"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Dict					)-> Optional[Any]:
								lowerCamelCase__		:		int		     =tempfile.mkdtemp()

								# fmt: off
								lowerCamelCase__		:		Optional[Any]		     =["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
								# fmt: on
								lowerCamelCase__		:		str		     =os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''vocab_file''']					)
								with open(self.vocab_file,  '''w''',  encoding='''utf-8'''					) as vocab_writer:
											vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]					)					)

								lowerCamelCase__		:		str		     ={
								    "do_resize": True,
								    "size": {"height": 18, "width": 18},
								    "do_normalize": True,
								    "image_mean": [0.5, 0.5, 0.5],
								    "image_std": [0.5, 0.5, 0.5],
								}
								lowerCamelCase__		:		Tuple		     =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							:  int,  **lowerCamelCase							:  List[str]					)-> Tuple:
								return BertTokenizer.from_pretrained(self.tmpdirname,  **_a					)



					def 		snake_case	(	self							:  Tuple,  **lowerCamelCase							:  str					)-> int:
								return ViTImageProcessor.from_pretrained(self.tmpdirname,  **_a					)



					def 		snake_case	(	self							:  Optional[int]					)-> Any:
								shutil.rmtree(self.tmpdirname					)



					def 		snake_case	(	self							:  Tuple					)-> int:
								lowerCamelCase__		:		int		     =[np.random.randint(255,  size=(3, 30, 400),  dtype=np.uinta					)]

								lowerCamelCase__		:		Tuple		     =[Image.fromarray(np.moveaxis(_a,  0,  -1					)					) for x in image_inputs]

								return image_inputs



					def 		snake_case	(	self							:  int					)-> List[Any]:
								lowerCamelCase__		:		int		     =self.get_tokenizer()
								lowerCamelCase__		:		List[Any]		     =self.get_image_processor()

								lowerCamelCase__		:		List[Any]		     =VisionTextDualEncoderProcessor(tokenizer=_a,  image_processor=_a					)

								processor.save_pretrained(self.tmpdirname					)
								lowerCamelCase__		:		Tuple		     =VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname					)

								self.assertEqual(processor.tokenizer.get_vocab(),  tokenizer.get_vocab()					)
								self.assertIsInstance(processor.tokenizer,  (BertTokenizer, BertTokenizerFast)					)

								self.assertEqual(processor.image_processor.to_json_string(),  image_processor.to_json_string()					)
								self.assertIsInstance(processor.image_processor,  _a					)



					def 		snake_case	(	self							:  int					)-> List[str]:
								lowerCamelCase__		:		Union[str, Any]		     =VisionTextDualEncoderProcessor(
								    tokenizer=self.get_tokenizer(),  image_processor=self.get_image_processor()					)
								processor.save_pretrained(self.tmpdirname					)

								lowerCamelCase__		:		str		     =self.get_tokenizer(bos_token='''(BOS)''',  eos_token='''(EOS)'''					)
								lowerCamelCase__		:		Any		     =self.get_image_processor(do_normalize=_a,  padding_value=1.0					)

								lowerCamelCase__		:		List[str]		     =VisionTextDualEncoderProcessor.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,  (BertTokenizer, BertTokenizerFast)					)

								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							:  Dict					)-> List[str]:
								lowerCamelCase__		:		int		     =self.get_image_processor()
								lowerCamelCase__		:		Union[str, Any]		     =self.get_tokenizer()

								lowerCamelCase__		:		List[Any]		     =VisionTextDualEncoderProcessor(tokenizer=_a,  image_processor=_a					)

								lowerCamelCase__		:		Dict		     =self.prepare_image_inputs()

								lowerCamelCase__		:		int		     =image_processor(_a,  return_tensors='''np'''					)
								lowerCamelCase__		:		Any		     =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							:  Any					)-> Any:
								lowerCamelCase__		:		Any		     =self.get_image_processor()
								lowerCamelCase__		:		Optional[Any]		     =self.get_tokenizer()

								lowerCamelCase__		:		Union[str, Any]		     =VisionTextDualEncoderProcessor(tokenizer=_a,  image_processor=_a					)

								lowerCamelCase__		:		int		     ="lower newer"

								lowerCamelCase__		:		Any		     =processor(text=_a					)

								lowerCamelCase__		:		List[str]		     =tokenizer(_a					)

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



					def 		snake_case	(	self							:  Any					)-> Dict:
								lowerCamelCase__		:		Optional[Any]		     =self.get_image_processor()
								lowerCamelCase__		:		int		     =self.get_tokenizer()

								lowerCamelCase__		:		Optional[Any]		     =VisionTextDualEncoderProcessor(tokenizer=_a,  image_processor=_a					)

								lowerCamelCase__		:		Any		     ="lower newer"
								lowerCamelCase__		:		Any		     =self.prepare_image_inputs()

								lowerCamelCase__		:		List[Any]		     =processor(text=_a,  images=_a					)

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

								# test if it raises when no input is passed
								with self.assertRaises(_a					):
											processor()



					def 		snake_case	(	self							:  Optional[int]					)-> Optional[Any]:
								lowerCamelCase__		:		Tuple		     =self.get_image_processor()
								lowerCamelCase__		:		Optional[Any]		     =self.get_tokenizer()

								lowerCamelCase__		:		Union[str, Any]		     =VisionTextDualEncoderProcessor(tokenizer=_a,  image_processor=_a					)

								lowerCamelCase__		:		List[str]		     =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								lowerCamelCase__		:		Tuple		     =processor.batch_decode(_a					)
								lowerCamelCase__		:		int		     =tokenizer.batch_decode(_a					)

								self.assertListEqual(_a,  _a					)




					def 		snake_case	(	self							:  Union[str, Any]					)-> List[Any]:
								lowerCamelCase__		:		Any		     =self.get_image_processor()
								lowerCamelCase__		:		Tuple		     =self.get_tokenizer()

								lowerCamelCase__		:		Any		     =VisionTextDualEncoderProcessor(tokenizer=_a,  image_processor=_a					)

								lowerCamelCase__		:		Dict		     ="lower newer"
								lowerCamelCase__		:		Union[str, Any]		     =self.prepare_image_inputs()

								lowerCamelCase__		:		Any		     =processor(text=_a,  images=_a					)

								self.assertListEqual(list(inputs.keys()					),  processor.model_input_names					)









"""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 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Union[str, Any]					)-> Dict:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[Any]		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[int]		     =controlnet_params

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

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

								lowerCamelCase__		:		Optional[int]		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Dict		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		Tuple		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		Optional[int]		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =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)

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

								lowerCamelCase__		:		Optional[Any]		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Dict		     =jnp.array(
								    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




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

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

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

								lowerCamelCase__		:		Tuple		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Optional[Any]		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		int		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		int		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =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)

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

								lowerCamelCase__		:		int		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Any		     =jnp.array(
								    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




