
'''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 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					Dict				       =							FlaxControlNetModel.from_pretrained(
       "lllyasviel/sd-controlnet-canny"			,				from_pt=lowercase__			,				dtype=jnp.bfloataa )
   UpperCAmelCase__       ,			UpperCAmelCase__      :					Optional[int]				       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
       "runwayml/stable-diffusion-v1-5"			,				controlnet=lowercase__			,				from_pt=lowercase__			,				dtype=jnp.bfloataa )
   UpperCAmelCase__      :					Dict				       =							controlnet_params

   UpperCAmelCase__      :					int				       =							"bird"
   UpperCAmelCase__      :					List[Any]				       =							jax.device_count()
   UpperCAmelCase__      :					int				       =							pipe.prepare_text_inputs([prompts] * num_samples )

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

   UpperCAmelCase__      :					str				       =							jax.random.PRNGKey(0 )
   UpperCAmelCase__      :					Optional[Any]				       =							jax.random.split(lowercase__			,				jax.device_count() )

   UpperCAmelCase__      :					Tuple				       =							replicate(lowercase__ )
   UpperCAmelCase__      :					Dict				       =							shard(lowercase__ )
   UpperCAmelCase__      :					Optional[int]				       =							shard(lowercase__ )

   UpperCAmelCase__      :					Dict				       =							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, 7_68, 5_12, 3)

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

   UpperCAmelCase__      :					List[str]				       =							jnp.asarray(jax.device_get(image_slice.flatten() ) )
   UpperCAmelCase__      :					Union[str, Any]				       =							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 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					Dict				       =							FlaxControlNetModel.from_pretrained(
       "lllyasviel/sd-controlnet-openpose"			,				from_pt=lowercase__			,				dtype=jnp.bfloataa )
   UpperCAmelCase__       ,			UpperCAmelCase__      :					Optional[int]				       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
       "runwayml/stable-diffusion-v1-5"			,				controlnet=lowercase__			,				from_pt=lowercase__			,				dtype=jnp.bfloataa )
   UpperCAmelCase__      :					int				       =							controlnet_params

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

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

   UpperCAmelCase__      :					Any				       =							jax.random.PRNGKey(0 )
   UpperCAmelCase__      :					Tuple				       =							jax.random.split(lowercase__			,				jax.device_count() )

   UpperCAmelCase__      :					List[Any]				       =							replicate(lowercase__ )
   UpperCAmelCase__      :					List[str]				       =							shard(lowercase__ )
   UpperCAmelCase__      :					List[Any]				       =							shard(lowercase__ )

   UpperCAmelCase__      :					Optional[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, 7_68, 5_12, 3)

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

   UpperCAmelCase__      :					Dict				       =							jnp.asarray(jax.device_get(image_slice.flatten() ) )
   UpperCAmelCase__      :					Optional[Any]				       =							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 warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    ["""image_processor""", """tokenizer"""]
       lowercase_						=    """LayoutLMv2ImageProcessor"""
       lowercase_						=    ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

       def __init__(							self ,   lowercase__=None ,   lowercase__=None ,   **lowercase__     ):







             '''simple docstring'''



             if "feature_extractor" in kwargs:
                   warnings.warn(
                       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                       ''' instead.''' ,   lowercase__ ,   )
                   __A    =kwargs.pop('''feature_extractor'''     )

             __A    =image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                   raise ValueError('''You need to specify an `image_processor`.'''     )
             if tokenizer is None:
                   raise ValueError('''You need to specify a `tokenizer`.'''     )

             super().__init__(lowercase__ ,   lowercase__     )

       def __call__(							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = True ,   lowercase__ = False ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = 0 ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = True ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             if self.image_processor.apply_ocr and (boxes is not None):
                   raise ValueError(
                       '''You cannot provide bounding boxes '''
                       '''if you initialized the image processor with apply_ocr set to True.'''     )

             if self.image_processor.apply_ocr and (word_labels is not None):
                   raise ValueError(
                       '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''     )

             if return_overflowing_tokens is True and return_offsets_mapping is False:
                   raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''     )

             # first, apply the image processor
             __A    =self.image_processor(images=lowercase__ ,   return_tensors=lowercase__     )

             # second, apply the tokenizer
             if text is not None and self.image_processor.apply_ocr and text_pair is None:
                   if isinstance(lowercase__ ,   lowercase__     ):
                         __A    =[text]  # add batch dimension (as the image processor always adds a batch dimension)
                   __A    =features['''words''']

             __A    =self.tokenizer(
                 text=text if text is not None else features['''words'''] ,   text_pair=text_pair if text_pair is not None else None ,   boxes=boxes if boxes is not None else features['''boxes'''] ,   word_labels=lowercase__ ,   add_special_tokens=lowercase__ ,   padding=lowercase__ ,   truncation=lowercase__ ,   max_length=lowercase__ ,   stride=lowercase__ ,   pad_to_multiple_of=lowercase__ ,   return_token_type_ids=lowercase__ ,   return_attention_mask=lowercase__ ,   return_overflowing_tokens=lowercase__ ,   return_special_tokens_mask=lowercase__ ,   return_offsets_mapping=lowercase__ ,   return_length=lowercase__ ,   verbose=lowercase__ ,   return_tensors=lowercase__ ,   **lowercase__ ,   )

             # add pixel values
             __A    =features.pop('''pixel_values'''     )
             if return_overflowing_tokens is True:
                   __A    =self.get_overflowing_images(lowercase__ ,   encoded_inputs['''overflow_to_sample_mapping''']     )
             __A    =images

             return encoded_inputs

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =[]
             for sample_idx in overflow_to_sample_mapping:
                   images_with_overflow.append(images[sample_idx]     )

             if len(lowercase__     ) != len(lowercase__     ):
                   raise ValueError(
                       '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                       f''' {len(lowercase__     )} and {len(lowercase__     )}'''     )

             return images_with_overflow

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             return self.tokenizer.batch_decode(*lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             return self.tokenizer.decode(*lowercase__ ,   **lowercase__     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return ["input_ids", "bbox", "attention_mask", "image"]

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             warnings.warn(
                 '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,   lowercase__ ,   )
             return self.image_processor_class


       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             warnings.warn(
                 '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,   lowercase__ ,   )
             return self.image_processor


