import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
@skip_mps
class    A_ (       _snake_case			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			VideoToVideoSDPipeline
  _SCREAMING_SNAKE_CASE    				=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""} ) - {"""image""", """width""", """height"""}
  _SCREAMING_SNAKE_CASE    				=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""} ) - {"""image"""}
  _SCREAMING_SNAKE_CASE    				=			PipelineTesterMixin.required_optional_params - {"""latents"""}
  _SCREAMING_SNAKE_CASE    				=			False

  # No `output_type`.
  _SCREAMING_SNAKE_CASE    				=			frozenset(
      [
          """num_inference_steps""",
          """generator""",
          """latents""",
          """return_dict""",
          """callback""",
          """callback_steps""",
      ] )

  def 		_UpperCAmelCase   (		self :      str       ):
   torch.manual_seed(0       )
   __a       =  UNetaDConditionModel(
       block_out_channels=(32, 64, 64, 64)     ,							layers_per_block=2     ,							sample_size=32     ,							in_channels=4     ,							out_channels=4     ,							down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")     ,							up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")     ,							cross_attention_dim=32     ,							attention_head_dim=4     ,							)
   __a       =  DDIMScheduler(
       beta_start=0.0_00_85     ,							beta_end=0.0_12     ,							beta_schedule="scaled_linear"     ,							clip_sample=lowerCAmelCase__     ,							set_alpha_to_one=lowerCAmelCase__     ,							)
   torch.manual_seed(0       )
   __a       =  AutoencoderKL(
       block_out_channels=[32, 64]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,							latent_channels=4     ,							sample_size=1_28     ,							)
   torch.manual_seed(0       )
   __a       =  CLIPTextConfig(
       bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=32     ,							intermediate_size=37     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=10_00     ,							hidden_act="gelu"     ,							projection_dim=5_12     ,							)
   __a       =  CLIPTextModel(lowerCAmelCase__       )
   __a       =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

   __a       =  {
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
   }
   return components

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Dict=0       ):
   # 3 frames
   __a       =  floats_tensor((1, 3, 3, 32, 32)     ,							rng=random.Random(lowerCAmelCase__       )       ).to(lowerCAmelCase__       )

   if str(lowerCAmelCase__       ).startswith("mps"       ):
    __a       =  torch.manual_seed(lowerCAmelCase__       )
   else:
    __a       =  torch.Generator(device=lowerCAmelCase__       ).manual_seed(lowerCAmelCase__       )
   __a       =  {
       "prompt": "A painting of a squirrel eating a burger",
       "video": video,
       "generator": generator,
       "num_inference_steps": 2,
       "guidance_scale": 6.0,
       "output_type": "pt",
   }
   return inputs

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
   __a       =  self.get_dummy_components()
   __a       =  VideoToVideoSDPipeline(**lowerCAmelCase__       )
   __a       =  sd_pipe.to(lowerCAmelCase__       )
   sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

   __a       =  self.get_dummy_inputs(lowerCAmelCase__       )
   __a       =  "np"
   __a       =  sd_pipe(**lowerCAmelCase__       ).frames
   __a       =  frames[0][-3:, -3:, -1]

   assert frames[0].shape == (32, 32, 3)
   __a       =  np.array([1_06, 1_17, 1_13, 1_74, 1_37, 1_12, 1_48, 1_51, 1_31]       )

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2

  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available()     ,							reason="XFormers attention is only available with CUDA and `xformers` installed"     ,							)
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCAmelCase__     ,							expected_max_diff=5E-3       )

  @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."       )
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   pass

  @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."       )
  def 		_UpperCAmelCase   (		self :      str       ):
   pass

  @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."       )
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   pass

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   return super().test_progress_bar()
@slow
@skip_mps
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_XL"     ,							torch_dtype=torch.floataa       )
   pipe.enable_model_cpu_offload()

   # 10 frames
   __a       =  torch.Generator(device="cpu"       ).manual_seed(0       )
   __a       =  torch.randn((1, 10, 3, 10_24, 5_76)     ,							generator=lowerCAmelCase__       )
   __a       =  video.to("cuda"       )

   __a       =  "Spiderman is surfing"

   __a       =  pipe(lowerCAmelCase__     ,							video=lowerCAmelCase__     ,							generator=lowerCAmelCase__     ,							num_inference_steps=3     ,							output_type="pt"       ).frames

   __a       =  np.array([-1.0_45_89_84, -1.1_27_92_97, -0.9_66_30_86, -0.91_50_39_06, -0.75_09_76_56]       )
   assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array       ).sum() < 1E-2



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE :	Dict							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	List[str]							     =		{
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			"""xlm-roberta-xl"""

  def __init__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      int=25_08_80     ,							__SCREAMING_SNAKE_CASE :      Dict=25_60     ,							__SCREAMING_SNAKE_CASE :      List[str]=36     ,							__SCREAMING_SNAKE_CASE :      int=32     ,							__SCREAMING_SNAKE_CASE :      int=1_02_40     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_14     ,							__SCREAMING_SNAKE_CASE :      List[Any]=1     ,							__SCREAMING_SNAKE_CASE :      Any=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=1E-05     ,							__SCREAMING_SNAKE_CASE :      str=1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=2     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="absolute"     ,							__SCREAMING_SNAKE_CASE :      str=True     ,							__SCREAMING_SNAKE_CASE :      str=None     ,							**__SCREAMING_SNAKE_CASE :      str     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  position_embedding_type
   __a       =  use_cache
   __a       =  classifier_dropout
class    A_ (       a_ ):

  @property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   if self.task == "multiple-choice":
    __a       =  {0: "batch", 1: "choice", 2: "sequence"}
   else:
    __a       =  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
       ]       )


