


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 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      str:
             with tempfile.TemporaryDirectory() as tmpdirname:
                    # pipeline has Flax weights
                    a_ :		Optional[Any]							=  FlaxDiffusionPipeline.from_pretrained(
                        'hf-internal-testing/tiny-stable-diffusion-pipe'					,							safety_checker=SCREAMING_SNAKE_CASE__					,							cache_dir=SCREAMING_SNAKE_CASE__					)

                    a_ :		Optional[Any]							=  [t[-1] for t in os.walk(os.path.join(SCREAMING_SNAKE_CASE__					,							os.listdir(SCREAMING_SNAKE_CASE__					)[0]					,							'snapshots'					)					)]
                    a_ :		int							=  [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 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Any:
             a_							,		a_ :		Optional[int]							=  FlaxStableDiffusionPipeline.from_pretrained(
                 'hf-internal-testing/tiny-stable-diffusion-pipe'					,							safety_checker=SCREAMING_SNAKE_CASE__					)

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

             a_ :		Any							=  jax.random.PRNGKey(0					)
             a_ :		Union[str, Any]							=  4

             a_ :		Optional[Any]							=  jax.device_count()
             a_ :		List[Any]							=  num_samples * [prompt]
             a_ :		int							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)

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

             a_ :		int							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images

             assert images.shape == (num_samples, 1, 6_4, 6_4, 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(SCREAMING_SNAKE_CASE__					,							dtype=np.floataa					).sum() - 49947.875					) < 5E-1

             a_ :		Optional[Any]							=  pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]					)					)					)
             assert len(SCREAMING_SNAKE_CASE__					) == num_samples
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Union[str, Any]:
             a_							,		a_ :		Optional[int]							=  FlaxStableDiffusionPipeline.from_pretrained(
                 'CompVis/stable-diffusion-v1-4'					,							revision='flax'					,							safety_checker=SCREAMING_SNAKE_CASE__					)

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

             a_ :		List[Any]							=  jax.random.PRNGKey(0					)
             a_ :		str							=  5_0

             a_ :		int							=  jax.device_count()
             a_ :		Tuple							=  num_samples * [prompt]
             a_ :		Any							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)

             # shard inputs and rng
             a_ :		int							=  replicate(SCREAMING_SNAKE_CASE__					)
             a_ :		int							=  jax.random.split(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
             a_ :		Tuple							=  shard(SCREAMING_SNAKE_CASE__					)

             a_ :		Tuple							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images

             assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 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(SCREAMING_SNAKE_CASE__					,							dtype=np.floataa					).sum() - 2383808.2)					) < 5E-1
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Tuple:
             a_							,		a_ :		Tuple							=  FlaxStableDiffusionPipeline.from_pretrained(
                 'CompVis/stable-diffusion-v1-4'					,							revision='bf16'					,							dtype=jnp.bfloataa					,							safety_checker=SCREAMING_SNAKE_CASE__					)

             a_ :		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'
             )

             a_ :		Optional[Any]							=  jax.random.PRNGKey(0					)
             a_ :		List[str]							=  5_0

             a_ :		Union[str, Any]							=  jax.device_count()
             a_ :		Tuple							=  num_samples * [prompt]
             a_ :		int							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)

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

             a_ :		List[Any]							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images

             assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 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(SCREAMING_SNAKE_CASE__					,							dtype=np.floataa					).sum() - 2373516.75)					) < 5E-1
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      int:
             a_							,		a_ :		List[str]							=  FlaxStableDiffusionPipeline.from_pretrained(
                 'CompVis/stable-diffusion-v1-4'					,							revision='bf16'					,							dtype=jnp.bfloataa					)

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

             a_ :		Optional[int]							=  jax.random.PRNGKey(0					)
             a_ :		List[str]							=  5_0

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

             # shard inputs and rng
             a_ :		Tuple							=  replicate(SCREAMING_SNAKE_CASE__					)
             a_ :		List[str]							=  jax.random.split(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
             a_ :		Dict							=  shard(SCREAMING_SNAKE_CASE__					)

             a_ :		Optional[int]							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images

             assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 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(SCREAMING_SNAKE_CASE__					,							dtype=np.floataa					).sum() - 2373516.75)					) < 5E-1
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Any:
             a_ :		Union[str, Any]							=  FlaxDDIMScheduler(
                 beta_start=0.00085					,							beta_end=0.012					,							beta_schedule='scaled_linear'					,							set_alpha_to_one=SCREAMING_SNAKE_CASE__					,							steps_offset=1					,							)

             a_							,		a_ :		int							=  FlaxStableDiffusionPipeline.from_pretrained(
                 'CompVis/stable-diffusion-v1-4'					,							revision='bf16'					,							dtype=jnp.bfloataa					,							scheduler=SCREAMING_SNAKE_CASE__					,							safety_checker=SCREAMING_SNAKE_CASE__					,							)
             a_ :		Union[str, Any]							=  scheduler.create_state()

             a_ :		str							=  scheduler_state

             a_ :		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'
             )

             a_ :		str							=  jax.random.PRNGKey(0					)
             a_ :		List[Any]							=  5_0

             a_ :		List[str]							=  jax.device_count()
             a_ :		Dict							=  num_samples * [prompt]
             a_ :		Tuple							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)

             # shard inputs and rng
             a_ :		List[Any]							=  replicate(SCREAMING_SNAKE_CASE__					)
             a_ :		Union[str, Any]							=  jax.random.split(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
             a_ :		Dict							=  shard(SCREAMING_SNAKE_CASE__					)

             a_ :		Optional[Any]							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images

             assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 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(SCREAMING_SNAKE_CASE__					,							dtype=np.floataa					).sum() - 2347693.5)					) < 5E-1

      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Union[str, Any]:
             a_ :		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'
             )

             a_ :		Dict							=  jax.device_count()
             a_ :		Optional[int]							=  num_samples * [prompt]
             a_ :		str							=  jax.random.split(jax.random.PRNGKey(0					)					,							SCREAMING_SNAKE_CASE__					)

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

             a_ :		Dict							=  replicate(SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)
             a_ :		int							=  shard(SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images
             assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
             a_ :		Optional[int]							=  images[2, 0, 2_5_6, 1_0:1_7, 1]

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

             a_ :		Any							=  replicate(SCREAMING_SNAKE_CASE__					)
             a_ :		int							=  pipeline.prepare_inputs(SCREAMING_SNAKE_CASE__					)
             a_ :		Dict							=  shard(SCREAMING_SNAKE_CASE__					)
             a_ :		Union[str, Any]							=  pipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							jit=SCREAMING_SNAKE_CASE__					).images
             assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
             a_ :		str							=  images[2, 0, 2_5_6, 1_0:1_7, 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 os
from collections import deque

import torch
from torch.utils.data import Dataset
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[Any]=""					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="train"					)		->      Tuple:
             assert os.path.isdir(SCREAMING_SNAKE_CASE__					)

             a_ :		int							=  []
             a_ :		Optional[int]							=  os.listdir(SCREAMING_SNAKE_CASE__					)
             for story_filename in story_filenames_list:
                    if "summary" in story_filename:
                           continue
                    a_ :		List[str]							=  os.path.join(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
                    if not os.path.isfile(SCREAMING_SNAKE_CASE__					):
                           continue
                    self.documents.append(SCREAMING_SNAKE_CASE__					)
      def __len__(	self    :	Dict					)		->      str:
             return len(self.documents					)

      def __getitem__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	str					)		->      str:
             a_ :		int							=  self.documents[idx]
             a_ :		Tuple							=  document_path.split('/'					)[-1]
             with open(SCREAMING_SNAKE_CASE__					,							encoding='utf-8'					) as source:
                    a_ :		Dict							=  source.read()
                    a_							,		a_ :		Optional[Any]							=  process_story(SCREAMING_SNAKE_CASE__					)
             return document_name, story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[Any]							=  list(filter(lambda __A    : len(__A    ) != 0  ,   [line.strip() for line in raw_story.split('\n'    )]    )    )

       # for some unknown reason some lines miss a period, add it
       a_ :		List[Any]							=  [_add_missing_period(__A    ) for line in nonempty_lines]

       # gather article lines
       a_ :		int							=  []
       a_ :		List[Any]							=  deque(__A    )
       while True:
              try:
                     a_ :		Dict							=  lines.popleft()
                     if element.startswith('@highlight'    ):
                            break
                     story_lines.append(__A    )
              except IndexError:
                     # if "@highlight" is absent from the file we pop
                     # all elements until there is None, raising an exception.
                     return story_lines, []

    # gather summary lines
       a_ :		List[str]							=  list(filter(lambda __A    : not t.startswith('@highlight'    )  ,   __A    )    )

       return story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]    )     ->		Any:

       """simple docstring"""

       a_ :		Any							=  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
       if line.startswith('@highlight'    ):
              return line
       if line[-1] in END_TOKENS:
              return line
       return line + "."







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      Union[str, Any]  ,   __A			:      List[str]    )     ->		Union[str, Any]:

       """simple docstring"""

       if len(__A    ) > block_size:
              return sequence[:block_size]
       else:
              sequence.extend([pad_token_id] * (block_size - len(__A    ))    )
              return sequence







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      str    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  torch.ones_like(__A    )
       a_ :		List[str]							=  sequence == pad_token_id
       a_ :		str							=  0
       return mask







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]  ,   __A			:      Optional[Any]  ,   __A			:      Dict    )     ->		List[str]:

       """simple docstring"""

       a_ :		Optional[int]							=  [tokenizer.encode(__A    ) for line in story_lines]
       a_ :		int							=  [token for sentence in story_lines_token_ids for token in sentence]
       a_ :		Dict							=  [tokenizer.encode(__A    ) for line in summary_lines]
       a_ :		int							=  [token for sentence in summary_lines_token_ids for token in sentence]

       return story_token_ids, summary_token_ids







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      List[str]    )     ->		Optional[Any]:

       """simple docstring"""

       a_ :		int							=  []
       for sequence in batch:
              a_ :		int							=  -1
              a_ :		Dict							=  []
              for s in sequence:
                     if s == separator_token_id:
                            sentence_num += 1
                     embeddings.append(sentence_num % 2    )
              batch_embeddings.append(__A    )
       return torch.tensor(__A    )


