





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      _SCREAMING_SNAKE_CASE		(			__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							unittest.TestCase					):







 '''simple docstring'''
 lowercase_	=    StableUnCLIPPipeline
 lowercase_	=    TEXT_TO_IMAGE_PARAMS
 lowercase_	=    TEXT_TO_IMAGE_BATCH_PARAMS
 lowercase_	=    TEXT_TO_IMAGE_IMAGE_PARAMS
 lowercase_	=    TEXT_TO_IMAGE_IMAGE_PARAMS

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

 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=32
   lowerCamelCase__:   Optional[int]    						=embedder_hidden_size

   # prior components

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

   torch.manual_seed(0)
   lowerCamelCase__:   str    						=CLIPTextModelWithProjection(
       CLIPTextConfig(
           bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=_UpperCamelCase ,				projection_dim=_UpperCamelCase ,				intermediate_size=37 ,				layer_norm_eps=1E-0_5 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=1_000 ,				))

   torch.manual_seed(0)
   lowerCamelCase__:   Optional[int]    						=PriorTransformer(
       num_attention_heads=2 ,				attention_head_dim=12 ,				embedding_dim=_UpperCamelCase ,				num_layers=1 ,				)

   torch.manual_seed(0)
   lowerCamelCase__:   Optional[Any]    						=DDPMScheduler(
       variance_type="fixed_small_log" ,				prediction_type="sample" ,				num_train_timesteps=1_000 ,				clip_sample=_UpperCamelCase ,				clip_sample_range=5.0 ,				beta_schedule="squaredcos_cap_v2" ,				)

   # regular denoising components

   torch.manual_seed(0)
   lowerCamelCase__:   Optional[int]    						=StableUnCLIPImageNormalizer(embedding_dim=_UpperCamelCase)
   lowerCamelCase__:   Dict    						=DDPMScheduler(beta_schedule="squaredcos_cap_v2")

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

   torch.manual_seed(0)
   lowerCamelCase__:   Optional[int]    						=CLIPTextModel(
       CLIPTextConfig(
           bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=_UpperCamelCase ,				projection_dim=32 ,				intermediate_size=37 ,				layer_norm_eps=1E-0_5 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=1_000 ,				))

   torch.manual_seed(0)
   lowerCamelCase__:   Optional[int]    						=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=_UpperCamelCase ,				layers_per_block=1 ,				upcast_attention=_UpperCamelCase ,				use_linear_projection=_UpperCamelCase ,				)

   torch.manual_seed(0)
   lowerCamelCase__:   Tuple    						=DDIMScheduler(
       beta_schedule="scaled_linear" ,				beta_start=0.0_0085 ,				beta_end=0.012 ,				prediction_type="v_prediction" ,				set_alpha_to_one=_UpperCamelCase ,				steps_offset=1 ,				)

   torch.manual_seed(0)
   lowerCamelCase__:   str    						=AutoencoderKL()

   lowerCamelCase__:   str    						={
       # 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 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any] ,				UpperCAmelCase_       :     int ,				UpperCAmelCase_       :     Dict=0) ->Dict:



   '''simple docstring'''



   if str(_UpperCamelCase).startswith("mps"):
     lowerCamelCase__:   Any    						=torch.manual_seed(_UpperCamelCase)
   else:
     lowerCamelCase__:   Union[str, Any]    						=torch.Generator(device=_UpperCamelCase).manual_seed(_UpperCamelCase)
   lowerCamelCase__:   int    						={
       """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 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:   Optional[Any]    						=torch_device == """cpu"""

   self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase)







 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						=torch_device in ["""cpu""", """mps"""]

   self._test_inference_batch_single_identical(test_max_difference=_UpperCamelCase)



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







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->Any:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						=load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy")

   lowerCamelCase__:   int    						=StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" ,				torch_dtype=torch.floataa)
   pipe.to(_UpperCamelCase)
   pipe.set_progress_bar_config(disable=_UpperCamelCase)
   # 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()

   lowerCamelCase__:   List[str]    						=torch.Generator(device="cpu").manual_seed(0)
   lowerCamelCase__:   Dict    						=pipe("anime turle" ,				generator=_UpperCamelCase ,				output_type="np")

   lowerCamelCase__:   Dict    						=output.images[0]

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

   assert_mean_pixel_difference(_UpperCamelCase ,				_UpperCamelCase)







 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->int:



   '''simple docstring'''



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

   lowerCamelCase__:   int    						=StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" ,				torch_dtype=torch.floataa)
   lowerCamelCase__:   Optional[Any]    						=pipe.to(_UpperCamelCase)
   pipe.set_progress_bar_config(disable=_UpperCamelCase)
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   lowerCamelCase__:   int    						=pipe(
       "anime turtle" ,				prior_num_inference_steps=2 ,				num_inference_steps=2 ,				output_type="np" ,				)

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













import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__A             =       logging.get_logger(__name__)

__A             =       {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A             =       {
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

__A             =       {
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


__A             =       {
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''
 lowercase_	=    VOCAB_FILES_NAMES
 lowercase_	=    PRETRAINED_VOCAB_FILES_MAP
 lowercase_	=    PRETRAINED_INIT_CONFIGURATION
 lowercase_	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_	=    SqueezeBertTokenizer

 def __init__(self       :     Any ,				UpperCAmelCase_       :     int=None ,				UpperCAmelCase_       :     List[str]=None ,				UpperCAmelCase_       :     int=True ,				UpperCAmelCase_       :     Optional[int]="[UNK]" ,				UpperCAmelCase_       :     int="[SEP]" ,				UpperCAmelCase_       :     Optional[Any]="[PAD]" ,				UpperCAmelCase_       :     Optional[int]="[CLS]" ,				UpperCAmelCase_       :     str="[MASK]" ,				UpperCAmelCase_       :     Union[str, Any]=True ,				UpperCAmelCase_       :     Optional[Any]=None ,				**UpperCAmelCase_       :     Dict ,				) ->Union[str, Any]:



   '''simple docstring'''



   super().__init__(
       UpperCAmelCase_ ,				tokenizer_file=UpperCAmelCase_ ,				do_lower_case=UpperCAmelCase_ ,				unk_token=UpperCAmelCase_ ,				sep_token=UpperCAmelCase_ ,				pad_token=UpperCAmelCase_ ,				cls_token=UpperCAmelCase_ ,				mask_token=UpperCAmelCase_ ,				tokenize_chinese_chars=UpperCAmelCase_ ,				strip_accents=UpperCAmelCase_ ,				**UpperCAmelCase_ ,				)

   lowerCamelCase__:   Dict    						=json.loads(self.backend_tokenizer.normalizer.__getstate__())
   if (
       normalizer_state.get("lowercase" ,				UpperCAmelCase_) != do_lower_case
       or normalizer_state.get("strip_accents" ,				UpperCAmelCase_) != strip_accents
       or normalizer_state.get("handle_chinese_chars" ,				UpperCAmelCase_) != tokenize_chinese_chars
   ):
     lowerCamelCase__:   Any    						=getattr(UpperCAmelCase_ ,				normalizer_state.pop("type"))
     lowerCamelCase__:   Union[str, Any]    						=do_lower_case
     lowerCamelCase__:   Optional[Any]    						=strip_accents
     lowerCamelCase__:   Optional[int]    						=tokenize_chinese_chars
     lowerCamelCase__:   int    						=normalizer_class(**UpperCAmelCase_)

   lowerCamelCase__:   Any    						=do_lower_case

 def 							SCREAMING_SNAKE_CASE_      (self       :     str ,				UpperCAmelCase_       :     List[str] ,				UpperCAmelCase_       :     str=None) ->Dict:



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

   if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

   return output

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int] ,				UpperCAmelCase_       :     List[int] ,				UpperCAmelCase_       :     Optional[List[int]] = None) ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:   Dict    						=[self.sep_token_id]
   lowerCamelCase__:   Tuple    						=[self.cls_token_id]
   if token_ids_a is None:
     return len(cls + token_ids_a + sep) * [0]
   return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]







 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any] ,				UpperCAmelCase_       :     str ,				UpperCAmelCase_       :     Optional[str] = None) ->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__:   Optional[int]    						=self._tokenizer.model.save(UpperCAmelCase_ ,				name=UpperCAmelCase_)
   return tuple(UpperCAmelCase_)

