





"""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       __a  (    unittest.TestCase						):



     def 		snake_case_     (			self  ):
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()



     def 		snake_case_     (			self  ):
            _lowerCamelCase      ,      _lowerCamelCase             =   FlaxControlNetModel.from_pretrained(
                'lllyasviel/sd-controlnet-canny'   ,	from_pt=A_   ,	dtype=jnp.bfloataa  )
            _lowerCamelCase      ,      _lowerCamelCase             =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
                'runwayml/stable-diffusion-v1-5'   ,	controlnet=A_   ,	from_pt=A_   ,	dtype=jnp.bfloataa  )
            _lowerCamelCase             =   controlnet_params

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

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

            _lowerCamelCase             =   jax.random.PRNGKey(0  )
            _lowerCamelCase             =   jax.random.split(A_   ,	jax.device_count()  )

            _lowerCamelCase             =   replicate(A_  )
            _lowerCamelCase             =   shard(A_  )
            _lowerCamelCase             =   shard(A_  )

            _lowerCamelCase             =   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, 7_68, 5_12, 3)

            _lowerCamelCase             =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
            _lowerCamelCase             =   images[0, 2_53:2_56, 2_53:2_56, -1]

            _lowerCamelCase             =   jnp.asarray(jax.device_get(image_slice.flatten()  )  )
            _lowerCamelCase             =   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  ):
            _lowerCamelCase      ,      _lowerCamelCase             =   FlaxControlNetModel.from_pretrained(
                'lllyasviel/sd-controlnet-openpose'   ,	from_pt=A_   ,	dtype=jnp.bfloataa  )
            _lowerCamelCase      ,      _lowerCamelCase             =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
                'runwayml/stable-diffusion-v1-5'   ,	controlnet=A_   ,	from_pt=A_   ,	dtype=jnp.bfloataa  )
            _lowerCamelCase             =   controlnet_params

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

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

            _lowerCamelCase             =   jax.random.PRNGKey(0  )
            _lowerCamelCase             =   jax.random.split(A_   ,	jax.device_count()  )

            _lowerCamelCase             =   replicate(A_  )
            _lowerCamelCase             =   shard(A_  )
            _lowerCamelCase             =   shard(A_  )

            _lowerCamelCase             =   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, 7_68, 5_12, 3)

            _lowerCamelCase             =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
            _lowerCamelCase             =   images[0, 2_53:2_56, 2_53:2_56, -1]

            _lowerCamelCase             =   jnp.asarray(jax.device_get(image_slice.flatten()  )  )
            _lowerCamelCase             =   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













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     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      ['image_processor', 'tokenizer']
 __UpperCamelCase       =      'BlipImageProcessor'
 __UpperCamelCase       =      'AutoTokenizer'

 def __init__(  self,		A_,		A_,		A_		)	->       str:
    super().__init__(A_,		A_		)

    # add QFormer tokenizer
    UpperCAmelCase__						=qformer_tokenizer

 def __call__(  self,		A_ = None,		A_ = None,		A_ = True,		A_ = False,		A_ = None,		A_ = None,		A_ = 0,		A_ = None,		A_ = None,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = True,		A_ = None,		**A_,		)	->       BatchFeature:
    if images is None and text is None:
       raise ValueError("You have to specify at least images or text."		)

    UpperCAmelCase__						=BatchFeature()

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       encoding.update(A_		)
       UpperCAmelCase__						=self.qformer_tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       UpperCAmelCase__						=qformer_text_encoding.pop("input_ids"		)
       UpperCAmelCase__						=qformer_text_encoding.pop("attention_mask"		)

    if images is not None:
       UpperCAmelCase__						=self.image_processor(A_,		return_tensors=A_		)
       encoding.update(A_		)

    return encoding

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       List[Any]:
    return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)

 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.tokenizer.model_input_names
    UpperCAmelCase__						=self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)

 def  __UpperCAmelCase						(  self,		A_,		**A_		)	->       str:
    if os.path.isfile(A_		):
       raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
    os.makedirs(A_,		exist_ok=A_		)
    UpperCAmelCase__						=os.path.join(A_,		"qformer_tokenizer"		)
    self.qformer_tokenizer.save_pretrained(A_		)
    return super().save_pretrained(A_,		**A_		)






 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		**A_		)	->       int:
    UpperCAmelCase__						=AutoTokenizer.from_pretrained(A_,		subfolder="qformer_tokenizer"		)
    UpperCAmelCase__						=cls._get_arguments_from_pretrained(A_,		**A_		)
    args.append(A_		)
    return cls(*A_		)



