




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

							_lowerCamelCase     :			str    =    'bird'
							_lowerCamelCase     :			str    =    jax.device_count()
							_lowerCamelCase     :			List[str]    =    pipe.prepare_text_inputs([prompts] * num_samples  )

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

							_lowerCamelCase     :			List[Any]    =    jax.random.PRNGKey(0  )
							_lowerCamelCase     :			List[Any]    =    jax.random.split(lowercase  ,      jax.device_count()  )

							_lowerCamelCase     :			str    =    replicate(lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    shard(lowercase  )
							_lowerCamelCase     :			Dict    =    shard(lowercase  )

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

							_lowerCamelCase     :			List[str]    =    jnp.asarray(jax.device_get(image_slice.flatten()  )  )
							_lowerCamelCase     :			Dict    =    jnp.array(
							    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]  )
							print(F'''output_slice: {output_slice}'''  )
							assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2






			def 					A_    (  self  ):
							_lowerCamelCase, _lowerCamelCase     :			List[str]    =    FlaxControlNetModel.from_pretrained(
							    'lllyasviel/sd-controlnet-openpose'  ,      from_pt=lowercase  ,      dtype=jnp.bfloataa  )
							_lowerCamelCase, _lowerCamelCase     :			Any    =    FlaxStableDiffusionControlNetPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'  ,      controlnet=lowercase  ,      from_pt=lowercase  ,      dtype=jnp.bfloataa  )
							_lowerCamelCase     :			Any    =    controlnet_params

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

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

							_lowerCamelCase     :			List[str]    =    jax.random.PRNGKey(0  )
							_lowerCamelCase     :			Optional[Any]    =    jax.random.split(lowercase  ,      jax.device_count()  )

							_lowerCamelCase     :			Any    =    replicate(lowercase  )
							_lowerCamelCase     :			Any    =    shard(lowercase  )
							_lowerCamelCase     :			Optional[int]    =    shard(lowercase  )

							_lowerCamelCase     :			int    =    pipe(
							    prompt_ids=lowercase  ,      image=lowercase  ,      params=lowercase  ,      prng_seed=lowercase  ,      num_inference_steps=50  ,      jit=lowercase  ,      ).images
							assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

							_lowerCamelCase     :			Tuple    =    jnp.asarray(jax.device_get(image_slice.flatten()  )  )
							_lowerCamelCase     :			Tuple    =    jnp.array(
							    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]  )
							print(F'''output_slice: {output_slice}'''  )
							assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2





"""simple docstring"""






import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			["""image_processor""", """tokenizer"""]
			lowerCamelCase__      =			"""BlipImageProcessor"""
			lowerCamelCase__      =			"""AutoTokenizer"""
			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							super().__init__(lowercase  ,      lowercase  )

							# add QFormer tokenizer
							_lowerCamelCase     :			int    =    qformer_tokenizer
			def __call__(  self  ,      lowercase = None  ,      lowercase = None  ,      lowercase = True  ,      lowercase = False  ,      lowercase = None  ,      lowercase = None  ,      lowercase = 0  ,      lowercase = None  ,      lowercase = None  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = True  ,      lowercase = None  ,      **lowercase  ,      ):
							if images is None and text is None:
											raise ValueError('You have to specify at least images or text.'  )

							_lowerCamelCase     :			int    =    BatchFeature()

							if text is not None:
											_lowerCamelCase     :			List[str]    =    self.tokenizer(
											    text=lowercase  ,      add_special_tokens=lowercase  ,      padding=lowercase  ,      truncation=lowercase  ,      max_length=lowercase  ,      stride=lowercase  ,      pad_to_multiple_of=lowercase  ,      return_attention_mask=lowercase  ,      return_overflowing_tokens=lowercase  ,      return_special_tokens_mask=lowercase  ,      return_offsets_mapping=lowercase  ,      return_token_type_ids=lowercase  ,      return_length=lowercase  ,      verbose=lowercase  ,      return_tensors=lowercase  ,      **lowercase  ,      )
											encoding.update(lowercase  )
											_lowerCamelCase     :			List[str]    =    self.qformer_tokenizer(
											    text=lowercase  ,      add_special_tokens=lowercase  ,      padding=lowercase  ,      truncation=lowercase  ,      max_length=lowercase  ,      stride=lowercase  ,      pad_to_multiple_of=lowercase  ,      return_attention_mask=lowercase  ,      return_overflowing_tokens=lowercase  ,      return_special_tokens_mask=lowercase  ,      return_offsets_mapping=lowercase  ,      return_token_type_ids=lowercase  ,      return_length=lowercase  ,      verbose=lowercase  ,      return_tensors=lowercase  ,      **lowercase  ,      )
											_lowerCamelCase     :			List[Any]    =    qformer_text_encoding.pop('input_ids'  )
											_lowerCamelCase     :			Tuple    =    qformer_text_encoding.pop('attention_mask'  )

							if images is not None:
											_lowerCamelCase     :			int    =    self.image_processor(lowercase  ,      return_tensors=lowercase  )
											encoding.update(lowercase  )

							return encoding
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.batch_decode(*lowercase  ,      **lowercase  )
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.decode(*lowercase  ,      **lowercase  )
			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.tokenizer.model_input_names
							_lowerCamelCase     :			Any    =    self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
			def 					A_    (  self  ,      lowercase  ,      **lowercase  ):
							if os.path.isfile(lowercase  ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''  )
							os.makedirs(lowercase  ,      exist_ok=lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    os.path.join(lowercase  ,      'qformer_tokenizer'  )
							self.qformer_tokenizer.save_pretrained(lowercase  )
							return super().save_pretrained(lowercase  ,      **lowercase  )






			@classmethod
			def 					A_    (  cls  ,      lowercase  ,      **lowercase  ):
							_lowerCamelCase     :			Union[str, Any]    =    AutoTokenizer.from_pretrained(lowercase  ,      subfolder='qformer_tokenizer'  )
							_lowerCamelCase     :			Dict    =    cls._get_arguments_from_pretrained(lowercase  ,      **lowercase  )
							args.append(lowercase  )
							return cls(*lowercase  )