

import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
     import onnxruntime as ort


_UpperCAmelCase		:       Dict						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       Dict						    =	{
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}







class 			lowercase    :




    def __init__( self				,       snake_case=None				,       **snake_case	):
           logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'	)
           snake_case_          =   model
           snake_case_          =   kwargs.get('model_save_dir'				,       _snake_case	)
           snake_case_          =   kwargs.get('latest_model_name'				,       _snake_case	)




    def __call__( self				,       **snake_case	):
           snake_case_          =   {k: np.array(_snake_case	) for k, v in kwargs.items()}
           return self.model.run(_snake_case				,       _snake_case	)




    @staticmethod
    def    a							( snake_case				,       snake_case=None				,       snake_case=None	):


           if provider is None:
                  logger.info('No onnxruntime provider specified, using CPUExecutionProvider'	)
                  snake_case_          =   'CPUExecutionProvider'

           return ort.InferenceSession(_snake_case				,       providers=[provider]				,       sess_options=_snake_case	)




    def    a							( self				,       snake_case				,       snake_case = None				,       **snake_case	):
           snake_case_          =   file_name if file_name is not None else ONNX_WEIGHTS_NAME

           snake_case_          =   self.model_save_dir.joinpath(self.latest_model_name	)
           snake_case_          =   Path(_snake_case	).joinpath(_snake_case	)
           try:
                  shutil.copyfile(_snake_case				,       _snake_case	)
           except shutil.SameFileError:
                  pass

           # copy external weights (for models >2GB)
           snake_case_          =   self.model_save_dir.joinpath(_snake_case	)
           if src_path.exists():
                  snake_case_          =   Path(_snake_case	).joinpath(_snake_case	)
                  try:
                         shutil.copyfile(_snake_case				,       _snake_case	)
                  except shutil.SameFileError:
                         pass




    def    a							( self				,       snake_case				,       **snake_case				,       ):


           if os.path.isfile(_snake_case	):
                  logger.error(F'''Provided path ({save_directory}) should be a directory, not a file'''	)
                  return

           os.makedirs(_snake_case				,       exist_ok=_snake_case	)

           # saving model weights/files
           self._save_pretrained(_snake_case				,       **_snake_case	)




    @classmethod
    def    a							( cls				,       snake_case				,       snake_case = None				,       snake_case = None				,       snake_case = False				,       snake_case = None				,       snake_case = None				,       snake_case = None				,       snake_case = None				,       **snake_case				,       ):
           snake_case_          =   file_name if file_name is not None else ONNX_WEIGHTS_NAME
           # load model from local directory
           if os.path.isdir(_snake_case	):
                  snake_case_          =   OnnxRuntimeModel.load_model(
                      os.path.join(_snake_case				,       _snake_case	)				,       provider=_snake_case				,       sess_options=_snake_case	)
                  snake_case_          =   Path(_snake_case	)
           # load model from hub
           else:
                  # download model
                  snake_case_          =   hf_hub_download(
                      repo_id=_snake_case				,       filename=_snake_case				,       use_auth_token=_snake_case				,       revision=_snake_case				,       cache_dir=_snake_case				,       force_download=_snake_case				,       )
                  snake_case_          =   Path(_snake_case	).parent
                  snake_case_          =   Path(_snake_case	).name
                  snake_case_          =   OnnxRuntimeModel.load_model(_snake_case				,       provider=_snake_case				,       sess_options=_snake_case	)
           return cls(model=_snake_case				,       **_snake_case	)




    @classmethod
    def    a							( cls				,       snake_case				,       snake_case = True				,       snake_case = None				,       snake_case = None				,       **snake_case				,       ):
           snake_case_          =   None
           if len(str(_snake_case	).split('@'	)	) == 2:
                  snake_case_    ,   snake_case_          =   model_id.split('@'	)

           return cls._from_pretrained(
               model_id=_snake_case				,       revision=_snake_case				,       cache_dir=_snake_case				,       force_download=_snake_case				,       use_auth_token=_snake_case				,       **_snake_case				,       )







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




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

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

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

											snake_case_          =   jax.random.PRNGKey(0	)
											snake_case_          =   jax.random.split(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

											snake_case_          =   pipe(
											    prompt_ids=snake_case				,       image=snake_case				,       params=snake_case				,       prng_seed=snake_case				,       num_inference_steps=50				,       jit=snake_case				,       ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

											snake_case_          =   jnp.asarray(jax.device_get(image_slice.flatten()	)	)
											snake_case_          =   jnp.array(
											    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]	)
											print(F'''output_slice: {output_slice}'''	)
											assert jnp.abs(output_slice - expected_slice	).max() < 1e-2




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

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

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

											snake_case_          =   jax.random.PRNGKey(0	)
											snake_case_          =   jax.random.split(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

											snake_case_          =   pipe(
											    prompt_ids=snake_case				,       image=snake_case				,       params=snake_case				,       prng_seed=snake_case				,       num_inference_steps=50				,       jit=snake_case				,       ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

											snake_case_          =   jnp.asarray(jax.device_get(image_slice.flatten()	)	)
											snake_case_          =   jnp.array(
											    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]	)
											print(F'''output_slice: {output_slice}'''	)
											assert jnp.abs(output_slice - expected_slice	).max() < 1e-2




