'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput





class 							lowercase						(						__lowerCamelCase ):


   def __init__(   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	int)      ->					int:
         lowercase_     =      dataset
         lowercase_     =      process
         lowercase_     =      params


   def __len__(   self				:	str)      ->					List[Any]:
         return len(self.dataset)


   def __getitem__(   self				:	Any   ,		__lowerCAmelCase				:	List[Any])      ->					str:
         lowercase_     =      self.dataset[i]
         lowercase_     =      self.process(__lowerCAmelCase   ,		**self.params)
         return processed





class 							lowercase						(						__lowerCamelCase ):


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	Tuple=None)      ->					Optional[int]:
         lowercase_     =      loader
         lowercase_     =      infer
         lowercase_     =      params
         if loader_batch_size == 1:
               # Let's spare some time by deactivating altogether
               lowercase_     =      None
         lowercase_     =      loader_batch_size

         # Internal bookkeeping
         lowercase_     =      None
         lowercase_     =      None


   def __len__(   self				:	str)      ->					Any:
         return len(self.loader)


   def __iter__(   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      iter(self.loader)
         return self


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Dict:
         if isinstance(self._loader_batch_data   ,		torch.Tensor):
               # Batch data is simple tensor, just fetch the slice
               lowercase_     =      self._loader_batch_data[self._loader_batch_index]
         else:
               # Batch data is assumed to be BaseModelOutput (or dict)
               lowercase_     =      {}
               for k, element in self._loader_batch_data.items():
                     if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                           # Convert ModelOutput to tuple first
                           lowercase_     =      element.to_tuple()
                           if isinstance(element[0]   ,		torch.Tensor):
                                 lowercase_     =      tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
                           elif isinstance(element[0]   ,		np.ndarray):
                                 lowercase_     =      tuple(np.expand_dims(el[self._loader_batch_index]   ,		0) for el in element)
                           continue
                     if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                           # Those are stored as lists of tensors so need specific unbatching.
                           if isinstance(element[0]   ,		torch.Tensor):
                                 lowercase_     =      tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
                           elif isinstance(element[0]   ,		np.ndarray):
                                 lowercase_     =      tuple(np.expand_dims(el[self._loader_batch_index]   ,		0) for el in element)
                           continue
                     if element is None:
                           # This can happen for optional data that get passed around
                           lowercase_     =      None
                     elif isinstance(element[self._loader_batch_index]   ,		torch.Tensor):
                           # Take correct batch data, but make it looked like batch_size=1
                           # For compatibility with other methods within transformers

                           lowercase_     =      element[self._loader_batch_index].unsqueeze(0)
                     elif isinstance(element[self._loader_batch_index]   ,		np.ndarray):
                           # Take correct batch data, but make it looked like batch_size=1
                           # For compatibility with other methods within transformers
                           lowercase_     =      np.expand_dims(element[self._loader_batch_index]   ,		0)
                     else:
                           # This is typically a list, so no need to `unsqueeze`.
                           lowercase_     =      element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
               lowercase_     =      self._loader_batch_data.__class__(__lowerCAmelCase)
         self._loader_batch_index += 1
         return result


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Optional[int]:
         if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
               # We are currently unrolling a batch so we just need to return
               # the current item within a batch
               return self.loader_batch_item()

         # We're out of items within a batch
         lowercase_     =      next(self.iterator)
         lowercase_     =      self.infer(__lowerCAmelCase   ,		**self.params)
         # We now have a batch of "inferred things".
         if self.loader_batch_size is not None:
               # Try to infer the size of the batch
               if isinstance(__lowerCAmelCase   ,		torch.Tensor):
                     lowercase_     =      processed
               else:
                     lowercase_     =      list(processed.keys())[0]
                     lowercase_     =      processed[key]
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      len(__lowerCAmelCase)
               else:
                     lowercase_     =      first_tensor.shape[0]
               if 0 < observed_batch_size < self.loader_batch_size:
                     # could be last batch so we can't unroll as many
                     # elements.
                     lowercase_     =      observed_batch_size
               # Setting internal index to unwrap the batch
               lowercase_     =      processed
               lowercase_     =      0
               return self.loader_batch_item()
         else:
               # We're not unrolling batches
               return processed





class 							lowercase						(						__lowerCamelCase ):


   def __init__(   self				:	Optional[Any]   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	int=None)      ->					Dict:
         super().__init__(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase)


   def __iter__(   self				:	Any)      ->					List[str]:
         lowercase_     =      iter(self.loader)
         lowercase_     =      None
         return self


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					List[Any]:
         if self.subiterator is None:
               lowercase_     =      self.infer(next(self.iterator)   ,		**self.params)
         try:
               # Try to return next item
               lowercase_     =      next(self.subiterator)
         except StopIteration:
               # When a preprocess iterator ends, we can start lookig at the next item
               # ChunkIterator will keep feeding until ALL elements of iterator
               # all have created their subiterator and have been iterating against.
               #
               # Another way to look at it, is we're basically flattening lists of lists
               # into a single list, but with generators
               lowercase_     =      self.infer(next(self.iterator)   ,		**self.params)
               lowercase_     =      next(self.subiterator)
         return processed





class 							lowercase						(						__lowerCamelCase ):


   def __iter__(   self				:	Optional[Any])      ->					Dict:
         lowercase_     =      iter(self.loader)
         return self


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Optional[Any]:
         # Extremely similar to PipelineIterator in its unpacking mechanism
         # BUT, we have an extra required item which is the presence of `is_last`
         # That is because everything is flattened by `PipelineChunkIterator` we
         # need to keep track of how to regroup here in the original `process`
         # boundaries so that `process` and `postprocess` see the same data.

         # This iterator accumulates items (possibly while unbatching) until it
         # its a `is_last` and then just passes it on to the caller.
         lowercase_     =      False
         lowercase_     =      []
         if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
               while self._loader_batch_index < self.loader_batch_size:
                     lowercase_     =      self.loader_batch_item()
                     lowercase_     =      item.pop("is_last")
                     accumulator.append(__lowerCAmelCase)
                     if is_last:
                           return accumulator

         while not is_last:
               lowercase_     =      self.infer(next(self.iterator)   ,		**self.params)
               if self.loader_batch_size is not None:
                     if isinstance(__lowerCAmelCase   ,		torch.Tensor):
                           lowercase_     =      processed
                     else:
                           lowercase_     =      list(processed.keys())[0]
                           lowercase_     =      processed[key]
                     if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                           lowercase_     =      len(__lowerCAmelCase)
                     else:
                           lowercase_     =      first_tensor.shape[0]
                     if 0 < observed_batch_size < self.loader_batch_size:
                           # could be last batch so we can't unroll as many
                           # elements.
                           lowercase_     =      observed_batch_size
                     lowercase_     =      processed
                     lowercase_     =      0
                     while self._loader_batch_index < self.loader_batch_size:
                           lowercase_     =      self.loader_batch_item()
                           lowercase_     =      item.pop("is_last")
                           accumulator.append(__lowerCAmelCase)
                           if is_last:
                                 return accumulator
               else:
                     lowercase_     =      processed
                     lowercase_     =      item.pop("is_last")
                     accumulator.append(__lowerCAmelCase)
         return accumulator





class 							lowercase						(						__lowerCamelCase ):


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	Dataset   ,		__lowerCAmelCase				:	str)      ->					Dict:
         lowercase_     =      dataset
         lowercase_     =      key


   def __len__(   self				:	List[Any])      ->					int:
         return len(self.dataset)


   def __getitem__(   self				:	str   ,		__lowerCAmelCase				:	List[Any])      ->					Optional[Any]:
         return self.dataset[i][self.key]





class 							lowercase						(						__lowerCamelCase ):


   def __init__(   self				:	List[Any]   ,		__lowerCAmelCase				:	Dataset   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	str)      ->					Tuple:
         lowercase_     =      dataset
         lowercase_     =      keya
         lowercase_     =      keya


   def __len__(   self				:	Union[str, Any])      ->					Union[str, Any]:
         return len(self.dataset)


   def __getitem__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	str)      ->					str:
         return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}







'''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 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	int)      ->					List[str]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()


   def __UpperCAmelCase       (   self				:	Dict)      ->					Any:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "bird"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

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

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      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)

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

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2


   def __UpperCAmelCase       (   self				:	Tuple)      ->					List[str]:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

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

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

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      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)

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

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2






