



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(lowerCamelCase__       )
class 					__lowerCAmelCase (     lowerCamelCase__       ):




				def __init__(							self						,				**_snake_case		):



									"""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						,				_snake_case						,				_snake_case = None						,				**_snake_case						,				):



									"""simple docstring"""





									if "text_queries" in kwargs:
														_lowerCAmelCase						=    kwargs.pop("""text_queries"""		)

									if isinstance(_snake_case						,				(str, Image.Image)		):
														_lowerCAmelCase						=    {"""image""": image, """candidate_labels""": candidate_labels}
									else:
														_lowerCAmelCase						=    image
									_lowerCAmelCase						=    super().__call__(_snake_case						,				**_snake_case		)
									return results




				def 							snake_case   (							self						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    {}
									if "threshold" in kwargs:
														_lowerCAmelCase						=    kwargs["""threshold"""]
									if "top_k" in kwargs:
														_lowerCAmelCase						=    kwargs["""top_k"""]
									return {}, {}, postprocess_params




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    load_image(inputs["""image"""]		)
									_lowerCAmelCase						=    inputs["""candidate_labels"""]
									if isinstance(_snake_case						,				_snake_case		):
														_lowerCAmelCase						=    candidate_labels.split(""","""		)

									_lowerCAmelCase						=    torch.tensor([[image.height, image.width]]						,				dtype=torch.intaa		)
									for i, candidate_label in enumerate(_snake_case		):
														_lowerCAmelCase						=    self.tokenizer(_snake_case						,				return_tensors=self.framework		)
														_lowerCAmelCase						=    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 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    model_inputs.pop("""target_size"""		)
									_lowerCAmelCase						=    model_inputs.pop("""candidate_label"""		)
									_lowerCAmelCase						=    model_inputs.pop("""is_last"""		)

									_lowerCAmelCase						=    self.model(**_snake_case		)

									_lowerCAmelCase						=    {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
									return model_outputs




				def 							snake_case   (							self						,				_snake_case						,				_snake_case=0.1						,				_snake_case=None		):



									"""simple docstring"""





									_lowerCAmelCase						=    []
									for model_output in model_outputs:
														_lowerCAmelCase						=    model_output["""candidate_label"""]
														_lowerCAmelCase						=    BaseModelOutput(_snake_case		)
														_lowerCAmelCase						=    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():
																			_lowerCAmelCase						=    outputs["""scores"""][index].item()
																			_lowerCAmelCase						=    self._get_bounding_box(outputs["""boxes"""][index][0]		)

																			_lowerCAmelCase						=    {"""score""": score, """label""": label, """box""": box}
																			results.append(_snake_case		)

									_lowerCAmelCase						=    sorted(_snake_case						,				key=lambda _snake_case		: x["score"]						,				reverse=_snake_case		)
									if top_k:
														_lowerCAmelCase						=    results[:top_k]

									return results




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





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











"""simple docstring"""



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        a     (   unittest.TestCase	):







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







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

      lowercase							    =       'A painting of a squirrel eating a burger'
      lowercase							    =       jax.device_count()
      lowercase							    =       num_samples * [prompt]
      lowercase							    =       sd_pipe.prepare_inputs(_lowerCamelCase       )

      lowercase							    =       replicate(_lowerCamelCase       )
      lowercase							    =       shard(_lowerCamelCase       )

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

      lowercase							    =       sd_pipe(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		num_inference_steps=2_5							,		jit=_lowerCamelCase       )[0]
      assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

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

      lowercase							    =       jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      lowercase							    =       jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2]       )
      print(F'output_slice: {output_slice}'       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       'stabilityai/stable-diffusion-2'
      lowercase    ,     lowercase							    =       FlaxDPMSolverMultistepScheduler.from_pretrained(_lowerCamelCase							,		subfolder='scheduler'       )
      lowercase    ,     lowercase							    =       FlaxStableDiffusionPipeline.from_pretrained(
          _lowerCamelCase							,		scheduler=_lowerCamelCase							,		revision='bf16'							,		dtype=jnp.bfloataa							,		)
      lowercase							    =       scheduler_params

      lowercase							    =       'A painting of a squirrel eating a burger'
      lowercase							    =       jax.device_count()
      lowercase							    =       num_samples * [prompt]
      lowercase							    =       sd_pipe.prepare_inputs(_lowerCamelCase       )

      lowercase							    =       replicate(_lowerCamelCase       )
      lowercase							    =       shard(_lowerCamelCase       )

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

      lowercase							    =       sd_pipe(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		num_inference_steps=2_5							,		jit=_lowerCamelCase       )[0]
      assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

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

      lowercase							    =       jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      lowercase							    =       jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7]       )
      print(F'output_slice: {output_slice}'       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2


