




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 ):



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

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

										SCREAMING_SNAKE_CASE_					      =							[t[-1] for t in os.walk(os.path.join(a_					,							os.listdir(a_  )[0]					,							'snapshots'  )  )]
										SCREAMING_SNAKE_CASE_					      =							[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 ):



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							FlaxStableDiffusionPipeline.from_pretrained(
								    'hf-internal-testing/tiny-stable-diffusion-pipe'					,							safety_checker=a_  )

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

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

								SCREAMING_SNAKE_CASE_					      =							jax.device_count()
								SCREAMING_SNAKE_CASE_					      =							num_samples * [prompt]
								SCREAMING_SNAKE_CASE_					      =							pipeline.prepare_inputs(a_  )

								# shard inputs and rng
								SCREAMING_SNAKE_CASE_					      =							replicate(a_  )
								SCREAMING_SNAKE_CASE_					      =							jax.random.split(a_					,							a_  )
								SCREAMING_SNAKE_CASE_					      =							shard(a_  )

								SCREAMING_SNAKE_CASE_					      =							pipeline(a_					,							a_					,							a_					,							a_					,							jit=a_  ).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_51_47_45  ) < 1e-3
										assert np.abs(np.abs(a_					,							dtype=np.floataa  ).sum() - 4_99_47.8_75  ) < 5e-1

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



						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							FlaxStableDiffusionPipeline.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'					,							revision='flax'					,							safety_checker=a_  )

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

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

								SCREAMING_SNAKE_CASE_					      =							jax.device_count()
								SCREAMING_SNAKE_CASE_					      =							num_samples * [prompt]
								SCREAMING_SNAKE_CASE_					      =							pipeline.prepare_inputs(a_  )

								# shard inputs and rng
								SCREAMING_SNAKE_CASE_					      =							replicate(a_  )
								SCREAMING_SNAKE_CASE_					      =							jax.random.split(a_					,							a_  )
								SCREAMING_SNAKE_CASE_					      =							shard(a_  )

								SCREAMING_SNAKE_CASE_					      =							pipeline(a_					,							a_					,							a_					,							a_					,							jit=a_  ).images

								assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
								if jax.device_count() == 8:
										assert np.abs((np.abs(images[0, 0, :2, :2, -2:]					,							dtype=np.floataa  ).sum() - 0.05_65_24_01)  ) < 1e-3
										assert np.abs((np.abs(a_					,							dtype=np.floataa  ).sum() - 2_38_38_08.2)  ) < 5e-1



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							FlaxStableDiffusionPipeline.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'					,							revision='bf16'					,							dtype=jnp.bfloataa					,							safety_checker=a_  )

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

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

								SCREAMING_SNAKE_CASE_					      =							jax.device_count()
								SCREAMING_SNAKE_CASE_					      =							num_samples * [prompt]
								SCREAMING_SNAKE_CASE_					      =							pipeline.prepare_inputs(a_  )

								# shard inputs and rng
								SCREAMING_SNAKE_CASE_					      =							replicate(a_  )
								SCREAMING_SNAKE_CASE_					      =							jax.random.split(a_					,							a_  )
								SCREAMING_SNAKE_CASE_					      =							shard(a_  )

								SCREAMING_SNAKE_CASE_					      =							pipeline(a_					,							a_					,							a_					,							a_					,							jit=a_  ).images

								assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
								if jax.device_count() == 8:
										assert np.abs((np.abs(images[0, 0, :2, :2, -2:]					,							dtype=np.floataa  ).sum() - 0.04_00_39_06)  ) < 1e-3
										assert np.abs((np.abs(a_					,							dtype=np.floataa  ).sum() - 2_37_35_16.75)  ) < 5e-1



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							FlaxStableDiffusionPipeline.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'					,							revision='bf16'					,							dtype=jnp.bfloataa  )

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

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

								SCREAMING_SNAKE_CASE_					      =							jax.device_count()
								SCREAMING_SNAKE_CASE_					      =							num_samples * [prompt]
								SCREAMING_SNAKE_CASE_					      =							pipeline.prepare_inputs(a_  )

								# shard inputs and rng
								SCREAMING_SNAKE_CASE_					      =							replicate(a_  )
								SCREAMING_SNAKE_CASE_					      =							jax.random.split(a_					,							a_  )
								SCREAMING_SNAKE_CASE_					      =							shard(a_  )

								SCREAMING_SNAKE_CASE_					      =							pipeline(a_					,							a_					,							a_					,							a_					,							jit=a_  ).images

								assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
								if jax.device_count() == 8:
										assert np.abs((np.abs(images[0, 0, :2, :2, -2:]					,							dtype=np.floataa  ).sum() - 0.04_00_39_06)  ) < 1e-3
										assert np.abs((np.abs(a_					,							dtype=np.floataa  ).sum() - 2_37_35_16.75)  ) < 5e-1



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							FlaxDDIMScheduler(
								    beta_start=0.0_00_85					,							beta_end=0.0_12					,							beta_schedule='scaled_linear'					,							set_alpha_to_one=a_					,							steps_offset=1					,							)

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							FlaxStableDiffusionPipeline.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'					,							revision='bf16'					,							dtype=jnp.bfloataa					,							scheduler=a_					,							safety_checker=a_					,							)
								SCREAMING_SNAKE_CASE_					      =							scheduler.create_state()

								SCREAMING_SNAKE_CASE_					      =							scheduler_state

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

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

								SCREAMING_SNAKE_CASE_					      =							jax.device_count()
								SCREAMING_SNAKE_CASE_					      =							num_samples * [prompt]
								SCREAMING_SNAKE_CASE_					      =							pipeline.prepare_inputs(a_  )

								# shard inputs and rng
								SCREAMING_SNAKE_CASE_					      =							replicate(a_  )
								SCREAMING_SNAKE_CASE_					      =							jax.random.split(a_					,							a_  )
								SCREAMING_SNAKE_CASE_					      =							shard(a_  )

								SCREAMING_SNAKE_CASE_					      =							pipeline(a_					,							a_					,							a_					,							a_					,							jit=a_  ).images

								assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
								if jax.device_count() == 8:
										assert np.abs((np.abs(images[0, 0, :2, :2, -2:]					,							dtype=np.floataa  ).sum() - 0.0_45_04_39_45)  ) < 1e-3
										assert np.abs((np.abs(a_					,							dtype=np.floataa  ).sum() - 2_34_76_93.5)  ) < 5e-1






						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

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

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

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							FlaxStableDiffusionPipeline.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'					,							revision='bf16'					,							dtype=jnp.bfloataa					,							safety_checker=a_					,							)

								SCREAMING_SNAKE_CASE_					      =							replicate(a_  )
								SCREAMING_SNAKE_CASE_					      =							pipeline.prepare_inputs(a_  )
								SCREAMING_SNAKE_CASE_					      =							shard(a_  )
								SCREAMING_SNAKE_CASE_					      =							pipeline(a_					,							a_					,							a_					,							jit=a_  ).images
								assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
								SCREAMING_SNAKE_CASE_					      =							images[2, 0, 2_56, 10:17, 1]

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

								SCREAMING_SNAKE_CASE_					      =							replicate(a_  )
								SCREAMING_SNAKE_CASE_					      =							pipeline.prepare_inputs(a_  )
								SCREAMING_SNAKE_CASE_					      =							shard(a_  )
								SCREAMING_SNAKE_CASE_					      =							pipeline(a_					,							a_					,							a_					,							jit=a_  ).images
								assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
								SCREAMING_SNAKE_CASE_					      =							images[2, 0, 2_56, 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


'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a					:			str							=    logging.get_logger(__name__)

_a					:			Dict							=    {
    'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			List[Any]     	= "cvt"
  def __init__(  self ,			a_=3 ,			a_=[7, 3, 3] ,			a_=[4, 2, 2] ,			a_=[2, 1, 1] ,			a_=[6_4, 1_9_2, 3_8_4] ,			a_=[1, 3, 6] ,			a_=[1, 2, 1_0] ,			a_=[4.0, 4.0, 4.0] ,			a_=[0.0, 0.0, 0.0] ,			a_=[0.0, 0.0, 0.0] ,			a_=[0.0, 0.0, 0.1] ,			a_=[True, True, True] ,			a_=[False, False, True] ,			a_=["dw_bn", "dw_bn", "dw_bn"] ,			a_=[3, 3, 3] ,			a_=[1, 1, 1] ,			a_=[2, 2, 2] ,			a_=[1, 1, 1] ,			a_=[1, 1, 1] ,			a_=0.02 ,			a_=1E-12 ,			**a_ ,			)				->					Union[str, Any]:


    """simple docstring"""
    super().__init__(**a_  )
    UpperCAmelCase    				=						num_channels
    UpperCAmelCase    				=						patch_sizes
    UpperCAmelCase    				=						patch_stride
    UpperCAmelCase    				=						patch_padding
    UpperCAmelCase    				=						embed_dim
    UpperCAmelCase    				=						num_heads
    UpperCAmelCase    				=						depth
    UpperCAmelCase    				=						mlp_ratio
    UpperCAmelCase    				=						attention_drop_rate
    UpperCAmelCase    				=						drop_rate
    UpperCAmelCase    				=						drop_path_rate
    UpperCAmelCase    				=						qkv_bias
    UpperCAmelCase    				=						cls_token
    UpperCAmelCase    				=						qkv_projection_method
    UpperCAmelCase    				=						kernel_qkv
    UpperCAmelCase    				=						padding_kv
    UpperCAmelCase    				=						stride_kv
    UpperCAmelCase    				=						padding_q
    UpperCAmelCase    				=						stride_q
    UpperCAmelCase    				=						initializer_range
    UpperCAmelCase    				=						layer_norm_eps


