




"""simple docstring"""


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 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



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

     _snake_case							:		int								=  [t[-1] for t in os.walk(os.path.join(__lowercase,      os.listdir(__lowercase		)[0],      """snapshots"""		)		)]
     _snake_case							:		Tuple								=  [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 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  FlaxStableDiffusionPipeline.from_pretrained(
       """hf-internal-testing/tiny-stable-diffusion-pipe""",      safety_checker=__lowercase		)

   _snake_case							:		Optional[int]								=  (
       '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
       ''' field, close up, split lighting, cinematic'''
   )

   _snake_case							:		Optional[Any]								=  jax.random.PRNGKey(0		)
   _snake_case							:		str								=  4

   _snake_case							:		Union[str, Any]								=  jax.device_count()
   _snake_case							:		Dict								=  num_samples * [prompt]
   _snake_case							:		Optional[int]								=  pipeline.prepare_inputs(__lowercase		)

   # shard inputs and rng
   _snake_case							:		str								=  replicate(__lowercase		)
   _snake_case							:		Optional[Any]								=  jax.random.split(__lowercase,      __lowercase		)
   _snake_case							:		Optional[Any]								=  shard(__lowercase		)

   _snake_case							:		Optional[Any]								=  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() - 4.1_514_745		) < 1E-3
     assert np.abs(np.abs(__lowercase,      dtype=np.floataa		).sum() - 49_947.875		) < 5E-1

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


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Dict								=  FlaxStableDiffusionPipeline.from_pretrained(
       """CompVis/stable-diffusion-v1-4""",      revision="""flax""",      safety_checker=__lowercase		)

   _snake_case							:		List[str]								=  (
       '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
       ''' field, close up, split lighting, cinematic'''
   )

   _snake_case							:		List[Any]								=  jax.random.PRNGKey(0		)
   _snake_case							:		str								=  50

   _snake_case							:		Union[str, Any]								=  jax.device_count()
   _snake_case							:		Any								=  num_samples * [prompt]
   _snake_case							:		Dict								=  pipeline.prepare_inputs(__lowercase		)

   # shard inputs and rng
   _snake_case							:		List[str]								=  replicate(__lowercase		)
   _snake_case							:		List[Any]								=  jax.random.split(__lowercase,      __lowercase		)
   _snake_case							:		str								=  shard(__lowercase		)

   _snake_case							:		List[str]								=  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() - 0.05_652_401)		) < 1E-3
     assert np.abs((np.abs(__lowercase,      dtype=np.floataa		).sum() - 2_383_808.2)		) < 5E-1


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  FlaxStableDiffusionPipeline.from_pretrained(
       """CompVis/stable-diffusion-v1-4""",      revision="""bf16""",      dtype=jnp.bfloataa,      safety_checker=__lowercase		)

   _snake_case							:		Optional[int]								=  (
       '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
       ''' field, close up, split lighting, cinematic'''
   )

   _snake_case							:		str								=  jax.random.PRNGKey(0		)
   _snake_case							:		Dict								=  50

   _snake_case							:		Optional[Any]								=  jax.device_count()
   _snake_case							:		Optional[int]								=  num_samples * [prompt]
   _snake_case							:		Optional[int]								=  pipeline.prepare_inputs(__lowercase		)

   # shard inputs and rng
   _snake_case							:		List[str]								=  replicate(__lowercase		)
   _snake_case							:		Optional[Any]								=  jax.random.split(__lowercase,      __lowercase		)
   _snake_case							:		Optional[int]								=  shard(__lowercase		)

   _snake_case							:		Optional[Any]								=  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() - 0.04_003_906)		) < 1E-3
     assert np.abs((np.abs(__lowercase,      dtype=np.floataa		).sum() - 2_373_516.75)		) < 5E-1


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  FlaxStableDiffusionPipeline.from_pretrained(
       """CompVis/stable-diffusion-v1-4""",      revision="""bf16""",      dtype=jnp.bfloataa		)

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

   _snake_case							:		List[Any]								=  jax.random.PRNGKey(0		)
   _snake_case							:		List[Any]								=  50

   _snake_case							:		Any								=  jax.device_count()
   _snake_case							:		List[str]								=  num_samples * [prompt]
   _snake_case							:		Tuple								=  pipeline.prepare_inputs(__lowercase		)

   # shard inputs and rng
   _snake_case							:		Any								=  replicate(__lowercase		)
   _snake_case							:		Optional[int]								=  jax.random.split(__lowercase,      __lowercase		)
   _snake_case							:		Dict								=  shard(__lowercase		)

   _snake_case							:		Optional[Any]								=  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() - 0.04_003_906)		) < 1E-3
     assert np.abs((np.abs(__lowercase,      dtype=np.floataa		).sum() - 2_373_516.75)		) < 5E-1


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  FlaxDDIMScheduler(
       beta_start=0.00_085,      beta_end=0.012,      beta_schedule="""scaled_linear""",      set_alpha_to_one=__lowercase,      steps_offset=1,      )

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

   _snake_case							:		Any								=  scheduler_state

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

   _snake_case							:		Union[str, Any]								=  jax.random.PRNGKey(0		)
   _snake_case							:		Any								=  50

   _snake_case							:		str								=  jax.device_count()
   _snake_case							:		Optional[int]								=  num_samples * [prompt]
   _snake_case							:		List[str]								=  pipeline.prepare_inputs(__lowercase		)

   # shard inputs and rng
   _snake_case							:		Optional[int]								=  replicate(__lowercase		)
   _snake_case							:		List[Any]								=  jax.random.split(__lowercase,      __lowercase		)
   _snake_case							:		int								=  shard(__lowercase		)

   _snake_case							:		List[Any]								=  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() - 0.045_043_945)		) < 1E-3
     assert np.abs((np.abs(__lowercase,      dtype=np.floataa		).sum() - 2_347_693.5)		) < 5E-1


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



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

   _snake_case							:		Optional[int]								=  jax.device_count()
   _snake_case							:		int								=  num_samples * [prompt]
   _snake_case							:		Optional[Any]								=  jax.random.split(jax.random.PRNGKey(0		),      __lowercase		)

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

   _snake_case							:		List[str]								=  replicate(__lowercase		)
   _snake_case							:		Optional[int]								=  pipeline.prepare_inputs(__lowercase		)
   _snake_case							:		List[Any]								=  shard(__lowercase		)
   _snake_case							:		str								=  pipeline(__lowercase,      __lowercase,      __lowercase,      jit=__lowercase		).images
   assert images.shape == (num_samples, 1, 512, 512, 3)
   _snake_case							:		int								=  images[2, 0, 256, 10:17, 1]

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

   _snake_case							:		List[str]								=  replicate(__lowercase		)
   _snake_case							:		Dict								=  pipeline.prepare_inputs(__lowercase		)
   _snake_case							:		Union[str, Any]								=  shard(__lowercase		)
   _snake_case							:		str								=  pipeline(__lowercase,      __lowercase,      __lowercase,      jit=__lowercase		).images
   assert images_eff.shape == (num_samples, 1, 512, 512, 3)
   _snake_case							:		Union[str, Any]								=  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 glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__lowercase							 =					(720, 1280)  # Height, Width
__lowercase							 =					(0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowercase							 =					1 / 100
__lowercase							 =					''''''
__lowercase							 =					''''''
__lowercase							 =					''''''
__lowercase							 =					250



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		get_dataset(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    for index in range(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		random.sample(range(len(SCREAMING_SNAKE_CASE	)	)  ,			4	)
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :str			  =		update_image_and_anno(
            SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			filter_scale=SCREAMING_SNAKE_CASE  ,			)

        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        __UpperCamelCase     :List[Any]			  =		random_chars(32	)
        __UpperCamelCase     :List[str]			  =		path.split(os.sep	)[-1].rsplit('''.'''  ,			1	)[0]
        __UpperCamelCase     :Tuple			  =		f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
        cva.imwrite(f"""{file_root}.jpg"""  ,			SCREAMING_SNAKE_CASE  ,			[cva.IMWRITE_JPEG_QUALITY, 85]	)
        print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""	)
        __UpperCamelCase     :Optional[Any]			  =		[]
        for anno in new_annos:
            __UpperCamelCase     :int			  =		anno[3] - anno[1]
            __UpperCamelCase     :Optional[int]			  =		anno[4] - anno[2]
            __UpperCamelCase     :int			  =		anno[1] + width / 2
            __UpperCamelCase     :List[str]			  =		anno[2] + height / 2
            __UpperCamelCase     :str			  =		f"""{anno[0]} {x_center} {y_center} {width} {height}"""
            annos_list.append(SCREAMING_SNAKE_CASE	)
        with open(f"""{file_root}.txt"""  ,			'''w'''	) as outfile:
            outfile.write('''\n'''.join(line for line in annos_list	)	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :str			  =		[]
    __UpperCamelCase     :str			  =		[]
    for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE  ,			'''*.txt'''	)	):
        __UpperCamelCase     :Any			  =		label_file.split(os.sep	)[-1].rsplit('''.'''  ,			1	)[0]
        with open(SCREAMING_SNAKE_CASE	) as in_file:
            __UpperCamelCase     :str			  =		in_file.readlines()
        __UpperCamelCase     :Optional[int]			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			f"""{label_name}.jpg"""	)

        __UpperCamelCase     :int			  =		[]
        for obj_list in obj_lists:
            __UpperCamelCase     :Optional[int]			  =		obj_list.rstrip('''\n'''	).split(''' '''	)
            __UpperCamelCase     :Any			  =		float(obj[1]	) - float(obj[3]	) / 2
            __UpperCamelCase     :List[str]			  =		float(obj[2]	) - float(obj[4]	) / 2
            __UpperCamelCase     :Dict			  =		float(obj[1]	) + float(obj[3]	) / 2
            __UpperCamelCase     :List[str]			  =		float(obj[2]	) + float(obj[4]	) / 2

            boxes.append([int(obj[0]	), xmin, ymin, xmax, ymax]	)
        if not boxes:
            continue
        img_paths.append(SCREAMING_SNAKE_CASE	)
        labels.append(SCREAMING_SNAKE_CASE	)
    return img_paths, labels



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 0.0  ,			):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		np.zeros([output_size[0], output_size[1], 3]  ,			dtype=np.uinta	)
    __UpperCamelCase     :List[Any]			  =		scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    __UpperCamelCase     :int			  =		scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    __UpperCamelCase     :Optional[int]			  =		int(scale_x * output_size[1]	)
    __UpperCamelCase     :Any			  =		int(scale_y * output_size[0]	)

    __UpperCamelCase     :List[str]			  =		[]
    __UpperCamelCase     :Dict			  =		[]
    for i, index in enumerate(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Any			  =		all_img_list[index]
        path_list.append(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :Any			  =		all_annos[index]
        __UpperCamelCase     :Union[str, Any]			  =		cva.imread(SCREAMING_SNAKE_CASE	)
        if i == 0:  # top-left
            __UpperCamelCase     :str			  =		cva.resize(SCREAMING_SNAKE_CASE  ,			(divid_point_x, divid_point_y)	)
            __UpperCamelCase     :Union[str, Any]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :Union[str, Any]			  =		bbox[1] * scale_x
                __UpperCamelCase     :Optional[Any]			  =		bbox[2] * scale_y
                __UpperCamelCase     :int			  =		bbox[3] * scale_x
                __UpperCamelCase     :Union[str, Any]			  =		bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
        elif i == 1:  # top-right
            __UpperCamelCase     :str			  =		cva.resize(SCREAMING_SNAKE_CASE  ,			(output_size[1] - divid_point_x, divid_point_y)	)
            __UpperCamelCase     :List[str]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :str			  =		scale_x + bbox[1] * (1 - scale_x)
                __UpperCamelCase     :Dict			  =		bbox[2] * scale_y
                __UpperCamelCase     :Optional[Any]			  =		scale_x + bbox[3] * (1 - scale_x)
                __UpperCamelCase     :List[Any]			  =		bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
        elif i == 2:  # bottom-left
            __UpperCamelCase     :str			  =		cva.resize(SCREAMING_SNAKE_CASE  ,			(divid_point_x, output_size[0] - divid_point_y)	)
            __UpperCamelCase     :Optional[int]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :Tuple			  =		bbox[1] * scale_x
                __UpperCamelCase     :Optional[Any]			  =		scale_y + bbox[2] * (1 - scale_y)
                __UpperCamelCase     :Tuple			  =		bbox[3] * scale_x
                __UpperCamelCase     :Dict			  =		scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
        else:  # bottom-right
            __UpperCamelCase     :Optional[int]			  =		cva.resize(
                SCREAMING_SNAKE_CASE  ,			(output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
            __UpperCamelCase     :Optional[int]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :Optional[Any]			  =		scale_x + bbox[1] * (1 - scale_x)
                __UpperCamelCase     :Optional[int]			  =		scale_y + bbox[2] * (1 - scale_y)
                __UpperCamelCase     :Optional[Any]			  =		scale_x + bbox[3] * (1 - scale_x)
                __UpperCamelCase     :int			  =		scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)

    # Remove bounding box small than scale of filter
    if filter_scale > 0:
        __UpperCamelCase     :List[Any]			  =		[
            anno
            for anno in new_anno
            if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
        ]

    return output_img, new_anno, path_list[0]



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    assert number_char > 1, "The number of character should greater than 1"
    __UpperCamelCase     :Optional[Any]			  =		ascii_lowercase + digits
    return "".join(random.choice(SCREAMING_SNAKE_CASE	) for _ in range(SCREAMING_SNAKE_CASE	)	)


if __name__ == "__main__":
    main()
    print('''DONE ✅''')





