
import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY




@is_pipeline_test
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      _A						:		Optional[int]    	=     MODEL_FOR_MASKED_LM_MAPPING
      _A						:		Dict    	=     TF_MODEL_FOR_MASKED_LM_MAPPING
      def A_			(			self ):
          super().tearDown()
          # clean-up as much as possible GPU memory occupied by PyTorch
          gc.collect()
          if is_torch_available():
              import torch

              torch.cuda.empty_cache()
      @require_tf
      def A_			(			self ):
          snake_case__						=				pipeline(task="fill-mask"		,	model="sshleifer/tiny-distilroberta-base"		,	top_k=2		,	framework="tf" )
          snake_case__						=				unmasker("My name is <mask>" )
          self.assertEqual(
              nested_simplify(lowerCamelCase		,	decimals=6 )		,	[
                  {"sequence": "My name is grouped", "score": 2.1e-05, "token": 3_80_15, "token_str": " grouped"},
                  {"sequence": "My name is accuser", "score": 2.1e-05, "token": 2_55_06, "token_str": " accuser"},
              ]		,	)

          snake_case__						=				unmasker("The largest city in France is <mask>" )
          self.assertEqual(
              nested_simplify(lowerCamelCase		,	decimals=6 )		,	[
                  {
                      "sequence": "The largest city in France is grouped",
                      "score": 2.1e-05,
                      "token": 3_80_15,
                      "token_str": " grouped",
                  },
                  {
                      "sequence": "The largest city in France is accuser",
                      "score": 2.1e-05,
                      "token": 2_55_06,
                      "token_str": " accuser",
                  },
              ]		,	)

          snake_case__						=				unmasker("My name is <mask>"		,	targets=[" Patrick", " Clara", " Teven"]		,	top_k=3 )
          self.assertEqual(
              nested_simplify(lowerCamelCase		,	decimals=6 )		,	[
                  {"sequence": "My name is Clara", "score": 2e-05, "token": 1_36_06, "token_str": " Clara"},
                  {"sequence": "My name is Patrick", "score": 2e-05, "token": 34_99, "token_str": " Patrick"},
                  {"sequence": "My name is Te", "score": 1.9e-05, "token": 29_41, "token_str": " Te"},
              ]		,	)
      @require_torch
      def A_			(			self ):
          snake_case__						=				pipeline(task="fill-mask"		,	model="sshleifer/tiny-distilroberta-base"		,	top_k=2		,	framework="pt" )

          snake_case__						=				unmasker("My name is <mask>" )
          self.assertEqual(
              nested_simplify(lowerCamelCase		,	decimals=6 )		,	[
                  {"sequence": "My name is Maul", "score": 2.2e-05, "token": 3_56_76, "token_str": " Maul"},
                  {"sequence": "My name isELS", "score": 2.2e-05, "token": 1_64_16, "token_str": "ELS"},
              ]		,	)

          snake_case__						=				unmasker("The largest city in France is <mask>" )
          self.assertEqual(
              nested_simplify(lowerCamelCase		,	decimals=6 )		,	[
                  {
                      "sequence": "The largest city in France is Maul",
                      "score": 2.2e-05,
                      "token": 3_56_76,
                      "token_str": " Maul",
                  },
                  {"sequence": "The largest city in France isELS", "score": 2.2e-05, "token": 1_64_16, "token_str": "ELS"},
              ]		,	)

          snake_case__						=				unmasker("My name is <mask>"		,	targets=[" Patrick", " Clara", " Teven"]		,	top_k=3 )
          self.assertEqual(
              nested_simplify(lowerCamelCase		,	decimals=6 )		,	[
                  {"sequence": "My name is Patrick", "score": 2.1e-05, "token": 34_99, "token_str": " Patrick"},
                  {"sequence": "My name is Te", "score": 2e-05, "token": 29_41, "token_str": " Te"},
                  {"sequence": "My name is Clara", "score": 2e-05, "token": 1_36_06, "token_str": " Clara"},
              ]		,	)

          snake_case__						=				unmasker("My name is <mask> <mask>"		,	top_k=2 )

          self.assertEqual(
              nested_simplify(lowerCamelCase		,	decimals=6 )		,	[
                  [
                      {
                          "score": 2.2e-05,
                          "token": 3_56_76,
                          "token_str": " Maul",
                          "sequence": "<s>My name is Maul<mask></s>",
                      },
                      {"score": 2.2e-05, "token": 1_64_16, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
                  ],
                  [
                      {
                          "score": 2.2e-05,
                          "token": 3_56_76,
                          "token_str": " Maul",
                          "sequence": "<s>My name is<mask> Maul</s>",
                      },
                      {"score": 2.2e-05, "token": 1_64_16, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
                  ],
              ]		,	)
      @require_torch_gpu
      def A_			(			self ):
          snake_case__						=				pipeline("fill-mask"		,	model="hf-internal-testing/tiny-random-distilbert"		,	device=0		,	framework="pt" )

          # convert model to fp16
          pipe.model.half()

          snake_case__						=				pipe("Paris is the [MASK] of France." )
          # We actually don't care about the result, we just want to make sure
          # it works, meaning the float16 tensor got casted back to float32
          # for postprocessing.
          self.assertIsInstance(lowerCamelCase		,	lowerCamelCase )
      @slow
      @require_torch
      def A_			(			self ):
          snake_case__						=				pipeline(task="fill-mask"		,	model="distilroberta-base"		,	top_k=2		,	framework="pt" )
          self.run_large_test(lowerCamelCase )
      @slow
      @require_tf
      def A_			(			self ):
          snake_case__						=				pipeline(task="fill-mask"		,	model="distilroberta-base"		,	top_k=2		,	framework="tf" )
          self.run_large_test(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				unmasker("My name is <mask>" )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[
                  {"sequence": "My name is John", "score": 0.0_0_8, "token": 6_10, "token_str": " John"},
                  {"sequence": "My name is Chris", "score": 0.0_0_7, "token": 15_73, "token_str": " Chris"},
              ]		,	)
          snake_case__						=				unmasker("The largest city in France is <mask>" )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[
                  {
                      "sequence": "The largest city in France is Paris",
                      "score": 0.2_5_1,
                      "token": 22_01,
                      "token_str": " Paris",
                  },
                  {
                      "sequence": "The largest city in France is Lyon",
                      "score": 0.2_1_4,
                      "token": 1_27_90,
                      "token_str": " Lyon",
                  },
              ]		,	)

          snake_case__						=				unmasker("My name is <mask>"		,	targets=[" Patrick", " Clara", " Teven"]		,	top_k=3 )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[
                  {"sequence": "My name is Patrick", "score": 0.0_0_5, "token": 34_99, "token_str": " Patrick"},
                  {"sequence": "My name is Clara", "score": 0.0_0_0, "token": 1_36_06, "token_str": " Clara"},
                  {"sequence": "My name is Te", "score": 0.0_0_0, "token": 29_41, "token_str": " Te"},
              ]		,	)
      @require_torch
      def A_			(			self ):
          snake_case__						=				pipeline(task="fill-mask"		,	model="sshleifer/tiny-distilroberta-base"		,	framework="pt" )
          snake_case__						=				None
          snake_case__						=				None
          self.run_pipeline_test(lowerCamelCase		,	[] )
      @require_tf
      def A_			(			self ):
          snake_case__						=				pipeline(task="fill-mask"		,	model="sshleifer/tiny-distilroberta-base"		,	framework="tf" )
          snake_case__						=				None
          snake_case__						=				None
          self.run_pipeline_test(lowerCamelCase		,	[] )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          if tokenizer is None or tokenizer.mask_token_id is None:
              self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)" )

          snake_case__						=				FillMaskPipeline(model=lowerCamelCase		,	tokenizer=lowerCamelCase )
          snake_case__						=				[
              F"""This is another {tokenizer.mask_token} test""",
          ]
          return fill_masker, examples
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				fill_masker.tokenizer
          snake_case__						=				fill_masker.model

          snake_case__						=				fill_masker(
              F"""This is a {tokenizer.mask_token}"""		,	)
          self.assertEqual(
              lowerCamelCase		,	[
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
              ]		,	)

          snake_case__						=				fill_masker([F"""This is a {tokenizer.mask_token}"""] )
          self.assertEqual(
              lowerCamelCase		,	[
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
              ]		,	)

          snake_case__						=				fill_masker([F"""This is a {tokenizer.mask_token}""", F"""Another {tokenizer.mask_token} great test."""] )
          self.assertEqual(
              lowerCamelCase		,	[
                  [
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  ],
                  [
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  ],
              ]		,	)

          with self.assertRaises(lowerCamelCase ):
              fill_masker([None] )
          # No mask_token is not supported
          with self.assertRaises(lowerCamelCase ):
              fill_masker("This is" )

          self.run_test_top_k(lowerCamelCase		,	lowerCamelCase )
          self.run_test_targets(lowerCamelCase		,	lowerCamelCase )
          self.run_test_top_k_targets(lowerCamelCase		,	lowerCamelCase )
          self.fill_mask_with_duplicate_targets_and_top_k(lowerCamelCase		,	lowerCamelCase )
          self.fill_mask_with_multiple_masks(lowerCamelCase		,	lowerCamelCase )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				tokenizer.get_vocab()
          snake_case__						=				sorted(vocab.keys() )[:2]
          # Pipeline argument
          snake_case__						=				FillMaskPipeline(model=lowerCamelCase		,	tokenizer=lowerCamelCase		,	targets=lowerCamelCase )
          snake_case__						=				fill_masker(F"""This is a {tokenizer.mask_token}""" )
          self.assertEqual(
              lowerCamelCase		,	[
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
              ]		,	)
          snake_case__						=				{vocab[el] for el in targets}
          self.assertEqual({el["token"] for el in outputs}		,	lowerCamelCase )
          snake_case__						=				[tokenizer.decode([x] ) for x in target_ids]
          self.assertEqual({el["token_str"] for el in outputs}		,	set(lowerCamelCase ) )

          # Call argument
          snake_case__						=				FillMaskPipeline(model=lowerCamelCase		,	tokenizer=lowerCamelCase )
          snake_case__						=				fill_masker(F"""This is a {tokenizer.mask_token}"""		,	targets=lowerCamelCase )
          self.assertEqual(
              lowerCamelCase		,	[
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
              ]		,	)
          snake_case__						=				{vocab[el] for el in targets}
          self.assertEqual({el["token"] for el in outputs}		,	lowerCamelCase )
          snake_case__						=				[tokenizer.decode([x] ) for x in target_ids]
          self.assertEqual({el["token_str"] for el in outputs}		,	set(lowerCamelCase ) )

          # Score equivalence
          snake_case__						=				fill_masker(F"""This is a {tokenizer.mask_token}"""		,	targets=lowerCamelCase )
          snake_case__						=				[top_mask["token_str"] for top_mask in outputs]
          snake_case__						=				[top_mask["score"] for top_mask in outputs]

          # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
          if set(lowerCamelCase ) == set(lowerCamelCase ):
              snake_case__						=				fill_masker(F"""This is a {tokenizer.mask_token}"""		,	targets=lowerCamelCase )
              snake_case__						=				[top_mask["score"] for top_mask in unmasked_targets]
              self.assertEqual(nested_simplify(lowerCamelCase )		,	nested_simplify(lowerCamelCase ) )

          # Raises with invalid
          with self.assertRaises(lowerCamelCase ):
              snake_case__						=				fill_masker(F"""This is a {tokenizer.mask_token}"""		,	targets=[] )
          # For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
          if "" not in tokenizer.get_vocab():
              with self.assertRaises(lowerCamelCase ):
                  snake_case__						=				fill_masker(F"""This is a {tokenizer.mask_token}"""		,	targets=[""] )
              with self.assertRaises(lowerCamelCase ):
                  snake_case__						=				fill_masker(F"""This is a {tokenizer.mask_token}"""		,	targets="" )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				FillMaskPipeline(model=lowerCamelCase		,	tokenizer=lowerCamelCase		,	top_k=2 )
          snake_case__						=				fill_masker(F"""This is a {tokenizer.mask_token}""" )
          self.assertEqual(
              lowerCamelCase		,	[
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
              ]		,	)

          snake_case__						=				FillMaskPipeline(model=lowerCamelCase		,	tokenizer=lowerCamelCase )
          snake_case__						=				fill_masker(F"""This is a {tokenizer.mask_token}"""		,	top_k=2 )
          self.assertEqual(
              lowerCamelCase		,	[
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
              ]		,	)
          self.assertEqual(nested_simplify(lowerCamelCase )		,	nested_simplify(lowerCamelCase ) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				tokenizer.get_vocab()
          snake_case__						=				FillMaskPipeline(model=lowerCamelCase		,	tokenizer=lowerCamelCase )

          # top_k=2, ntargets=3
          snake_case__						=				sorted(vocab.keys() )[:3]
          snake_case__						=				fill_masker(F"""This is a {tokenizer.mask_token}"""		,	top_k=2		,	targets=lowerCamelCase )

          # If we use the most probably targets, and filter differently, we should still
          # have the same results
          snake_case__						=				[el["token_str"] for el in sorted(lowerCamelCase		,	key=lambda lowerCamelCase : x["score"]		,	reverse=lowerCamelCase )]
          # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
          if set(lowerCamelCase ).issubset(lowerCamelCase ):
              snake_case__						=				fill_masker(F"""This is a {tokenizer.mask_token}"""		,	top_k=3		,	targets=lowerCamelCase )
              # They should yield exactly the same result
              self.assertEqual(nested_simplify(lowerCamelCase )		,	nested_simplify(lowerCamelCase ) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				FillMaskPipeline(model=lowerCamelCase		,	tokenizer=lowerCamelCase )
          snake_case__						=				tokenizer.get_vocab()
          # String duplicates + id duplicates
          snake_case__						=				sorted(vocab.keys() )[:3]
          snake_case__						=				[targets[0], targets[1], targets[0], targets[2], targets[1]]
          snake_case__						=				fill_masker(F"""My name is {tokenizer.mask_token}"""		,	targets=lowerCamelCase		,	top_k=10 )

          # The target list contains duplicates, so we can't output more
          # than them
          self.assertEqual(len(lowerCamelCase )		,	3 )



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				FillMaskPipeline(model=lowerCamelCase		,	tokenizer=lowerCamelCase )

          snake_case__						=				fill_masker(
              F"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""		,	top_k=2 )
          self.assertEqual(
              lowerCamelCase		,	[
                  [
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  ],
                  [
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  ],
                  [
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                      {"sequence": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase ), "token": ANY(lowerCamelCase ), "token_str": ANY(lowerCamelCase )},
                  ],
              ]		,	)





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__magic_name__            =  8





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase=BITS							):
    snake_case__						=				x.device

    snake_case__						=				(x * 255).int().clamp(0    ,		255							)

    snake_case__						=				2 ** torch.arange(bits - 1    ,		-1    ,		-1    ,		device=__lowerCAmelCase							)
    snake_case__						=				rearrange(__lowerCAmelCase    ,		"d -> d 1 1"							)
    snake_case__						=				rearrange(__lowerCAmelCase    ,		"b c h w -> b c 1 h w"							)

    snake_case__						=				((x & mask) != 0).float()
    snake_case__						=				rearrange(__lowerCAmelCase    ,		"b c d h w -> b (c d) h w"							)
    snake_case__						=				bits * 2 - 1
    return bits





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase=BITS							):
    snake_case__						=				x.device

    snake_case__						=				(x > 0).int()
    snake_case__						=				2 ** torch.arange(bits - 1    ,		-1    ,		-1    ,		device=__lowerCAmelCase    ,		dtype=torch.intaa							)

    snake_case__						=				rearrange(__lowerCAmelCase    ,		"d -> d 1 1"							)
    snake_case__						=				rearrange(__lowerCAmelCase    ,		"b (c d) h w -> b c d h w"    ,		d=8							)
    snake_case__						=				reduce(x * mask    ,		"b c d h w -> b c h w"    ,		"sum"							)
    return (dec / 255).clamp(0.0    ,		1.0							)





def 	SCREAMING_SNAKE_CASE__    (      self    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase = 0.0    ,		__lowerCAmelCase = True    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase = True    ,		):
    if self.num_inference_steps is None:
        raise ValueError(
            "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"							)

    # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
    # Ideally, read DDIM paper in-detail understanding

    # Notation (<variable name> -> <name in paper>
    # - pred_noise_t -> e_theta(x_t, t)
    # - pred_original_sample -> f_theta(x_t, t) or x_0
    # - std_dev_t -> sigma_t
    # - eta -> η
    # - pred_sample_direction -> "direction pointing to x_t"
    # - pred_prev_sample -> "x_t-1"

    # 1. get previous step value (=t-1)
    snake_case__						=				timestep - self.config.num_train_timesteps // self.num_inference_steps

    # 2. compute alphas, betas
    snake_case__						=				self.alphas_cumprod[timestep]
    snake_case__						=				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

    snake_case__						=				1 - alpha_prod_t

    # 3. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    snake_case__						=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

    # 4. Clip "predicted x_0"
    snake_case__						=				self.bit_scale
    if self.config.clip_sample:
        snake_case__						=				torch.clamp(__lowerCAmelCase    ,		-scale    ,		__lowerCAmelCase							)

    # 5. compute variance: "sigma_t(η)" -> see formula (16)
    # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
    snake_case__						=				self._get_variance(__lowerCAmelCase    ,		__lowerCAmelCase							)
    snake_case__						=				eta * variance ** 0.5

    if use_clipped_model_output:
        # the model_output is always re-derived from the clipped x_0 in Glide
        snake_case__						=				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

    # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    snake_case__						=				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

    # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    snake_case__						=				alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

    if eta > 0:
        # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
        snake_case__						=				model_output.device if torch.is_tensor(__lowerCAmelCase							) else "cpu"
        snake_case__						=				torch.randn(model_output.shape    ,		dtype=model_output.dtype    ,		generator=__lowerCAmelCase							).to(__lowerCAmelCase							)
        snake_case__						=				self._get_variance(__lowerCAmelCase    ,		__lowerCAmelCase							) ** 0.5 * eta * noise

        snake_case__						=				prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=__lowerCAmelCase    ,		pred_original_sample=__lowerCAmelCase							)





def 	SCREAMING_SNAKE_CASE__    (      self    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase="epsilon"    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase = True    ,		):
    snake_case__						=				timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
        snake_case__						,					snake_case__						=				torch.split(__lowerCAmelCase    ,		sample.shape[1]    ,		dim=1							)
    else:
        snake_case__						=				None

    # 1. compute alphas, betas
    snake_case__						=				self.alphas_cumprod[t]
    snake_case__						=				self.alphas_cumprod[t - 1] if t > 0 else self.one
    snake_case__						=				1 - alpha_prod_t
    snake_case__						=				1 - alpha_prod_t_prev

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if prediction_type == "epsilon":
        snake_case__						=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif prediction_type == "sample":
        snake_case__						=				model_output
    else:
        raise ValueError(F"""Unsupported prediction_type {prediction_type}."""							)

    # 3. Clip "predicted x_0"
    snake_case__						=				self.bit_scale
    if self.config.clip_sample:
        snake_case__						=				torch.clamp(__lowerCAmelCase    ,		-scale    ,		__lowerCAmelCase							)

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    snake_case__						=				(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
    snake_case__						=				self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    snake_case__						=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    snake_case__						=				0
    if t > 0:
        snake_case__						=				torch.randn(
            model_output.size()    ,		dtype=model_output.dtype    ,		layout=model_output.layout    ,		generator=__lowerCAmelCase							).to(model_output.device							)
        snake_case__						=				(self._get_variance(__lowerCAmelCase    ,		predicted_variance=__lowerCAmelCase							) ** 0.5) * noise

    snake_case__						=				pred_prev_sample + variance

    if not return_dict:
        return (pred_prev_sample,)

    return DDPMSchedulerOutput(prev_sample=__lowerCAmelCase    ,		pred_original_sample=__lowerCAmelCase							)





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase = 1.0		,	):
          super().__init__()
          snake_case__						=				bit_scale
          snake_case__						=				(
              ddim_bit_scheduler_step if isinstance(lowerCamelCase		,	lowerCamelCase ) else ddpm_bit_scheduler_step
          )

          self.register_modules(unet=lowerCamelCase		,	scheduler=lowerCamelCase )



      @torch.no_grad()
      def __call__(			self		,	lowerCamelCase = 2_56		,	lowerCamelCase = 2_56		,	lowerCamelCase = 50		,	lowerCamelCase = None		,	lowerCamelCase = 1		,	lowerCamelCase = "pil"		,	lowerCamelCase = True		,	**lowerCamelCase		,	):
          snake_case__						=				torch.randn(
              (batch_size, self.unet.config.in_channels, height, width)		,	generator=lowerCamelCase		,	)
          snake_case__						=				decimal_to_bits(lowerCamelCase ) * self.bit_scale
          snake_case__						=				latents.to(self.device )

          self.scheduler.set_timesteps(lowerCamelCase )

          for t in self.progress_bar(self.scheduler.timesteps ):
              # predict the noise residual
              snake_case__						=				self.unet(lowerCamelCase		,	lowerCamelCase ).sample

              # compute the previous noisy sample x_t -> x_t-1
              snake_case__						=				self.scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ).prev_sample

          snake_case__						=				bits_to_decimal(lowerCamelCase )

          if output_type == "pil":
              snake_case__						=				self.numpy_to_pil(lowerCamelCase )

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=lowerCamelCase )



