"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__  		=						{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase__  		=						["""BertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase__  		=						[
          """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BertForMaskedLM""",
          """BertForMultipleChoice""",
          """BertForNextSentencePrediction""",
          """BertForPreTraining""",
          """BertForQuestionAnswering""",
          """BertForSequenceClassification""",
          """BertForTokenClassification""",
          """BertLayer""",
          """BertLMHeadModel""",
          """BertModel""",
          """BertPreTrainedModel""",
          """load_tf_weights_in_bert""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase__  		=						[
          """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFBertEmbeddings""",
          """TFBertForMaskedLM""",
          """TFBertForMultipleChoice""",
          """TFBertForNextSentencePrediction""",
          """TFBertForPreTraining""",
          """TFBertForQuestionAnswering""",
          """TFBertForSequenceClassification""",
          """TFBertForTokenClassification""",
          """TFBertLMHeadModel""",
          """TFBertMainLayer""",
          """TFBertModel""",
          """TFBertPreTrainedModel""",
      ]
try:
      if not is_tensorflow_text_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase__  		=						["""TFBertTokenizer"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase__  		=						[
          """FlaxBertForCausalLM""",
          """FlaxBertForMaskedLM""",
          """FlaxBertForMultipleChoice""",
          """FlaxBertForNextSentencePrediction""",
          """FlaxBertForPreTraining""",
          """FlaxBertForQuestionAnswering""",
          """FlaxBertForSequenceClassification""",
          """FlaxBertForTokenClassification""",
          """FlaxBertModel""",
          """FlaxBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
      from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_fast import BertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert import (
                BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BertForMaskedLM,
                BertForMultipleChoice,
                BertForNextSentencePrediction,
                BertForPreTraining,
                BertForQuestionAnswering,
                BertForSequenceClassification,
                BertForTokenClassification,
                BertLayer,
                BertLMHeadModel,
                BertModel,
                BertPreTrainedModel,
                load_tf_weights_in_bert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bert import (
                TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFBertEmbeddings,
                TFBertForMaskedLM,
                TFBertForMultipleChoice,
                TFBertForNextSentencePrediction,
                TFBertForPreTraining,
                TFBertForQuestionAnswering,
                TFBertForSequenceClassification,
                TFBertForTokenClassification,
                TFBertLMHeadModel,
                TFBertMainLayer,
                TFBertModel,
                TFBertPreTrainedModel,
            )

      try:
            if not is_tensorflow_text_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_tf import TFBertTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bert import (
                FlaxBertForCausalLM,
                FlaxBertForMaskedLM,
                FlaxBertForMultipleChoice,
                FlaxBertForNextSentencePrediction,
                FlaxBertForPreTraining,
                FlaxBertForQuestionAnswering,
                FlaxBertForSequenceClassification,
                FlaxBertForTokenClassification,
                FlaxBertModel,
                FlaxBertPreTrainedModel,
            )

else:
      import sys

      UpperCAmelCase__  		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""simple docstring"""


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        a				(		lowerCAmelCase_	,							lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  Optional[int]     							=							StableUnCLIPPipeline
    _snake_case     :  List[str]     							=							TEXT_TO_IMAGE_PARAMS
    _snake_case     :  Tuple     							=							TEXT_TO_IMAGE_BATCH_PARAMS
    _snake_case     :  List[Any]     							=							TEXT_TO_IMAGE_IMAGE_PARAMS
    _snake_case     :  str     							=							TEXT_TO_IMAGE_IMAGE_PARAMS

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


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      32
      _UpperCAmelCase						 =      embedder_hidden_size

      # prior components

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

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=__lowerCAmelCase   ,				projection_dim=__lowerCAmelCase   ,				intermediate_size=37   ,				layer_norm_eps=1e-0_5   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      PriorTransformer(
          num_attention_heads=2   ,				attention_head_dim=12   ,				embedding_dim=__lowerCAmelCase   ,				num_layers=1   ,				)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      DDPMScheduler(
          variance_type="""fixed_small_log"""   ,				prediction_type="""sample"""   ,				num_train_timesteps=1000   ,				clip_sample=__lowerCAmelCase   ,				clip_sample_range=5.0   ,				beta_schedule="""squaredcos_cap_v2"""   ,				)

      # regular denoising components

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      StableUnCLIPImageNormalizer(embedding_dim=__lowerCAmelCase					)
      _UpperCAmelCase						 =      DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""					)

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

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=__lowerCAmelCase   ,				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=1000   ,				)					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      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=__lowerCAmelCase   ,				layers_per_block=1   ,				upcast_attention=__lowerCAmelCase   ,				use_linear_projection=__lowerCAmelCase   ,				)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      DDIMScheduler(
          beta_schedule="""scaled_linear"""   ,				beta_start=0.00_085   ,				beta_end=0.012   ,				prediction_type="""v_prediction"""   ,				set_alpha_to_one=__lowerCAmelCase   ,				steps_offset=1   ,				)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      AutoencoderKL()

      _UpperCAmelCase						 =      {
          # 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      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  int=0					):
      if str(__lowerCAmelCase					).startswith("""mps"""					):
        _UpperCAmelCase						 =      torch.manual_seed(__lowerCAmelCase					)
      else:
        _UpperCAmelCase						 =      torch.Generator(device=__lowerCAmelCase					).manual_seed(__lowerCAmelCase					)
      _UpperCAmelCase						 =      {
          """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      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      torch_device == """cpu"""

      self._test_attention_slicing_forward_pass(test_max_difference=__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      torch_device in ["""cpu""", """mps"""]

      self._test_inference_batch_single_identical(test_max_difference=__lowerCAmelCase					)



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


    def      lowerCAmelCase_  (					self	:  List[str]					):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


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

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

      _UpperCAmelCase						 =      torch.Generator(device="""cpu"""					).manual_seed(0					)
      _UpperCAmelCase						 =      pipe("""anime turle"""   ,				generator=__lowerCAmelCase   ,				output_type="""np"""					)

      _UpperCAmelCase						 =      output.images[0]

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

      assert_mean_pixel_difference(__lowerCAmelCase   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      _UpperCAmelCase						 =      StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""   ,				torch_dtype=torch.floataa					)
      _UpperCAmelCase						 =      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _UpperCAmelCase						 =      pipe(
          """anime turtle"""   ,				prior_num_inference_steps=2   ,				num_inference_steps=2   ,				output_type="""np"""   ,				)

      _UpperCAmelCase						 =      torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9
