





'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





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

            '''simple docstring'''




            _UpperCamelCase       =	tempfile.mkdtemp()

            # fmt: off
            _UpperCamelCase       =	['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
            # fmt: on
            _UpperCamelCase       =	dict(zip(A__	,			range(len(A__					)					)					)					)
            _UpperCamelCase       =	['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
            _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(A__					) + '''\n'''					)
            with open(self.merges_file	,			'''w'''	,			encoding='''utf-8'''					) as fp:
                 fp.write('''\n'''.join(A__					)					)

            _UpperCamelCase       =	{
                '''do_resize''': True,
                '''size''': 20,
                '''do_center_crop''': True,
                '''crop_size''': 18,
                '''do_normalize''': True,
                '''image_mean''': [0.48145466, 0.4578275, 0.40821073],
                '''image_std''': [0.26862954, 0.26130258, 0.27577711],
            }
            _UpperCamelCase       =	os.path.join(self.tmpdirname	,			A__					)
            with open(self.image_processor_file	,			'''w'''	,			encoding='''utf-8'''					) as fp:
                 json.dump(A__	,			A__					)





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

            '''simple docstring'''




            return CLIPTokenizer.from_pretrained(self.tmpdirname	,			**A__					)





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

            '''simple docstring'''




            return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,			**A__					)





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

            '''simple docstring'''




            return ViTImageProcessor.from_pretrained(self.tmpdirname	,			**A__					)





       def snake_case__						(							self       :		int					)   ->					Dict:

            '''simple docstring'''




            shutil.rmtree(self.tmpdirname					)





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

            '''simple docstring'''




            _UpperCamelCase       =	[np.random.randint(255	,			size=(3, 30, 400)	,			dtype=np.uinta					)]

            _UpperCamelCase       =	[Image.fromarray(np.moveaxis(A__	,			0	,			-1					)					) for x in image_inputs]

            return image_inputs





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

            '''simple docstring'''




            _UpperCamelCase       =	self.get_tokenizer()
            _UpperCamelCase       =	self.get_rust_tokenizer()
            _UpperCamelCase       =	self.get_image_processor()

            _UpperCamelCase       =	CLIPSegProcessor(tokenizer=A__	,			image_processor=A__					)
            processor_slow.save_pretrained(self.tmpdirname					)
            _UpperCamelCase       =	CLIPSegProcessor.from_pretrained(self.tmpdirname	,			use_fast=A__					)

            _UpperCamelCase       =	CLIPSegProcessor(tokenizer=A__	,			image_processor=A__					)
            processor_fast.save_pretrained(self.tmpdirname					)
            _UpperCamelCase       =	CLIPSegProcessor.from_pretrained(self.tmpdirname					)

            self.assertEqual(processor_slow.tokenizer.get_vocab()	,			tokenizer_slow.get_vocab()					)
            self.assertEqual(processor_fast.tokenizer.get_vocab()	,			tokenizer_fast.get_vocab()					)
            self.assertEqual(tokenizer_slow.get_vocab()	,			tokenizer_fast.get_vocab()					)
            self.assertIsInstance(processor_slow.tokenizer	,			A__					)
            self.assertIsInstance(processor_fast.tokenizer	,			A__					)

            self.assertEqual(processor_slow.image_processor.to_json_string()	,			image_processor.to_json_string()					)
            self.assertEqual(processor_fast.image_processor.to_json_string()	,			image_processor.to_json_string()					)
            self.assertIsInstance(processor_slow.image_processor	,			A__					)
            self.assertIsInstance(processor_fast.image_processor	,			A__					)





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

            '''simple docstring'''




            _UpperCamelCase       =	CLIPSegProcessor(tokenizer=self.get_tokenizer()	,			image_processor=self.get_image_processor()					)
            processor.save_pretrained(self.tmpdirname					)

            _UpperCamelCase       =	self.get_tokenizer(bos_token='''(BOS)'''	,			eos_token='''(EOS)'''					)
            _UpperCamelCase       =	self.get_image_processor(do_normalize=A__	,			padding_value=1.0					)

            _UpperCamelCase       =	CLIPSegProcessor.from_pretrained(
                self.tmpdirname	,			bos_token='''(BOS)'''	,			eos_token='''(EOS)'''	,			do_normalize=A__	,			padding_value=1.0					)

            self.assertEqual(processor.tokenizer.get_vocab()	,			tokenizer_add_kwargs.get_vocab()					)
            self.assertIsInstance(processor.tokenizer	,			A__					)

            self.assertEqual(processor.image_processor.to_json_string()	,			image_processor_add_kwargs.to_json_string()					)
            self.assertIsInstance(processor.image_processor	,			A__					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.get_image_processor()
            _UpperCamelCase       =	self.get_tokenizer()

            _UpperCamelCase       =	CLIPSegProcessor(tokenizer=A__	,			image_processor=A__					)

            _UpperCamelCase       =	self.prepare_image_inputs()

            _UpperCamelCase       =	image_processor(A__	,			return_tensors='''np'''					)
            _UpperCamelCase       =	processor(images=A__	,			return_tensors='''np'''					)

            for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()	,			input_processor[key].sum()	,			delta=1e-2					)





       def snake_case__						(							self       :		str					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	self.get_image_processor()
            _UpperCamelCase       =	self.get_tokenizer()

            _UpperCamelCase       =	CLIPSegProcessor(tokenizer=A__	,			image_processor=A__					)

            _UpperCamelCase       =	'''lower newer'''

            _UpperCamelCase       =	processor(text=A__					)

            _UpperCamelCase       =	tokenizer(A__					)

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,			encoded_processor[key]					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.get_image_processor()
            _UpperCamelCase       =	self.get_tokenizer()

            _UpperCamelCase       =	CLIPSegProcessor(tokenizer=A__	,			image_processor=A__					)

            _UpperCamelCase       =	'''lower newer'''
            _UpperCamelCase       =	self.prepare_image_inputs()

            _UpperCamelCase       =	processor(text=A__	,			images=A__					)

            self.assertListEqual(list(inputs.keys()					)	,			['''input_ids''', '''attention_mask''', '''pixel_values''']					)

            # test if it raises when no input is passed
            with pytest.raises(A__					):
                 processor()





       def snake_case__						(							self       :		int					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	self.get_image_processor()
            _UpperCamelCase       =	self.get_tokenizer()

            _UpperCamelCase       =	CLIPSegProcessor(tokenizer=A__	,			image_processor=A__					)

            _UpperCamelCase       =	self.prepare_image_inputs()
            _UpperCamelCase       =	self.prepare_image_inputs()

            _UpperCamelCase       =	processor(images=A__	,			visual_prompt=A__					)

            self.assertListEqual(list(inputs.keys()					)	,			['''pixel_values''', '''conditional_pixel_values''']					)

            # test if it raises when no input is passed
            with pytest.raises(A__					):
                 processor()







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

            '''simple docstring'''




            _UpperCamelCase       =	self.get_image_processor()
            _UpperCamelCase       =	self.get_tokenizer()

            _UpperCamelCase       =	CLIPSegProcessor(tokenizer=A__	,			image_processor=A__					)

            _UpperCamelCase       =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _UpperCamelCase       =	processor.batch_decode(A__					)
            _UpperCamelCase       =	tokenizer.batch_decode(A__					)

            self.assertListEqual(A__	,			A__					)













'''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



