=						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__       : List[str]   					=						jax.random.PRNGKey(0      )
     lowerCamelCase__       : Optional[int]   					=						jax.random.split(UpperCAmelCase     ,	jax.device_count()      )

     lowerCamelCase__       : List[Any]   					=						replicate(UpperCAmelCase      )
     lowerCamelCase__       : Dict   					=						shard(UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						shard(UpperCAmelCase      )

     lowerCamelCase__       : Any   					=						pipe(
         prompt_ids=UpperCAmelCase     ,	image=UpperCAmelCase     ,	params=UpperCAmelCase     ,	prng_seed=UpperCAmelCase     ,	num_inference_steps=50     ,	jit=UpperCAmelCase     ,	).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__       : Tuple   					=						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
     lowerCamelCase__       : Tuple   					=						jnp.array(
         [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]      )
     print(F"""output_slice: {output_slice}"""      )
     assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2







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

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

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

     lowerCamelCase__       : Tuple   					=						jax.random.PRNGKey(0      )
     lowerCamelCase__       : List[str]   					=						jax.random.split(UpperCAmelCase     ,	jax.device_count()      )

     lowerCamelCase__       : Dict   					=						replicate(UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						shard(UpperCAmelCase      )
     lowerCamelCase__       : Tuple   					=						shard(UpperCAmelCase      )

     lowerCamelCase__       : Union[str, Any]   					=						pipe(
         prompt_ids=UpperCAmelCase     ,	image=UpperCAmelCase     ,	params=UpperCAmelCase     ,	prng_seed=UpperCAmelCase     ,	num_inference_steps=50     ,	jit=UpperCAmelCase     ,	).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__       : int   					=						images[0, 253:256, 253:256, -1]

     lowerCamelCase__       : Optional[int]   					=						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
     lowerCamelCase__       : int   					=						jnp.array(
         [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]      )
     print(F"""output_slice: {output_slice}"""      )
     assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2





