from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_torch_available():
   import torch

   from transformers.modeling_outputs import BaseModelOutput

   from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

a_    					=       logging.get_logger(__name__)





@add_end_docstrings(snake_case_       )
class 		_lowercase						(					snake_case_       ):

 def __init__( self	:							List[str]   ,      **snake_case	:							List[str]			)   ->						List[Any]:

  """simple docstring"""





  super().__init__(**snake_case			)

  if self.framework == "tf":
   raise ValueError(f"The {self.__class__} is only available in PyTorch."			)

  requires_backends(self   ,      'vision'			)
  self.check_model_type(snake_case			)

 def __call__( self	:							List[str]   ,      snake_case	:							Union[str, "Image.Image", List[Dict[str, Any]]]   ,      snake_case	:							Union[str, List[str]] = None   ,      **snake_case	:							str   ,      )   ->						int:

  """simple docstring"""





  if "text_queries" in kwargs:
   UpperCamelCase_					:						Optional[int] =					kwargs.pop('text_queries'			)

  if isinstance(snake_case   ,      (str, Image.Image)			):
   UpperCamelCase_					:						Optional[int] =					{'image': image, 'candidate_labels': candidate_labels}
  else:
   UpperCamelCase_					:						Tuple =					image
  UpperCamelCase_					:						Dict =					super().__call__(snake_case   ,      **snake_case			)
  return results

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      **snake_case	:							List[str]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					{}
  if "threshold" in kwargs:
   UpperCamelCase_					:						Optional[int] =					kwargs['threshold']
  if "top_k" in kwargs:
   UpperCamelCase_					:						Any =					kwargs['top_k']
  return {}, {}, postprocess_params

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							Dict			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					load_image(inputs['image']			)
  UpperCamelCase_					:						Tuple =					inputs['candidate_labels']
  if isinstance(snake_case   ,      snake_case			):
   UpperCamelCase_					:						Any =					candidate_labels.split(','			)

  UpperCamelCase_					:						Any =					torch.tensor([[image.height, image.width]]   ,      dtype=torch.intaa			)
  for i, candidate_label in enumerate(snake_case			):
   UpperCamelCase_					:						List[Any] =					self.tokenizer(snake_case   ,      return_tensors=self.framework			)
   UpperCamelCase_					:						str =					self.image_processor(snake_case   ,      return_tensors=self.framework			)
   yield {
       "is_last": i == len(snake_case			) - 1,
       "target_size": target_size,
       "candidate_label": candidate_label,
       **text_inputs,
       **image_features,
   }

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							str			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					model_inputs.pop('target_size'			)
  UpperCamelCase_					:						List[Any] =					model_inputs.pop('candidate_label'			)
  UpperCamelCase_					:						Union[str, Any] =					model_inputs.pop('is_last'			)

  UpperCamelCase_					:						str =					self.model(**snake_case			)

  UpperCamelCase_					:						Tuple =					{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
  return model_outputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Any   ,      snake_case	:							List[Any]=0.1   ,      snake_case	:							int=None			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					[]
  for model_output in model_outputs:
   UpperCamelCase_					:						List[Any] =					model_output['candidate_label']
   UpperCamelCase_					:						List[str] =					BaseModelOutput(snake_case			)
   UpperCamelCase_					:						str =					self.image_processor.post_process_object_detection(
       outputs=snake_case   ,      threshold=snake_case   ,      target_sizes=model_output['target_size']			)[0]

   for index in outputs["scores"].nonzero():
    UpperCamelCase_					:						str =					outputs['scores'][index].item()
    UpperCamelCase_					:						int =					self._get_bounding_box(outputs['boxes'][index][0]			)

    UpperCamelCase_					:						Any =					{'score': score, 'label': label, 'box': box}
    results.append(snake_case			)

  UpperCamelCase_					:						List[str] =					sorted(snake_case   ,      key=lambda snake_case			: x["score"]   ,      reverse=snake_case			)
  if top_k:
   UpperCamelCase_					:						Optional[int] =					results[:top_k]

  return results


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							"torch.Tensor"			)   ->						Dict[str, int]:

  """simple docstring"""





  if self.framework != "pt":
   raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'			)
  UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						int =					box.int().tolist()
  UpperCamelCase_					:						Optional[int] =					{
      'xmin': xmin,
      'ymin': ymin,
      'xmax': xmax,
      'ymax': ymax,
  }
  return bbox




import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						str:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						str =					FlaxStableDiffusionPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2'   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )

  UpperCamelCase_					:						str =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Any =					jax.device_count()
  UpperCamelCase_					:						List[str] =					num_samples * [prompt]
  UpperCamelCase_					:						List[Any] =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						Dict =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Dict =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						Tuple =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						Optional[Any] =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						int =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						Tuple =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						str =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						List[str] =					jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'stabilityai/stable-diffusion-2'
  UpperCamelCase_, UpperCamelCase_					:						Tuple =					FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case   ,      subfolder='scheduler'			)
  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					FlaxStableDiffusionPipeline.from_pretrained(
      snake_case   ,      scheduler=snake_case   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )
  UpperCamelCase_					:						Optional[int] =					scheduler_params

  UpperCamelCase_					:						Optional[Any] =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Union[str, Any] =					jax.device_count()
  UpperCamelCase_					:						Union[str, Any] =					num_samples * [prompt]
  UpperCamelCase_					:						Tuple =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						List[Any] =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Tuple =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						str =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						str =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						str =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						int =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						int =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						Union[str, Any] =					jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2



