


'''simple docstring'''






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  __magic_name__							(		unittest.TestCase    ):
       UpperCAmelCase										=MODEL_FOR_MASKED_LM_MAPPING
       UpperCAmelCase										=TF_MODEL_FOR_MASKED_LM_MAPPING






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          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       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =pipeline(task='fill-mask'  ,							model='sshleifer/tiny-distilroberta-base'  ,							top_k=2  ,							framework='tf')
          _UpperCAmelCase       :       Tuple							   =unmasker('My name is <mask>')
          self.assertEqual(
              nested_simplify(snake_case  ,							decimals=6)  ,							[
                  {'sequence': 'My name is grouped', 'score': 2.1E-0_5, 'token': 3_8_0_1_5, 'token_str': ' grouped'},
                  {'sequence': 'My name is accuser', 'score': 2.1E-0_5, 'token': 2_5_5_0_6, 'token_str': ' accuser'},
              ]  ,							)

          _UpperCAmelCase       :       Optional[int]							   =unmasker('The largest city in France is <mask>')
          self.assertEqual(
              nested_simplify(snake_case  ,							decimals=6)  ,							[
                  {
                      'sequence': 'The largest city in France is grouped',
                      'score': 2.1E-0_5,
                      'token': 3_8_0_1_5,
                      'token_str': ' grouped',
                  },
                  {
                      'sequence': 'The largest city in France is accuser',
                      'score': 2.1E-0_5,
                      'token': 2_5_5_0_6,
                      'token_str': ' accuser',
                  },
              ]  ,							)

          _UpperCAmelCase       :       Optional[int]							   =unmasker('My name is <mask>'  ,							targets=[' Patrick', ' Clara', ' Teven']  ,							top_k=3)
          self.assertEqual(
              nested_simplify(snake_case  ,							decimals=6)  ,							[
                  {'sequence': 'My name is Clara', 'score': 2E-0_5, 'token': 1_3_6_0_6, 'token_str': ' Clara'},
                  {'sequence': 'My name is Patrick', 'score': 2E-0_5, 'token': 3_4_9_9, 'token_str': ' Patrick'},
                  {'sequence': 'My name is Te', 'score': 1.9E-0_5, 'token': 2_9_4_1, 'token_str': ' Te'},
              ]  ,							)






       @require_torch
       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =pipeline(task='fill-mask'  ,							model='sshleifer/tiny-distilroberta-base'  ,							top_k=2  ,							framework='pt')

          _UpperCAmelCase       :       List[Any]							   =unmasker('My name is <mask>')
          self.assertEqual(
              nested_simplify(snake_case  ,							decimals=6)  ,							[
                  {'sequence': 'My name is Maul', 'score': 2.2E-0_5, 'token': 3_5_6_7_6, 'token_str': ' Maul'},
                  {'sequence': 'My name isELS', 'score': 2.2E-0_5, 'token': 1_6_4_1_6, 'token_str': 'ELS'},
              ]  ,							)

          _UpperCAmelCase       :       Any							   =unmasker('The largest city in France is <mask>')
          self.assertEqual(
              nested_simplify(snake_case  ,							decimals=6)  ,							[
                  {
                      'sequence': 'The largest city in France is Maul',
                      'score': 2.2E-0_5,
                      'token': 3_5_6_7_6,
                      'token_str': ' Maul',
                  },
                  {'sequence': 'The largest city in France isELS', 'score': 2.2E-0_5, 'token': 1_6_4_1_6, 'token_str': 'ELS'},
              ]  ,							)

          _UpperCAmelCase       :       Optional[int]							   =unmasker('My name is <mask>'  ,							targets=[' Patrick', ' Clara', ' Teven']  ,							top_k=3)
          self.assertEqual(
              nested_simplify(snake_case  ,							decimals=6)  ,							[
                  {'sequence': 'My name is Patrick', 'score': 2.1E-0_5, 'token': 3_4_9_9, 'token_str': ' Patrick'},
                  {'sequence': 'My name is Te', 'score': 2E-0_5, 'token': 2_9_4_1, 'token_str': ' Te'},
                  {'sequence': 'My name is Clara', 'score': 2E-0_5, 'token': 1_3_6_0_6, 'token_str': ' Clara'},
              ]  ,							)

          _UpperCAmelCase       :       List[str]							   =unmasker('My name is <mask> <mask>'  ,							top_k=2)

          self.assertEqual(
              nested_simplify(snake_case  ,							decimals=6)  ,							[
                  [
                      {
                          'score': 2.2E-0_5,
                          'token': 3_5_6_7_6,
                          'token_str': ' Maul',
                          'sequence': '<s>My name is Maul<mask></s>',
                      },
                      {'score': 2.2E-0_5, 'token': 1_6_4_1_6, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
                  ],
                  [
                      {
                          'score': 2.2E-0_5,
                          'token': 3_5_6_7_6,
                          'token_str': ' Maul',
                          'sequence': '<s>My name is<mask> Maul</s>',
                      },
                      {'score': 2.2E-0_5, 'token': 1_6_4_1_6, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
                  ],
              ]  ,							)






       @require_torch_gpu
       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =pipeline('fill-mask'  ,							model='hf-internal-testing/tiny-random-distilbert'  ,							device=0  ,							framework='pt')

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

          _UpperCAmelCase       :       List[str]							   =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(snake_case  ,							snake_case)






       @slow
       @require_torch
       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =pipeline(task='fill-mask'  ,							model='distilroberta-base'  ,							top_k=2  ,							framework='pt')
          self.run_large_test(snake_case)






       @slow
       @require_tf
       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =pipeline(task='fill-mask'  ,							model='distilroberta-base'  ,							top_k=2  ,							framework='tf')
          self.run_large_test(snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =unmasker('My name is <mask>')
          self.assertEqual(
              nested_simplify(snake_case)  ,							[
                  {'sequence': 'My name is John', 'score': 0.0_08, 'token': 6_1_0, 'token_str': ' John'},
                  {'sequence': 'My name is Chris', 'score': 0.0_07, 'token': 1_5_7_3, 'token_str': ' Chris'},
              ]  ,							)
          _UpperCAmelCase       :       Optional[Any]							   =unmasker('The largest city in France is <mask>')
          self.assertEqual(
              nested_simplify(snake_case)  ,							[
                  {
                      'sequence': 'The largest city in France is Paris',
                      'score': 0.2_51,
                      'token': 2_2_0_1,
                      'token_str': ' Paris',
                  },
                  {
                      'sequence': 'The largest city in France is Lyon',
                      'score': 0.2_14,
                      'token': 1_2_7_9_0,
                      'token_str': ' Lyon',
                  },
              ]  ,							)

          _UpperCAmelCase       :       Dict							   =unmasker('My name is <mask>'  ,							targets=[' Patrick', ' Clara', ' Teven']  ,							top_k=3)
          self.assertEqual(
              nested_simplify(snake_case)  ,							[
                  {'sequence': 'My name is Patrick', 'score': 0.0_05, 'token': 3_4_9_9, 'token_str': ' Patrick'},
                  {'sequence': 'My name is Clara', 'score': 0.0_00, 'token': 1_3_6_0_6, 'token_str': ' Clara'},
                  {'sequence': 'My name is Te', 'score': 0.0_00, 'token': 2_9_4_1, 'token_str': ' Te'},
              ]  ,							)






       @require_torch
       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =pipeline(task='fill-mask'  ,							model='sshleifer/tiny-distilroberta-base'  ,							framework='pt')
          _UpperCAmelCase       :       Tuple							   =None
          _UpperCAmelCase       :       str							   =None
          self.run_pipeline_test(snake_case  ,							[])






       @require_tf
       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =pipeline(task='fill-mask'  ,							model='sshleifer/tiny-distilroberta-base'  ,							framework='tf')
          _UpperCAmelCase       :       Optional[Any]							   =None
          _UpperCAmelCase       :       Union[str, Any]							   =None
          self.run_pipeline_test(snake_case  ,							[])






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case)						->  Optional[int]:




          '''simple docstring'''
          if tokenizer is None or tokenizer.mask_token_id is None:
             self.skipTest('The provided tokenizer has no mask token, (probably reformer or wav2vec2)')

          _UpperCAmelCase       :       Any							   =FillMaskPipeline(model=snake_case  ,							tokenizer=snake_case)
          _UpperCAmelCase       :       Union[str, Any]							   =[
              f"This is another {tokenizer.mask_token} test",
          ]
          return fill_masker, examples






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =fill_masker.tokenizer
          _UpperCAmelCase       :       Dict							   =fill_masker.model

          _UpperCAmelCase       :       Dict							   =fill_masker(
              f"This is a {tokenizer.mask_token}"  ,							)
          self.assertEqual(
              snake_case  ,							[
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
              ]  ,							)

          _UpperCAmelCase       :       Tuple							   =fill_masker([f"This is a {tokenizer.mask_token}"])
          self.assertEqual(
              snake_case  ,							[
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
              ]  ,							)

          _UpperCAmelCase       :       Optional[int]							   =fill_masker([f"This is a {tokenizer.mask_token}", f"Another {tokenizer.mask_token} great test."])
          self.assertEqual(
              snake_case  ,							[
                  [
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  ],
                  [
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  ],
              ]  ,							)

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

          self.run_test_top_k(snake_case  ,							snake_case)
          self.run_test_targets(snake_case  ,							snake_case)
          self.run_test_top_k_targets(snake_case  ,							snake_case)
          self.fill_mask_with_duplicate_targets_and_top_k(snake_case  ,							snake_case)
          self.fill_mask_with_multiple_masks(snake_case  ,							snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =tokenizer.get_vocab()
          _UpperCAmelCase       :       int							   =sorted(vocab.keys())[:2]
          # Pipeline argument
          _UpperCAmelCase       :       Optional[int]							   =FillMaskPipeline(model=snake_case  ,							tokenizer=snake_case  ,							targets=snake_case)
          _UpperCAmelCase       :       Any							   =fill_masker(f"This is a {tokenizer.mask_token}")
          self.assertEqual(
              snake_case  ,							[
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
              ]  ,							)
          _UpperCAmelCase       :       Dict							   ={vocab[el] for el in targets}
          self.assertEqual({el['token'] for el in outputs}  ,							snake_case)
          _UpperCAmelCase       :       int							   =[tokenizer.decode([x]) for x in target_ids]
          self.assertEqual({el['token_str'] for el in outputs}  ,							set(snake_case))

          # Call argument
          _UpperCAmelCase       :       Any							   =FillMaskPipeline(model=snake_case  ,							tokenizer=snake_case)
          _UpperCAmelCase       :       Union[str, Any]							   =fill_masker(f"This is a {tokenizer.mask_token}"  ,							targets=snake_case)
          self.assertEqual(
              snake_case  ,							[
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
              ]  ,							)
          _UpperCAmelCase       :       str							   ={vocab[el] for el in targets}
          self.assertEqual({el['token'] for el in outputs}  ,							snake_case)
          _UpperCAmelCase       :       List[Any]							   =[tokenizer.decode([x]) for x in target_ids]
          self.assertEqual({el['token_str'] for el in outputs}  ,							set(snake_case))

          # Score equivalence
          _UpperCAmelCase       :       Tuple							   =fill_masker(f"This is a {tokenizer.mask_token}"  ,							targets=snake_case)
          _UpperCAmelCase       :       Tuple							   =[top_mask['token_str'] for top_mask in outputs]
          _UpperCAmelCase       :       Dict							   =[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(snake_case) == set(snake_case):
             _UpperCAmelCase       :       Tuple							   =fill_masker(f"This is a {tokenizer.mask_token}"  ,							targets=snake_case)
             _UpperCAmelCase       :       List[str]							   =[top_mask['score'] for top_mask in unmasked_targets]
             self.assertEqual(nested_simplify(snake_case)  ,							nested_simplify(snake_case))

          # Raises with invalid
          with self.assertRaises(snake_case):
             _UpperCAmelCase       :       Union[str, Any]							   =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(snake_case):
                _UpperCAmelCase       :       Any							   =fill_masker(f"This is a {tokenizer.mask_token}"  ,							targets=[''])
             with self.assertRaises(snake_case):
                _UpperCAmelCase       :       Union[str, Any]							   =fill_masker(f"This is a {tokenizer.mask_token}"  ,							targets='')






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   =FillMaskPipeline(model=snake_case  ,							tokenizer=snake_case  ,							top_k=2)
          _UpperCAmelCase       :       List[Any]							   =fill_masker(f"This is a {tokenizer.mask_token}")
          self.assertEqual(
              snake_case  ,							[
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
              ]  ,							)

          _UpperCAmelCase       :       int							   =FillMaskPipeline(model=snake_case  ,							tokenizer=snake_case)
          _UpperCAmelCase       :       Optional[Any]							   =fill_masker(f"This is a {tokenizer.mask_token}"  ,							top_k=2)
          self.assertEqual(
              snake_case  ,							[
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
              ]  ,							)
          self.assertEqual(nested_simplify(snake_case)  ,							nested_simplify(snake_case))






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =tokenizer.get_vocab()
          _UpperCAmelCase       :       str							   =FillMaskPipeline(model=snake_case  ,							tokenizer=snake_case)

          # top_k=2, ntargets=3
          _UpperCAmelCase       :       Any							   =sorted(vocab.keys())[:3]
          _UpperCAmelCase       :       Any							   =fill_masker(f"This is a {tokenizer.mask_token}"  ,							top_k=2  ,							targets=snake_case)

          # If we use the most probably targets, and filter differently, we should still
          # have the same results
          _UpperCAmelCase       :       List[str]							   =[el['token_str'] for el in sorted(snake_case  ,							key=lambda snake_case: x["score"]  ,							reverse=snake_case)]
          # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
          if set(snake_case).issubset(snake_case):
             _UpperCAmelCase       :       int							   =fill_masker(f"This is a {tokenizer.mask_token}"  ,							top_k=3  ,							targets=snake_case)
             # They should yield exactly the same result
             self.assertEqual(nested_simplify(snake_case)  ,							nested_simplify(snake_case))






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =FillMaskPipeline(model=snake_case  ,							tokenizer=snake_case)
          _UpperCAmelCase       :       Union[str, Any]							   =tokenizer.get_vocab()
          # String duplicates + id duplicates
          _UpperCAmelCase       :       Dict							   =sorted(vocab.keys())[:3]
          _UpperCAmelCase       :       List[str]							   =[targets[0], targets[1], targets[0], targets[2], targets[1]]
          _UpperCAmelCase       :       Dict							   =fill_masker(f"My name is {tokenizer.mask_token}"  ,							targets=snake_case  ,							top_k=1_0)

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






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   =FillMaskPipeline(model=snake_case  ,							tokenizer=snake_case)

          _UpperCAmelCase       :       Optional[int]							   =fill_masker(
              f"This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"  ,							top_k=2)
          self.assertEqual(
              snake_case  ,							[
                  [
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  ],
                  [
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  ],
                  [
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                      {'sequence': ANY(snake_case), 'score': ANY(snake_case), 'token': ANY(snake_case), 'token_str': ANY(snake_case)},
                  ],
              ]  ,							)





'''simple docstring'''






from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowercase							=logging.get_logger(__name__)  # pylint: disable=invalid-name






class  __magic_name__							(		lowerCAmelCase ,lowerCAmelCase    ):






       @register_to_config
       def __init__(      self  ,							snake_case  ,							snake_case = None  ,							snake_case = None)						->  Union[str, Any]:




          '''simple docstring'''
          super().__init__()

          _UpperCAmelCase       :       List[Any]							   =learnable

          if self.learnable:
             assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
             assert length is not None, "learnable=True requires `length` to be set"

             _UpperCAmelCase       :       str							   =torch.zeros(snake_case  ,							snake_case)
          else:
             _UpperCAmelCase       :       Tuple							   =None

          _UpperCAmelCase       :       int							   =torch.nn.Parameter(snake_case)






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42






       def __init__(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							)						->  Tuple:




          '''simple docstring'''
          super().__init__()

          self.register_modules(
              vqvae=snake_case  ,							transformer=snake_case  ,							text_encoder=snake_case  ,							tokenizer=snake_case  ,							scheduler=snake_case  ,							learned_classifier_free_sampling_embeddings=snake_case  ,							)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =len(snake_case) if isinstance(snake_case  ,							snake_case) else 1

          # get prompt text embeddings
          _UpperCAmelCase       :       Optional[Any]							   =self.tokenizer(
              snake_case  ,							padding='max_length'  ,							max_length=self.tokenizer.model_max_length  ,							return_tensors='pt'  ,							)
          _UpperCAmelCase       :       Union[str, Any]							   =text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             _UpperCAmelCase       :       str							   =self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
             logger.warning(
                 'The following part of your input was truncated because CLIP can only handle sequences up to'
                 f" {self.tokenizer.model_max_length} tokens: {removed_text}")
             _UpperCAmelCase       :       Union[str, Any]							   =text_input_ids[:, : self.tokenizer.model_max_length]
          _UpperCAmelCase       :       Optional[int]							   =self.text_encoder(text_input_ids.to(self.device))[0]

          # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
          # While CLIP does normalize the pooled output of the text transformer when combining
          # the image and text embeddings, CLIP does not directly normalize the last hidden state.
          #
          # CLIP normalizing the pooled output.
          # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
          _UpperCAmelCase       :       List[str]							   =prompt_embeds / prompt_embeds.norm(dim=-1  ,							keepdim=snake_case)

          # duplicate text embeddings for each generation per prompt
          _UpperCAmelCase       :       Optional[Any]							   =prompt_embeds.repeat_interleave(snake_case  ,							dim=0)

          if do_classifier_free_guidance:
             if self.learned_classifier_free_sampling_embeddings.learnable:
                _UpperCAmelCase       :       Dict							   =self.learned_classifier_free_sampling_embeddings.embeddings
                _UpperCAmelCase       :       Any							   =negative_prompt_embeds.unsqueeze(0).repeat(snake_case  ,							1  ,							1)
             else:
                _UpperCAmelCase       :       str							   =[''] * batch_size

                _UpperCAmelCase       :       Dict							   =text_input_ids.shape[-1]
                _UpperCAmelCase       :       str							   =self.tokenizer(
                    snake_case  ,							padding='max_length'  ,							max_length=snake_case  ,							truncation=snake_case  ,							return_tensors='pt'  ,							)
                _UpperCAmelCase       :       str							   =self.text_encoder(uncond_input.input_ids.to(self.device))[0]
                # See comment for normalizing text embeddings
                _UpperCAmelCase       :       Tuple							   =negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1  ,							keepdim=snake_case)

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             _UpperCAmelCase       :       int							   =negative_prompt_embeds.shape[1]
             _UpperCAmelCase       :       List[str]							   =negative_prompt_embeds.repeat(1  ,							snake_case  ,							1)
             _UpperCAmelCase       :       Optional[int]							   =negative_prompt_embeds.view(batch_size * num_images_per_prompt  ,							snake_case  ,							-1)

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             _UpperCAmelCase       :       str							   =torch.cat([negative_prompt_embeds, prompt_embeds])

          return prompt_embeds






       @torch.no_grad()
       def __call__(      self  ,							snake_case  ,							snake_case = 1_0_0  ,							snake_case = 5.0  ,							snake_case = 1.0  ,							snake_case = 1  ,							snake_case = None  ,							snake_case = None  ,							snake_case = "pil"  ,							snake_case = True  ,							snake_case = None  ,							snake_case = 1  ,							)						->  Union[ImagePipelineOutput, Tuple]:




          '''simple docstring'''
          if isinstance(snake_case  ,							snake_case):
             _UpperCAmelCase       :       Tuple							   =1
          elif isinstance(snake_case  ,							snake_case):
             _UpperCAmelCase       :       int							   =len(snake_case)
          else:
             raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(snake_case)}")

          _UpperCAmelCase       :       Optional[Any]							   =batch_size * num_images_per_prompt

          _UpperCAmelCase       :       Union[str, Any]							   =guidance_scale > 1.0

          _UpperCAmelCase       :       Any							   =self._encode_prompt(snake_case  ,							snake_case  ,							snake_case)

          if (callback_steps is None) or (
              callback_steps is not None and (not isinstance(snake_case  ,							snake_case) or callback_steps <= 0)
          ):
             raise ValueError(
                 f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                 f" {type(snake_case)}.")

          # get the initial completely masked latents unless the user supplied it

          _UpperCAmelCase       :       List[Any]							   =(batch_size, self.transformer.num_latent_pixels)
          if latents is None:
             _UpperCAmelCase       :       Optional[Any]							   =self.transformer.num_vector_embeds - 1
             _UpperCAmelCase       :       Optional[int]							   =torch.full(snake_case  ,							snake_case).to(self.device)
          else:
             if latents.shape != latents_shape:
                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
             if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                raise ValueError(
                    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
                    f" {self.transformer.num_vector_embeds - 1} (inclusive).")
             _UpperCAmelCase       :       Optional[Any]							   =latents.to(self.device)

          # set timesteps
          self.scheduler.set_timesteps(snake_case  ,							device=self.device)

          _UpperCAmelCase       :       int							   =self.scheduler.timesteps.to(self.device)

          _UpperCAmelCase       :       Dict							   =latents

          for i, t in enumerate(self.progress_bar(snake_case)):
             # expand the sample if we are doing classifier free guidance
             _UpperCAmelCase       :       Union[str, Any]							   =torch.cat([sample] * 2) if do_classifier_free_guidance else sample

             # predict the un-noised image
             # model_output == `log_p_x_0`
             _UpperCAmelCase       :       Optional[Any]							   =self.transformer(snake_case  ,							encoder_hidden_states=snake_case  ,							timestep=snake_case).sample

             if do_classifier_free_guidance:
                _UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =model_output.chunk(2)
                _UpperCAmelCase       :       Dict							   =model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                model_output -= torch.logsumexp(snake_case  ,							dim=1  ,							keepdim=snake_case)

             _UpperCAmelCase       :       Any							   =self.truncate(snake_case  ,							snake_case)

             # remove `log(0)`'s (`-inf`s)
             _UpperCAmelCase       :       int							   =model_output.clamp(-7_0)

             # compute the previous noisy sample x_t -> x_t-1
             _UpperCAmelCase       :       List[Any]							   =self.scheduler.step(snake_case  ,							timestep=snake_case  ,							sample=snake_case  ,							generator=snake_case).prev_sample

             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
                callback(snake_case  ,							snake_case  ,							snake_case)

          _UpperCAmelCase       :       List[str]							   =self.vqvae.config.vq_embed_dim
          _UpperCAmelCase       :       Optional[int]							   =(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
          _UpperCAmelCase       :       int							   =self.vqvae.quantize.get_codebook_entry(snake_case  ,							shape=snake_case)
          _UpperCAmelCase       :       str							   =self.vqvae.decode(snake_case  ,							force_not_quantize=snake_case).sample

          _UpperCAmelCase       :       str							   =(image / 2 + 0.5).clamp(0  ,							1)
          _UpperCAmelCase       :       Tuple							   =image.cpu().permute(0  ,							2  ,							3  ,							1).numpy()

          if output_type == "pil":
             _UpperCAmelCase       :       Optional[int]							   =self.numpy_to_pil(snake_case)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  torch.FloatTensor:




          '''simple docstring'''
          _UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =torch.sort(snake_case  ,							1  ,							descending=snake_case)
          _UpperCAmelCase       :       Dict							   =torch.exp(snake_case)
          _UpperCAmelCase       :       str							   =sorted_p_x_0.cumsum(dim=1) < truncation_rate

          # Ensure that at least the largest probability is not zeroed out
          _UpperCAmelCase       :       Optional[int]							   =torch.full_like(keep_mask[:, 0:1, :]  ,							snake_case)
          _UpperCAmelCase       :       Any							   =torch.cat((all_true, keep_mask)  ,							dim=1)
          _UpperCAmelCase       :       Dict							   =keep_mask[:, :-1, :]

          _UpperCAmelCase       :       Any							   =keep_mask.gather(1  ,							indices.argsort(1))

          _UpperCAmelCase       :       str							   =log_p_x_0.clone()

          _UpperCAmelCase       :       Any							   =-torch.inf  # -inf = log(0)

          return rv

