"""simple docstring"""


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase__     						=  logging.get_logger(__name__)

UpperCAmelCase__     						=  {
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     List[str]   							=							'gpt_neo'
    _snake_case   :     Optional[int]   							=							['past_key_values']
    _snake_case   :     List[Any]   							=							{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__(    self		:     Optional[int]      , __lowerCAmelCase		:     Optional[Any]=5_0257      , __lowerCAmelCase		:     Optional[Any]=2048      , __lowerCAmelCase		:     str=2048      , __lowerCAmelCase		:     List[str]=24      , __lowerCAmelCase		:     Optional[Any]=[[["global", "local"], 12]]      , __lowerCAmelCase		:     Optional[Any]=16      , __lowerCAmelCase		:     int=None      , __lowerCAmelCase		:     str=256      , __lowerCAmelCase		:     str="gelu_new"      , __lowerCAmelCase		:     Any=0.0      , __lowerCAmelCase		:     List[str]=0.0      , __lowerCAmelCase		:     Optional[int]=0.0      , __lowerCAmelCase		:     List[Any]=0.1      , __lowerCAmelCase		:     List[Any]=1e-5      , __lowerCAmelCase		:     Dict=0.02      , __lowerCAmelCase		:     Dict=True      , __lowerCAmelCase		:     Dict=5_0256      , __lowerCAmelCase		:     str=5_0256      , **__lowerCAmelCase		:     Optional[Any]      , ):
      _UpperCAmelCase         =					vocab_size
      _UpperCAmelCase         =					max_position_embeddings
      _UpperCAmelCase         =					hidden_size
      _UpperCAmelCase         =					num_layers
      _UpperCAmelCase         =					num_heads
      _UpperCAmelCase         =					intermediate_size
      _UpperCAmelCase         =					window_size
      _UpperCAmelCase         =					activation_function
      _UpperCAmelCase         =					resid_dropout
      _UpperCAmelCase         =					embed_dropout
      _UpperCAmelCase         =					attention_dropout
      _UpperCAmelCase         =					classifier_dropout
      _UpperCAmelCase         =					layer_norm_epsilon
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					use_cache

      _UpperCAmelCase         =					bos_token_id
      _UpperCAmelCase         =					eos_token_id

      _UpperCAmelCase         =					attention_types
      _UpperCAmelCase         =					self.expand_attention_types_params(__lowerCAmelCase    )

      if len(self.attention_layers    ) != self.num_layers:
        raise ValueError(
            """Configuration for convolutional module is incorrect. """
            """It is required that `len(config.attention_layers)` == `config.num_layers` """
            f'''but is `len(config.attention_layers) = {len(self.attention_layers    )}`, '''
            f'''`config.num_layers = {self.num_layers}`. '''
            """`config.attention_layers` is prepared using `config.attention_types`. """
            """Please verify the value of `config.attention_types` argument."""    )

      super().__init__(bos_token_id=__lowerCAmelCase      , eos_token_id=__lowerCAmelCase      , **__lowerCAmelCase    )


    @staticmethod
    def    lowerCAmelCase_     (    __lowerCAmelCase		:     Dict    ):
      _UpperCAmelCase         =					[]
      for item in attention_types:
        for _ in range(item[1]    ):
          attentions.extend(item[0]    )
      return attentions






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  import torch

  _UpperCAmelCase         =					input.size()
  _UpperCAmelCase         =					len(lowercase						)
  _UpperCAmelCase         =					shape[dimension]

  _UpperCAmelCase         =					torch.arange(0      ,lowercase      ,lowercase						)
  _UpperCAmelCase         =					torch.div(sizedim - size      ,lowercase      ,rounding_mode="""floor"""						) + 1
  _UpperCAmelCase         =					torch.arange(lowercase						) + low_indices[:min_length][:, None]

  _UpperCAmelCase         =					[slice(lowercase						)] * rank
  _UpperCAmelCase         =					indices
  _UpperCAmelCase         =					input[s]

  _UpperCAmelCase         =					list(range(0      ,rank + 1						)						)
  perm.append(perm.pop(dimension + 1						)						)

  return sliced.permute(lowercase						)






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  import torch

  _UpperCAmelCase         =					torch.arange(1      ,lowercase						)
  _UpperCAmelCase         =					torch.remainder(lowercase      ,lowercase						)
  _UpperCAmelCase         =					remainders == 0
  _UpperCAmelCase         =					candidates[divisor_indices]
  _UpperCAmelCase         =					torch.max(lowercase						)
  return largest_divisor, torch.div(lowercase      ,lowercase      ,rounding_mode="""floor"""						)


class 				a (						lowerCAmelCase_      ):


    @property
    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
      if self.use_past:
        self.fill_with_past_key_values_(__lowerCAmelCase      , direction="""inputs"""    )
        _UpperCAmelCase         =					{0: """batch""", 1: """past_sequence + sequence"""}
      else:
        _UpperCAmelCase         =					{0: """batch""", 1: """sequence"""}

      return common_inputs


    @property
    def    lowerCAmelCase_     (    self		:     Any    ):
      return self._config.num_heads


    def    lowerCAmelCase_     (    self		:     Tuple      , __lowerCAmelCase		:     PreTrainedTokenizer      , __lowerCAmelCase		:     int = -1      , __lowerCAmelCase		:     int = -1      , __lowerCAmelCase		:     bool = False      , __lowerCAmelCase		:     Optional[TensorType] = None      , ):
      _UpperCAmelCase         =					super(__lowerCAmelCase      , self    ).generate_dummy_inputs(
          __lowerCAmelCase      , batch_size=__lowerCAmelCase      , seq_length=__lowerCAmelCase      , is_pair=__lowerCAmelCase      , framework=__lowerCAmelCase    )

      # We need to order the input in the way they appears in the forward()
      _UpperCAmelCase         =					OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}    )

      # Need to add the past_keys
      if self.use_past:
        if not is_torch_available():
          raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""    )
        else:
          import torch

          _UpperCAmelCase       , _UpperCAmelCase         =					common_inputs["""input_ids"""].shape
          # Not using the same length for past_key_values
          _UpperCAmelCase         =					seqlen + 2
          _UpperCAmelCase         =					(
              batch,
              self.num_attention_heads,
              past_key_values_length,
              self._config.hidden_size // self.num_attention_heads,
          )
          _UpperCAmelCase         =					[
              (torch.zeros(__lowerCAmelCase    ), torch.zeros(__lowerCAmelCase    )) for _ in range(self.num_layers    )
          ]

      _UpperCAmelCase         =					common_inputs["""attention_mask"""]
      if self.use_past:
        _UpperCAmelCase         =					ordered_inputs["""attention_mask"""].dtype
        _UpperCAmelCase         =					torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(__lowerCAmelCase      , __lowerCAmelCase      , dtype=__lowerCAmelCase    )]      , dim=1    )

      return ordered_inputs


    @property
    def    lowerCAmelCase_     (    self		:     Dict    ):
      return 13

"""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   :     int   							=							StableUnCLIPPipeline
    _snake_case   :     str   							=							TEXT_TO_IMAGE_PARAMS
    _snake_case   :     Any   							=							TEXT_TO_IMAGE_BATCH_PARAMS
    _snake_case   :     Optional[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		:     Optional[int]    ):
      _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		:     Optional[int]      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     str=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		:     Optional[int]    ):
      _UpperCAmelCase         =					torch_device == """cpu"""

      self._test_attention_slicing_forward_pass(test_max_difference=__lowerCAmelCase    )


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


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _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		:     Any    ):
      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
