





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowercase  :   Dict						      =logging.get_logger(__name__)


__lowercase  :   Optional[Any]						      ={"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

__lowercase  :   Optional[Any]						      ={
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

__lowercase  :   Dict						      ={
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     a__ (					):






   '''simple docstring'''






   UpperCAmelCase_											=(
       list(range(ord("!"  )  ,    ord("~"  ) + 1  )  ) + list(range(ord("¡"  )  ,    ord("¬"  ) + 1  )  ) + list(range(ord("®"  )  ,    ord("ÿ"  ) + 1  )  )
   )
   UpperCAmelCase_											=bs[:]
   UpperCAmelCase_											=0
   for b in range(2**8  ):
      if b not in bs:
         bs.append(lowercase__  )
         cs.append(2**8 + n  )
         n += 1
   UpperCAmelCase_											=[chr(lowercase__  ) for n in cs]
   return dict(zip(lowercase__  ,    lowercase__  )  )




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=set()
   UpperCAmelCase_											=word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)  )
      UpperCAmelCase_											=char
   return pairs





class      A       (	__lowercase						):
      _snake_case							     =VOCAB_FILES_NAMES
      _snake_case							     =PRETRAINED_VOCAB_FILES_MAP
      _snake_case							     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case							     =['''input_ids''', '''attention_mask''']



      def __init__(	self:						int		,  _lowerCAmelCase:						Optional[int]		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						Optional[Any]="replace"		,  _lowerCAmelCase:						Union[str, Any]="<s>"		,  _lowerCAmelCase:						int="</s>"		,  _lowerCAmelCase:						int="</s>"		,  _lowerCAmelCase:						List[Any]="<s>"		,  _lowerCAmelCase:						Tuple="<unk>"		,  _lowerCAmelCase:						Tuple="<pad>"		,  _lowerCAmelCase:						Dict="<mask>"		,  _lowerCAmelCase:						Any=False		,  **_lowerCAmelCase:						Any		,  )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=AddedToken(_lowerCAmelCase		,  lstrip=_lowerCAmelCase		,  rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) else bos_token
         UpperCAmelCase_											=AddedToken(_lowerCAmelCase		,  lstrip=_lowerCAmelCase		,  rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) else eos_token
         UpperCAmelCase_											=AddedToken(_lowerCAmelCase		,  lstrip=_lowerCAmelCase		,  rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) else sep_token
         UpperCAmelCase_											=AddedToken(_lowerCAmelCase		,  lstrip=_lowerCAmelCase		,  rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) else cls_token
         UpperCAmelCase_											=AddedToken(_lowerCAmelCase		,  lstrip=_lowerCAmelCase		,  rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) else unk_token
         UpperCAmelCase_											=AddedToken(_lowerCAmelCase		,  lstrip=_lowerCAmelCase		,  rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         UpperCAmelCase_											=AddedToken(_lowerCAmelCase		,  lstrip=_lowerCAmelCase		,  rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) else mask_token

         super().__init__(
             errors=_lowerCAmelCase		,  bos_token=_lowerCAmelCase		,  eos_token=_lowerCAmelCase		,  unk_token=_lowerCAmelCase		,  sep_token=_lowerCAmelCase		,  cls_token=_lowerCAmelCase		,  pad_token=_lowerCAmelCase		,  mask_token=_lowerCAmelCase		,  add_prefix_space=_lowerCAmelCase		,  **_lowerCAmelCase		,  )

         with open(_lowerCAmelCase		,  encoding="utf-8"       ) as vocab_handle:
            UpperCAmelCase_											=json.load(_lowerCAmelCase       )
         UpperCAmelCase_											={v: k for k, v in self.encoder.items()}
         UpperCAmelCase_											=errors  # how to handle errors in decoding
         UpperCAmelCase_											=bytes_to_unicode()
         UpperCAmelCase_											={v: k for k, v in self.byte_encoder.items()}
         with open(_lowerCAmelCase		,  encoding="utf-8"       ) as merges_handle:
            UpperCAmelCase_											=merges_handle.read().split("\n"       )[1:-1]
         UpperCAmelCase_											=[tuple(merge.split()       ) for merge in bpe_merges]
         UpperCAmelCase_											=dict(zip(_lowerCAmelCase		,  range(len(_lowerCAmelCase       )       )       )       )
         UpperCAmelCase_											={}
         UpperCAmelCase_											=add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         UpperCAmelCase_											=re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"       )



      @property
      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Tuple:
         '''simple docstring'''

         return len(self.encoder       )



      def 	lowerCAmelCase__    (	self:						str       )						->    Union[str, Any]:
         '''simple docstring'''

         return dict(self.encoder		,  **self.added_tokens_encoder       )



      def 	lowerCAmelCase__    (	self:						List[str]		,  _lowerCAmelCase:						List[str]       )						->    str:
         '''simple docstring'''

         if token in self.cache:
            return self.cache[token]
         UpperCAmelCase_											=tuple(_lowerCAmelCase       )
         UpperCAmelCase_											=get_pairs(_lowerCAmelCase       )

         if not pairs:
            return token

         while True:
            UpperCAmelCase_											=min(_lowerCAmelCase		,  key=lambda _lowerCAmelCase       : self.bpe_ranks.get(_lowerCAmelCase		,  float("inf"       )       )       )
            if bigram not in self.bpe_ranks:
               break
            UpperCAmelCase_						,       UpperCAmelCase_											=bigram
            UpperCAmelCase_											=[]
            UpperCAmelCase_											=0
            while i < len(_lowerCAmelCase       ):
               try:
                  UpperCAmelCase_											=word.index(_lowerCAmelCase		,  _lowerCAmelCase       )
               except ValueError:
                  new_word.extend(word[i:]       )
                  break
               else:
                  new_word.extend(word[i:j]       )
                  UpperCAmelCase_											=j

               if word[i] == first and i < len(_lowerCAmelCase       ) - 1 and word[i + 1] == second:
                  new_word.append(first + second       )
                  i += 2
               else:
                  new_word.append(word[i]       )
                  i += 1
            UpperCAmelCase_											=tuple(_lowerCAmelCase       )
            UpperCAmelCase_											=new_word
            if len(_lowerCAmelCase       ) == 1:
               break
            else:
               UpperCAmelCase_											=get_pairs(_lowerCAmelCase       )
         UpperCAmelCase_											=" ".join(_lowerCAmelCase       )
         UpperCAmelCase_											=word
         return word



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						Optional[Any]       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=[]
         for token in re.findall(self.pat		,  _lowerCAmelCase       ):
            UpperCAmelCase_											="".join(
                self.byte_encoder[b] for b in token.encode("utf-8"       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCAmelCase       ).split(" "       )       )
         return bpe_tokens



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						Union[str, Any]       )						->    str:
         '''simple docstring'''

         return self.encoder.get(_lowerCAmelCase		,  self.encoder.get(self.unk_token       )       )



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						Any       )						->    Optional[Any]:
         '''simple docstring'''

         return self.decoder.get(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						Dict       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											="".join(_lowerCAmelCase       )
         UpperCAmelCase_											=bytearray([self.byte_decoder[c] for c in text]       ).decode("utf-8"		,  errors=self.errors       )
         return text



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Optional[str] = None       )						->    Tuple[str]:
         '''simple docstring'''

         if not os.path.isdir(_lowerCAmelCase       ):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'       )
            return
         UpperCAmelCase_											=os.path.join(
             _lowerCAmelCase		,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
         UpperCAmelCase_											=os.path.join(
             _lowerCAmelCase		,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]       )

         with open(_lowerCAmelCase		,  "w"		,  encoding="utf-8"       ) as f:
            f.write(json.dumps(self.encoder		,  indent=2		,  sort_keys=_lowerCAmelCase		,  ensure_ascii=_lowerCAmelCase       ) + "\n"       )

         UpperCAmelCase_											=0
         with open(_lowerCAmelCase		,  "w"		,  encoding="utf-8"       ) as writer:
            writer.write("#version: 0.2\n"       )
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,  key=lambda _lowerCAmelCase       : kv[1]       ):
               if index != token_index:
                  logger.warning(
                      F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                      " Please check that the tokenizer is not corrupted!"       )
                  UpperCAmelCase_											=token_index
               writer.write(" ".join(_lowerCAmelCase       ) + "\n"       )
               index += 1

         return vocab_file, merge_file



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						List[int]		,  _lowerCAmelCase:						Optional[List[int]] = None       )						->    List[int]:
         '''simple docstring'''

         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCAmelCase_											=[self.cls_token_id]
         UpperCAmelCase_											=[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						List[int]		,  _lowerCAmelCase:						Optional[List[int]] = None		,  _lowerCAmelCase:						bool = False       )						->    List[int]:
         '''simple docstring'''

         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=_lowerCAmelCase		,  token_ids_a=_lowerCAmelCase		,  already_has_special_tokens=_lowerCAmelCase       )

         if token_ids_a is None:
            return [1] + ([0] * len(_lowerCAmelCase       )) + [1]
         return [1] + ([0] * len(_lowerCAmelCase       )) + [1, 1] + ([0] * len(_lowerCAmelCase       )) + [1]



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						List[int]		,  _lowerCAmelCase:						Optional[List[int]] = None       )						->    List[int]:
         '''simple docstring'''

         UpperCAmelCase_											=[self.sep_token_id]
         UpperCAmelCase_											=[self.cls_token_id]

         if token_ids_a is None:
            return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]






      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						List[str]=False		,  **_lowerCAmelCase:						Dict       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=kwargs.pop("add_prefix_space"		,  self.add_prefix_space       )
         if (is_split_into_words or add_prefix_space) and (len(_lowerCAmelCase       ) > 0 and not text[0].isspace()):
            UpperCAmelCase_											=" " + text
         return (text, kwargs)











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      A       (	unittest.TestCase						):



      def 	lowerCAmelCase__    (	self:						str       )						->    Union[str, Any]:
         '''simple docstring'''

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

            UpperCAmelCase_											=[t[-1] for t in os.walk(os.path.join(_lowerCAmelCase		,  os.listdir(_lowerCAmelCase       )[0]		,  "snapshots"       )       )]
            UpperCAmelCase_											=[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      A       (	unittest.TestCase						):



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "hf-internal-testing/tiny-stable-diffusion-pipe"		,  safety_checker=_lowerCAmelCase       )

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

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

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).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(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 4_99_47.8_75       ) < 5e-1

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



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="flax"		,  safety_checker=_lowerCAmelCase       )

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

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

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).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_65_24_01)       ) < 1e-3
            assert np.abs((np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 2_38_38_08.2)       ) < 5e-1



      def 	lowerCAmelCase__    (	self:						int       )						->    Union[str, Any]:
         '''simple docstring'''

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

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

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

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).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_00_39_06)       ) < 1e-3
            assert np.abs((np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 2_37_35_16.75)       ) < 5e-1



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Tuple:
         '''simple docstring'''

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

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

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

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).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_00_39_06)       ) < 1e-3
            assert np.abs((np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 2_37_35_16.75)       ) < 5e-1



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=FlaxDDIMScheduler(
             beta_start=0.0_00_85		,  beta_end=0.0_12		,  beta_schedule="scaled_linear"		,  set_alpha_to_one=_lowerCAmelCase		,  steps_offset=1		,  )

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

         UpperCAmelCase_											=scheduler_state

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

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

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).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() - 0.0_45_04_39_45)       ) < 1e-3
            assert np.abs((np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 2_34_76_93.5)       ) < 5e-1






      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    int:
         '''simple docstring'''

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

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

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

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

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

         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )
         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).images
         assert images_eff.shape == (num_samples, 1, 512, 512, 3)
         UpperCAmelCase_											=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




