





'''simple docstring'''

import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Optional[Any]						    =   DebertaTokenizer
       _snake_case     :					Union[str, Any]						    =   True
       _snake_case     :					List[str]						    =   DebertaTokenizerFast





       def snake_case__						(							self       :		List[str]					)   ->					Optional[Any]:

            '''simple docstring'''




            super().setUp()

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

            _UpperCamelCase       =	os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''vocab_file''']					)
            _UpperCamelCase       =	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(lowerCAmelCase__					) + '''\n'''					)
            with open(self.merges_file	,			'''w'''	,			encoding='''utf-8'''					) as fp:
                 fp.write('''\n'''.join(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[int]	,			**lowerCAmelCase__       :		List[Any]					)   ->					Tuple:

            '''simple docstring'''




            kwargs.update(self.special_tokens_map					)
            return self.tokenizer_class.from_pretrained(self.tmpdirname	,			**lowerCAmelCase__					)





       def snake_case__						(							self       :		str	,			lowerCAmelCase__       :		str					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	'''lower newer'''
            _UpperCamelCase       =	'''lower newer'''
            return input_text, output_text





       def snake_case__						(							self       :		str					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.get_tokenizer()
            _UpperCamelCase       =	'''lower newer'''
            _UpperCamelCase       =	['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
            _UpperCamelCase       =	tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokens + [tokenizer.unk_token]
            _UpperCamelCase       =	[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__					)	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		int					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	self.get_tokenizer()
            _UpperCamelCase       =	tokenizer('''Hello'''	,			'''World'''					)
            _UpperCamelCase       =	[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
            self.assertListEqual(tokd['''token_type_ids''']	,			lowerCAmelCase__					)





       @slow
       def snake_case__						(							self       :		List[Any]					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''					)

            _UpperCamelCase       =	tokenizer.encode('''sequence builders'''	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	tokenizer.encode('''multi-sequence build'''	,			add_special_tokens=lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(
                '''sequence builders'''	,			add_special_tokens=lowerCAmelCase__	,			add_prefix_space=lowerCAmelCase__					)
            _UpperCamelCase       =	tokenizer.encode(
                '''sequence builders'''	,			'''multi-sequence build'''	,			add_special_tokens=lowerCAmelCase__	,			add_prefix_space=lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__					)
            _UpperCamelCase       =	tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__	,			lowerCAmelCase__					)

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode







       @slow
       def snake_case__						(							self       :		int					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[self.tokenizer_class]
            if self.test_rust_tokenizer:
                 tokenizer_classes.append(self.rust_tokenizer_class					)

            for tokenizer_class in tokenizer_classes:
                 _UpperCamelCase       =	tokenizer_class.from_pretrained('''microsoft/deberta-base'''					)

                 _UpperCamelCase       =	[
                     '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                     '''ALBERT incorporates two parameter reduction techniques''',
                     '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                     ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                     ''' vocabulary embedding.''',
                 ]

                 _UpperCamelCase       =	tokenizer(lowerCAmelCase__	,			padding=lowerCAmelCase__					)
                 _UpperCamelCase       =	[tokenizer.decode(lowerCAmelCase__	,			skip_special_tokens=lowerCAmelCase__					) for seq in encoding['''input_ids''']]

                 # fmt: off
                 _UpperCamelCase       =	{
                     '''input_ids''': [
                         [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
                     ],
                     '''token_type_ids''': [
                         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                     ],
                     '''attention_mask''': [
                         [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, 0, 0, 0],
                         [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, 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]
                     ]
                 }
                 # fmt: on

                 _UpperCamelCase       =	[
                     '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                     '''ALBERT incorporates two parameter reduction techniques''',
                     '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                     ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                     ''' vocabulary embedding.''',
                 ]

                 self.assertDictEqual(encoding.data	,			lowerCAmelCase__					)

                 for expected, decoded in zip(lowerCAmelCase__	,			lowerCAmelCase__					):
                      self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)













'''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     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		List[str]					)   ->					Optional[int]:

            '''simple docstring'''




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





       def snake_case__						(							self       :		int					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxControlNetModel.from_pretrained(
                '''lllyasviel/sd-controlnet-canny'''	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''	,			controlnet=lowerCAmelCase__	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase       =	controlnet_params

            _UpperCamelCase       =	'''bird'''
            _UpperCamelCase       =	jax.device_count()
            _UpperCamelCase       =	pipe.prepare_text_inputs([prompts] * num_samples					)

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

            _UpperCamelCase       =	jax.random.PRNGKey(0					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	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)

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

            _UpperCamelCase       =	jnp.asarray(jax.device_get(image_slice.flatten()					)					)
            _UpperCamelCase       =	jnp.array(
                [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]					)
            print(f"""output_slice: {output_slice}"""					)
            assert jnp.abs(output_slice - expected_slice					).max() < 1e-2







       def snake_case__						(							self       :		Union[str, Any]					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxControlNetModel.from_pretrained(
                '''lllyasviel/sd-controlnet-openpose'''	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase   ,    _UpperCamelCase       =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''	,			controlnet=lowerCAmelCase__	,			from_pt=lowerCAmelCase__	,			dtype=jnp.bfloataa					)
            _UpperCamelCase       =	controlnet_params

            _UpperCamelCase       =	'''Chef in the kitchen'''
            _UpperCamelCase       =	jax.device_count()
            _UpperCamelCase       =	pipe.prepare_text_inputs([prompts] * num_samples					)

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

            _UpperCamelCase       =	jax.random.PRNGKey(0					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	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)

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

            _UpperCamelCase       =	jnp.asarray(jax.device_get(image_slice.flatten()					)					)
            _UpperCamelCase       =	jnp.array(
                [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]					)
            print(f"""output_slice: {output_slice}"""					)
            assert jnp.abs(output_slice - expected_slice					).max() < 1e-2



