





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   __lowerCAmelCase			(					unittest.TestCase  ):
   _UpperCamelCase :						Tuple     		=				MODEL_FOR_MASKED_LM_MAPPING
   _UpperCamelCase :						Any     		=				TF_MODEL_FOR_MASKED_LM_MAPPING





   def 	_snake_case ( self      )				->  int:
      """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 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						List[Any]      		=  pipeline(task="fill-mask"					,			model="sshleifer/tiny-distilroberta-base"					,			top_k=2					,			framework="tf"      )
      a__	:						List[Any]      		=  unmasker("My name is <mask>"      )
      self.assertEqual(
          nested_simplify(__A					,			decimals=6      )					,			[
              {"sequence": "My name is grouped", "score": 2.1E-05, "token": 38_015, "token_str": " grouped"},
              {"sequence": "My name is accuser", "score": 2.1E-05, "token": 25_506, "token_str": " accuser"},
          ]					,			)

      a__	:						Optional[int]      		=  unmasker("The largest city in France is <mask>"      )
      self.assertEqual(
          nested_simplify(__A					,			decimals=6      )					,			[
              {
                  "sequence": "The largest city in France is grouped",
                  "score": 2.1E-05,
                  "token": 38_015,
                  "token_str": " grouped",
              },
              {
                  "sequence": "The largest city in France is accuser",
                  "score": 2.1E-05,
                  "token": 25_506,
                  "token_str": " accuser",
              },
          ]					,			)

      a__	:						str      		=  unmasker("My name is <mask>"					,			targets=[" Patrick", " Clara", " Teven"]					,			top_k=3      )
      self.assertEqual(
          nested_simplify(__A					,			decimals=6      )					,			[
              {"sequence": "My name is Clara", "score": 2E-05, "token": 13_606, "token_str": " Clara"},
              {"sequence": "My name is Patrick", "score": 2E-05, "token": 3_499, "token_str": " Patrick"},
              {"sequence": "My name is Te", "score": 1.9E-05, "token": 2_941, "token_str": " Te"},
          ]					,			)





   @require_torch
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						List[str]      		=  pipeline(task="fill-mask"					,			model="sshleifer/tiny-distilroberta-base"					,			top_k=2					,			framework="pt"      )

      a__	:						str      		=  unmasker("My name is <mask>"      )
      self.assertEqual(
          nested_simplify(__A					,			decimals=6      )					,			[
              {"sequence": "My name is Maul", "score": 2.2E-05, "token": 35_676, "token_str": " Maul"},
              {"sequence": "My name isELS", "score": 2.2E-05, "token": 16_416, "token_str": "ELS"},
          ]					,			)

      a__	:						Dict      		=  unmasker("The largest city in France is <mask>"      )
      self.assertEqual(
          nested_simplify(__A					,			decimals=6      )					,			[
              {
                  "sequence": "The largest city in France is Maul",
                  "score": 2.2E-05,
                  "token": 35_676,
                  "token_str": " Maul",
              },
              {"sequence": "The largest city in France isELS", "score": 2.2E-05, "token": 16_416, "token_str": "ELS"},
          ]					,			)

      a__	:						str      		=  unmasker("My name is <mask>"					,			targets=[" Patrick", " Clara", " Teven"]					,			top_k=3      )
      self.assertEqual(
          nested_simplify(__A					,			decimals=6      )					,			[
              {"sequence": "My name is Patrick", "score": 2.1E-05, "token": 3_499, "token_str": " Patrick"},
              {"sequence": "My name is Te", "score": 2E-05, "token": 2_941, "token_str": " Te"},
              {"sequence": "My name is Clara", "score": 2E-05, "token": 13_606, "token_str": " Clara"},
          ]					,			)

      a__	:						str      		=  unmasker("My name is <mask> <mask>"					,			top_k=2      )

      self.assertEqual(
          nested_simplify(__A					,			decimals=6      )					,			[
              [
                  {
                      "score": 2.2E-05,
                      "token": 35_676,
                      "token_str": " Maul",
                      "sequence": "<s>My name is Maul<mask></s>",
                  },
                  {"score": 2.2E-05, "token": 16_416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
              ],
              [
                  {
                      "score": 2.2E-05,
                      "token": 35_676,
                      "token_str": " Maul",
                      "sequence": "<s>My name is<mask> Maul</s>",
                  },
                  {"score": 2.2E-05, "token": 16_416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
              ],
          ]					,			)





   @require_torch_gpu
   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						str      		=  pipeline("fill-mask"					,			model="hf-internal-testing/tiny-random-distilbert"					,			device=0					,			framework="pt"      )

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

      a__	:						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(__A					,			__A      )





   @slow
   @require_torch
   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						List[Any]      		=  pipeline(task="fill-mask"					,			model="distilroberta-base"					,			top_k=2					,			framework="pt"      )
      self.run_large_test(__A      )





   @slow
   @require_tf
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						Tuple      		=  pipeline(task="fill-mask"					,			model="distilroberta-base"					,			top_k=2					,			framework="tf"      )
      self.run_large_test(__A      )





   def 	_snake_case ( self					,			snake_case      )				->  Any:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  unmasker("My name is <mask>"      )
      self.assertEqual(
          nested_simplify(__A      )					,			[
              {"sequence": "My name is John", "score": 0.008, "token": 610, "token_str": " John"},
              {"sequence": "My name is Chris", "score": 0.007, "token": 1_573, "token_str": " Chris"},
          ]					,			)
      a__	:						List[str]      		=  unmasker("The largest city in France is <mask>"      )
      self.assertEqual(
          nested_simplify(__A      )					,			[
              {
                  "sequence": "The largest city in France is Paris",
                  "score": 0.251,
                  "token": 2_201,
                  "token_str": " Paris",
              },
              {
                  "sequence": "The largest city in France is Lyon",
                  "score": 0.214,
                  "token": 12_790,
                  "token_str": " Lyon",
              },
          ]					,			)

      a__	:						Tuple      		=  unmasker("My name is <mask>"					,			targets=[" Patrick", " Clara", " Teven"]					,			top_k=3      )
      self.assertEqual(
          nested_simplify(__A      )					,			[
              {"sequence": "My name is Patrick", "score": 0.005, "token": 3_499, "token_str": " Patrick"},
              {"sequence": "My name is Clara", "score": 0.000, "token": 13_606, "token_str": " Clara"},
              {"sequence": "My name is Te", "score": 0.000, "token": 2_941, "token_str": " Te"},
          ]					,			)





   @require_torch
   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  pipeline(task="fill-mask"					,			model="sshleifer/tiny-distilroberta-base"					,			framework="pt"      )
      a__	:						List[str]      		=  None
      a__	:						str      		=  None
      self.run_pipeline_test(__A					,			[]      )





   @require_tf
   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						List[str]      		=  pipeline(task="fill-mask"					,			model="sshleifer/tiny-distilroberta-base"					,			framework="tf"      )
      a__	:						Optional[int]      		=  None
      a__	:						Union[str, Any]      		=  None
      self.run_pipeline_test(__A					,			[]      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  Tuple:
      """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)"      )

      a__	:						str      		=  FillMaskPipeline(model=__A					,			tokenizer=__A      )
      a__	:						Any      		=  [
          F"""This is another {tokenizer.mask_token} test""",
      ]
      return fill_masker, examples





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  Tuple:
      """simple docstring"""





      a__	:						List[Any]      		=  fill_masker.tokenizer
      a__	:						Union[str, Any]      		=  fill_masker.model

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

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

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

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

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





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  str:
      """simple docstring"""





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

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

      # Score equivalence
      a__	:						Dict      		=  fill_masker(F"""This is a {tokenizer.mask_token}"""					,			targets=__A      )
      a__	:						Any      		=  [top_mask["token_str"] for top_mask in outputs]
      a__	:						List[Any]      		=  [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(__A      ) == set(__A      ):
         a__	:						List[str]      		=  fill_masker(F"""This is a {tokenizer.mask_token}"""					,			targets=__A      )
         a__	:						Union[str, Any]      		=  [top_mask["score"] for top_mask in unmasked_targets]
         self.assertEqual(nested_simplify(__A      )					,			nested_simplify(__A      )      )

      # Raises with invalid
      with self.assertRaises(__A      ):
         a__	:						str      		=  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(__A      ):
            a__	:						Dict      		=  fill_masker(F"""This is a {tokenizer.mask_token}"""					,			targets=[""]      )
         with self.assertRaises(__A      ):
            a__	:						int      		=  fill_masker(F"""This is a {tokenizer.mask_token}"""					,			targets=""      )





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  str:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  FillMaskPipeline(model=__A					,			tokenizer=__A					,			top_k=2      )
      a__	:						Tuple      		=  fill_masker(F"""This is a {tokenizer.mask_token}"""      )
      self.assertEqual(
          __A					,			[
              {"sequence": ANY(__A      ), "score": ANY(__A      ), "token": ANY(__A      ), "token_str": ANY(__A      )},
              {"sequence": ANY(__A      ), "score": ANY(__A      ), "token": ANY(__A      ), "token_str": ANY(__A      )},
          ]					,			)

      a__	:						Union[str, Any]      		=  FillMaskPipeline(model=__A					,			tokenizer=__A      )
      a__	:						Optional[int]      		=  fill_masker(F"""This is a {tokenizer.mask_token}"""					,			top_k=2      )
      self.assertEqual(
          __A					,			[
              {"sequence": ANY(__A      ), "score": ANY(__A      ), "token": ANY(__A      ), "token_str": ANY(__A      )},
              {"sequence": ANY(__A      ), "score": ANY(__A      ), "token": ANY(__A      ), "token_str": ANY(__A      )},
          ]					,			)
      self.assertEqual(nested_simplify(__A      )					,			nested_simplify(__A      )      )





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  Tuple:
      """simple docstring"""





      a__	:						Any      		=  tokenizer.get_vocab()
      a__	:						List[Any]      		=  FillMaskPipeline(model=__A					,			tokenizer=__A      )

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

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





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						Tuple      		=  FillMaskPipeline(model=__A					,			tokenizer=__A      )
      a__	:						Optional[Any]      		=  tokenizer.get_vocab()
      # String duplicates + id duplicates
      a__	:						Union[str, Any]      		=  sorted(vocab.keys()      )[:3]
      a__	:						Optional[int]      		=  [targets[0], targets[1], targets[0], targets[2], targets[1]]
      a__	:						Union[str, Any]      		=  fill_masker(F"""My name is {tokenizer.mask_token}"""					,			targets=__A					,			top_k=10      )

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





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  FillMaskPipeline(model=__A					,			tokenizer=__A      )

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








import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()







class   __lowerCAmelCase			(					_UpperCamelCase		,_UpperCamelCase		,_UpperCamelCase		,unittest.TestCase  ):
   _UpperCamelCase :						Optional[int]     		=				StableUnCLIPPipeline
   _UpperCamelCase :						Optional[int]     		=				TEXT_TO_IMAGE_PARAMS
   _UpperCamelCase :						str     		=				TEXT_TO_IMAGE_BATCH_PARAMS
   _UpperCamelCase :						List[Any]     		=				TEXT_TO_IMAGE_IMAGE_PARAMS
   _UpperCamelCase :						str     		=				TEXT_TO_IMAGE_IMAGE_PARAMS

   # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
   _UpperCamelCase :						Any     		=				False





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Any      		=  32
      a__	:						int      		=  embedder_hidden_size

      # prior components

      torch.manual_seed(0      )
      a__	:						Any      		=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

      torch.manual_seed(0      )
      a__	:						Optional[Any]      		=  CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0					,			eos_token_id=2					,			hidden_size=snake_case					,			projection_dim=snake_case					,			intermediate_size=37					,			layer_norm_eps=1E-05					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=1_000					,			)      )

      torch.manual_seed(0      )
      a__	:						int      		=  PriorTransformer(
          num_attention_heads=2					,			attention_head_dim=12					,			embedding_dim=snake_case					,			num_layers=1					,			)

      torch.manual_seed(0      )
      a__	:						str      		=  DDPMScheduler(
          variance_type="fixed_small_log"					,			prediction_type="sample"					,			num_train_timesteps=1_000					,			clip_sample=snake_case					,			clip_sample_range=5.0					,			beta_schedule="squaredcos_cap_v2"					,			)

      # regular denoising components

      torch.manual_seed(0      )
      a__	:						Any      		=  StableUnCLIPImageNormalizer(embedding_dim=snake_case      )
      a__	:						int      		=  DDPMScheduler(beta_schedule="squaredcos_cap_v2"      )

      torch.manual_seed(0      )
      a__	:						Optional[Any]      		=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

      torch.manual_seed(0      )
      a__	:						Union[str, Any]      		=  CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0					,			eos_token_id=2					,			hidden_size=snake_case					,			projection_dim=32					,			intermediate_size=37					,			layer_norm_eps=1E-05					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=1_000					,			)      )

      torch.manual_seed(0      )
      a__	:						Any      		=  UNetaDConditionModel(
          sample_size=32					,			in_channels=4					,			out_channels=4					,			down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")					,			up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")					,			block_out_channels=(32, 64)					,			attention_head_dim=(2, 4)					,			class_embed_type="projection"					,			projection_class_embeddings_input_dim=embedder_projection_dim * 2					,			cross_attention_dim=snake_case					,			layers_per_block=1					,			upcast_attention=snake_case					,			use_linear_projection=snake_case					,			)

      torch.manual_seed(0      )
      a__	:						Tuple      		=  DDIMScheduler(
          beta_schedule="scaled_linear"					,			beta_start=0.00_085					,			beta_end=0.012					,			prediction_type="v_prediction"					,			set_alpha_to_one=snake_case					,			steps_offset=1					,			)

      torch.manual_seed(0      )
      a__	:						Optional[int]      		=  AutoencoderKL()

      a__	:						Any      		=  {
          # prior components
          "prior_tokenizer": prior_tokenizer,
          "prior_text_encoder": prior_text_encoder,
          "prior": prior,
          "prior_scheduler": prior_scheduler,
          # image noising components
          "image_normalizer": image_normalizer,
          "image_noising_scheduler": image_noising_scheduler,
          # regular denoising components
          "tokenizer": tokenizer,
          "text_encoder": text_encoder,
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
      }

      return components





   def 	_snake_case ( self					,			snake_case					,			snake_case=0      )				->  Dict:
      """simple docstring"""





      if str(snake_case      ).startswith("mps"      ):
         a__	:						Union[str, Any]      		=  torch.manual_seed(snake_case      )
      else:
         a__	:						List[str]      		=  torch.Generator(device=snake_case      ).manual_seed(snake_case      )
      a__	:						Any      		=  {
          "prompt": "A painting of a squirrel eating a burger",
          "generator": generator,
          "num_inference_steps": 2,
          "prior_num_inference_steps": 2,
          "output_type": "numpy",
      }
      return inputs





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Dict      		=  torch_device == "cpu"

      self._test_attention_slicing_forward_pass(test_max_difference=snake_case      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						int      		=  torch_device in ["cpu", "mps"]

      self._test_inference_batch_single_identical(test_max_difference=snake_case      )







@slow
@require_torch_gpu
class   __lowerCAmelCase			(					unittest.TestCase  ):





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						int      		=  load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"      )

      a__	:						int      		=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"					,			torch_dtype=torch.floataa      )
      pipe.to(snake_case      )
      pipe.set_progress_bar_config(disable=snake_case      )
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      a__	:						Dict      		=  torch.Generator(device="cpu"      ).manual_seed(0      )
      a__	:						Dict      		=  pipe("anime turle"					,			generator=snake_case					,			output_type="np"      )

      a__	:						List[str]      		=  output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(snake_case					,			snake_case      )





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      a__	:						str      		=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"					,			torch_dtype=torch.floataa      )
      a__	:						Union[str, Any]      		=  pipe.to(snake_case      )
      pipe.set_progress_bar_config(disable=snake_case      )
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      a__	:						Union[str, Any]      		=  pipe(
          "anime turtle"					,			prior_num_inference_steps=2					,			num_inference_steps=2					,			output_type="np"					,			)

      a__	:						List[Any]      		=  torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9

