



import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
							import jax
							import jax.numpy as jnp
							from flax.jax_utils import replicate
							from flax.training.common_utils import shard

							from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class 			_snake_case     (   unittest.TestCase):




				def 							A__ (  self   :  Dict  ):
							with tempfile.TemporaryDirectory() as tmpdirname:
										# pipeline has Flax weights
										lowercase__   					=			FlaxDiffusionPipeline.from_pretrained(
										    "hf-internal-testing/tiny-stable-diffusion-pipe",   safety_checker=__lowercase,   cache_dir=__lowercase  )

										lowercase__   					=			[t[-1] for t in os.walk(os.path.join(__lowercase,   os.listdir(__lowercase  )[0],   "snapshots"  )  )]
										lowercase__   					=			[item for sublist in all_root_files for item in sublist]

										# None of the downloaded files should be a PyTorch file even if we have some here:
										# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
										assert not any(f.endswith(".bin"  ) for f in files  )
@slow
@require_flax
class 			_snake_case     (   unittest.TestCase):




				def 							A__ (  self   :  Any  ):
							lowercase__					,    lowercase__   					=			FlaxStableDiffusionPipeline.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-pipe",   safety_checker=__lowercase  )

							lowercase__   					=			(
							    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
							    " field, close up, split lighting, cinematic"
							)

							lowercase__   					=			jax.random.PRNGKey(0  )
							lowercase__   					=			4

							lowercase__   					=			jax.device_count()
							lowercase__   					=			num_samples * [prompt]
							lowercase__   					=			pipeline.prepare_inputs(__lowercase  )

							# shard inputs and rng
							lowercase__   					=			replicate(__lowercase  )
							lowercase__   					=			jax.random.split(__lowercase,   __lowercase  )
							lowercase__   					=			shard(__lowercase  )

							lowercase__   					=			pipeline(__lowercase,   __lowercase,   __lowercase,   __lowercase,   jit=__lowercase  ).images

							assert images.shape == (num_samples, 1, 64, 64, 3)
							if jax.device_count() == 8:
										assert np.abs(np.abs(images[0, 0, :2, :2, -2:],   dtype=np.floataa  ).sum() - 4.1514745  ) < 1e-3
										assert np.abs(np.abs(__lowercase,   dtype=np.floataa  ).sum() - 49947.875  ) < 5e-1

							lowercase__   					=			pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]  )  )  )
							assert len(__lowercase  ) == num_samples




				def 							A__ (  self   :  Union[str, Any]  ):
							lowercase__					,    lowercase__   					=			FlaxStableDiffusionPipeline.from_pretrained(
							    "CompVis/stable-diffusion-v1-4",   revision="flax",   safety_checker=__lowercase  )

							lowercase__   					=			(
							    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
							    " field, close up, split lighting, cinematic"
							)

							lowercase__   					=			jax.random.PRNGKey(0  )
							lowercase__   					=			50

							lowercase__   					=			jax.device_count()
							lowercase__   					=			num_samples * [prompt]
							lowercase__   					=			pipeline.prepare_inputs(__lowercase  )

							# shard inputs and rng
							lowercase__   					=			replicate(__lowercase  )
							lowercase__   					=			jax.random.split(__lowercase,   __lowercase  )
							lowercase__   					=			shard(__lowercase  )

							lowercase__   					=			pipeline(__lowercase,   __lowercase,   __lowercase,   __lowercase,   jit=__lowercase  ).images

							assert images.shape == (num_samples, 1, 512, 512, 3)
							if jax.device_count() == 8:
										assert np.abs((np.abs(images[0, 0, :2, :2, -2:],   dtype=np.floataa  ).sum() - 0.05652401)  ) < 1e-3
										assert np.abs((np.abs(__lowercase,   dtype=np.floataa  ).sum() - 2383808.2)  ) < 5e-1




				def 							A__ (  self   :  str  ):
							lowercase__					,    lowercase__   					=			FlaxStableDiffusionPipeline.from_pretrained(
							    "CompVis/stable-diffusion-v1-4",   revision="bf16",   dtype=jnp.bfloataa,   safety_checker=__lowercase  )

							lowercase__   					=			(
							    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
							    " field, close up, split lighting, cinematic"
							)

							lowercase__   					=			jax.random.PRNGKey(0  )
							lowercase__   					=			50

							lowercase__   					=			jax.device_count()
							lowercase__   					=			num_samples * [prompt]
							lowercase__   					=			pipeline.prepare_inputs(__lowercase  )

							# shard inputs and rng
							lowercase__   					=			replicate(__lowercase  )
							lowercase__   					=			jax.random.split(__lowercase,   __lowercase  )
							lowercase__   					=			shard(__lowercase  )

							lowercase__   					=			pipeline(__lowercase,   __lowercase,   __lowercase,   __lowercase,   jit=__lowercase  ).images

							assert images.shape == (num_samples, 1, 512, 512, 3)
							if jax.device_count() == 8:
										assert np.abs((np.abs(images[0, 0, :2, :2, -2:],   dtype=np.floataa  ).sum() - 0.04003906)  ) < 1e-3
										assert np.abs((np.abs(__lowercase,   dtype=np.floataa  ).sum() - 2373516.75)  ) < 5e-1




				def 							A__ (  self   :  Tuple  ):
							lowercase__					,    lowercase__   					=			FlaxStableDiffusionPipeline.from_pretrained(
							    "CompVis/stable-diffusion-v1-4",   revision="bf16",   dtype=jnp.bfloataa  )

							lowercase__   					=			(
							    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
							    " field, close up, split lighting, cinematic"
							)

							lowercase__   					=			jax.random.PRNGKey(0  )
							lowercase__   					=			50

							lowercase__   					=			jax.device_count()
							lowercase__   					=			num_samples * [prompt]
							lowercase__   					=			pipeline.prepare_inputs(__lowercase  )

							# shard inputs and rng
							lowercase__   					=			replicate(__lowercase  )
							lowercase__   					=			jax.random.split(__lowercase,   __lowercase  )
							lowercase__   					=			shard(__lowercase  )

							lowercase__   					=			pipeline(__lowercase,   __lowercase,   __lowercase,   __lowercase,   jit=__lowercase  ).images

							assert images.shape == (num_samples, 1, 512, 512, 3)
							if jax.device_count() == 8:
										assert np.abs((np.abs(images[0, 0, :2, :2, -2:],   dtype=np.floataa  ).sum() - 0.04003906)  ) < 1e-3
										assert np.abs((np.abs(__lowercase,   dtype=np.floataa  ).sum() - 2373516.75)  ) < 5e-1




				def 							A__ (  self   :  int  ):
							lowercase__   					=			FlaxDDIMScheduler(
							    beta_start=0.00085,   beta_end=0.012,   beta_schedule="scaled_linear",   set_alpha_to_one=__lowercase,   steps_offset=1,   )

							lowercase__					,    lowercase__   					=			FlaxStableDiffusionPipeline.from_pretrained(
							    "CompVis/stable-diffusion-v1-4",   revision="bf16",   dtype=jnp.bfloataa,   scheduler=__lowercase,   safety_checker=__lowercase,   )
							lowercase__   					=			scheduler.create_state()

							lowercase__   					=			scheduler_state

							lowercase__   					=			(
							    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
							    " field, close up, split lighting, cinematic"
							)

							lowercase__   					=			jax.random.PRNGKey(0  )
							lowercase__   					=			50

							lowercase__   					=			jax.device_count()
							lowercase__   					=			num_samples * [prompt]
							lowercase__   					=			pipeline.prepare_inputs(__lowercase  )

							# shard inputs and rng
							lowercase__   					=			replicate(__lowercase  )
							lowercase__   					=			jax.random.split(__lowercase,   __lowercase  )
							lowercase__   					=			shard(__lowercase  )

							lowercase__   					=			pipeline(__lowercase,   __lowercase,   __lowercase,   __lowercase,   jit=__lowercase  ).images

							assert images.shape == (num_samples, 1, 512, 512, 3)
							if jax.device_count() == 8:
										assert np.abs((np.abs(images[0, 0, :2, :2, -2:],   dtype=np.floataa  ).sum() - 0.045043945)  ) < 1e-3
										assert np.abs((np.abs(__lowercase,   dtype=np.floataa  ).sum() - 2347693.5)  ) < 5e-1




				def 							A__ (  self   :  List[Any]  ):
							lowercase__   					=			(
							    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
							    " field, close up, split lighting, cinematic"
							)

							lowercase__   					=			jax.device_count()
							lowercase__   					=			num_samples * [prompt]
							lowercase__   					=			jax.random.split(jax.random.PRNGKey(0  ),   __lowercase  )

							lowercase__					,    lowercase__   					=			FlaxStableDiffusionPipeline.from_pretrained(
							    "CompVis/stable-diffusion-v1-4",   revision="bf16",   dtype=jnp.bfloataa,   safety_checker=__lowercase,   )

							lowercase__   					=			replicate(__lowercase  )
							lowercase__   					=			pipeline.prepare_inputs(__lowercase  )
							lowercase__   					=			shard(__lowercase  )
							lowercase__   					=			pipeline(__lowercase,   __lowercase,   __lowercase,   jit=__lowercase  ).images
							assert images.shape == (num_samples, 1, 512, 512, 3)
							lowercase__   					=			images[2, 0, 256, 10:17, 1]

							# With memory efficient attention
							lowercase__					,    lowercase__   					=			FlaxStableDiffusionPipeline.from_pretrained(
							    "CompVis/stable-diffusion-v1-4",   revision="bf16",   dtype=jnp.bfloataa,   safety_checker=__lowercase,   use_memory_efficient_attention=__lowercase,   )

							lowercase__   					=			replicate(__lowercase  )
							lowercase__   					=			pipeline.prepare_inputs(__lowercase  )
							lowercase__   					=			shard(__lowercase  )
							lowercase__   					=			pipeline(__lowercase,   __lowercase,   __lowercase,   jit=__lowercase  ).images
							assert images_eff.shape == (num_samples, 1, 512, 512, 3)
							lowercase__   					=			images[2, 0, 256, 10:17, 1]

							# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
							# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
							assert abs(slice_eff - slice  ).max() < 1e-2




import fire

from utils import calculate_rouge, save_json



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=None  ,					**SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()]
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()][: len(SCREAMING_SNAKE_CASE_					)]
			lowercase__   					=			calculate_rouge(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					**SCREAMING_SNAKE_CASE_					)
			if save_path is not None:
						save_json(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					indent=SCREAMING_SNAKE_CASE_					)
			return metrics  # these print nicely


if __name__ == "__main__":
							fire.Fire(calculate_rouge_path)




