





import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase			       =      get_tests_dir('fixtures/test_sentencepiece.model')
_lowerCamelCase			       =      get_tests_dir('fixtures/test_sentencepiece_bpe.model')

_lowerCamelCase			       =      """pt""" if is_torch_available() else """tf"""







@require_sentencepiece
@require_tokenizers
class 			__A       (      lowerCamelCase__ ,unittest.TestCase  ):




						"""simple docstring"""







						UpperCAmelCase__						=      CamembertTokenizer
						UpperCAmelCase__						=      CamembertTokenizerFast
						UpperCAmelCase__						=      True
						UpperCAmelCase__						=      True


						def 				__snake_case			(		self):



													"""simple docstring"""





													super().setUp()

													# We have a SentencePiece fixture for testing
													_lowerCamelCase							:     Optional[int]	=		CamembertTokenizer(a__)
													tokenizer.save_pretrained(self.tmpdirname)


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[Any]	=		'''<pad>'''
													_lowerCamelCase							:     int	=		1

													self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__)      ,							a__)
													self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__)      ,							a__)


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     int	=		list(self.get_tokenizer().get_vocab().keys())

													self.assertEqual(vocab_keys[0]      ,							'''<s>NOTUSED''')
													self.assertEqual(vocab_keys[1]      ,							'''<pad>''')
													self.assertEqual(vocab_keys[-1]      ,							'''<mask>''')
													self.assertEqual(len(a__)      ,							1004)


						def 				__snake_case			(		self):



													"""simple docstring"""





													self.assertEqual(self.get_tokenizer().vocab_size      ,							1005)


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[int]	=		CamembertTokenizer(a__)
													tokenizer.save_pretrained(self.tmpdirname)
													_lowerCamelCase							:     int	=		CamembertTokenizerFast.from_pretrained(self.tmpdirname)

													_lowerCamelCase							:     Tuple	=		'''I was born in 92000, and this is falsé.'''

													_lowerCamelCase							:     List[str]	=		tokenizer.encode(a__)
													_lowerCamelCase							:     int	=		rust_tokenizer.encode(a__)
													self.assertListEqual(a__      ,							a__)

													_lowerCamelCase							:     List[str]	=		tokenizer.encode(a__      ,							add_special_tokens=a__)
													_lowerCamelCase							:     str	=		rust_tokenizer.encode(a__      ,							add_special_tokens=a__)
													self.assertListEqual(a__      ,							a__)

													# <unk> tokens are not the same for `rust` than for `slow`.
													# Because spm gives back raw token instead of `unk` in EncodeAsPieces
													# tokens = tokenizer.tokenize(sequence)
													_lowerCamelCase							:     List[Any]	=		tokenizer.convert_ids_to_tokens(a__)
													_lowerCamelCase							:     List[str]	=		rust_tokenizer.tokenize(a__)
													self.assertListEqual(a__      ,							a__)


						def 				__snake_case			(		self):



													"""simple docstring"""





													if not self.test_rust_tokenizer:
																				return

													_lowerCamelCase							:     Tuple	=		self.get_tokenizer()
													_lowerCamelCase							:     Optional[Any]	=		self.get_rust_tokenizer()

													_lowerCamelCase							:     str	=		'''I was born in 92000, and this is falsé.'''

													_lowerCamelCase							:     str	=		tokenizer.tokenize(a__)
													_lowerCamelCase							:     List[str]	=		rust_tokenizer.tokenize(a__)
													self.assertListEqual(a__      ,							a__)

													_lowerCamelCase							:     List[Any]	=		tokenizer.encode(a__      ,							add_special_tokens=a__)
													_lowerCamelCase							:     int	=		rust_tokenizer.encode(a__      ,							add_special_tokens=a__)
													self.assertListEqual(a__      ,							a__)

													_lowerCamelCase							:     Any	=		self.get_rust_tokenizer()
													_lowerCamelCase							:     Any	=		tokenizer.encode(a__)
													_lowerCamelCase							:     str	=		rust_tokenizer.encode(a__)
													self.assertListEqual(a__      ,							a__)



						@slow
						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[Any]	=		{'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
													# fmt: on

													# camembert is a french model. So we also use french texts.
													_lowerCamelCase							:     str	=		[
													    '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
													    '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
													    '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
													    '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
													    '''telles que la traduction et la synthèse de texte.''',
													]

													self.tokenizer_integration_test_util(
													    expected_encoding=a__      ,							model_name='''camembert-base'''      ,							revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''      ,							sequences=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

