
"""simple docstring"""




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__						(      a_,			unittest.TestCase						):
   __lowerCAmelCase            =				VideoToVideoSDPipeline
   __lowerCAmelCase            =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""}						) - {"""image""", """width""", """height"""}
   __lowerCAmelCase            =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""}						) - {"""image"""}
   __lowerCAmelCase            =				PipelineTesterMixin.required_optional_params - {"""latents"""}
   __lowerCAmelCase            =				False

   # No `output_type`.
   __lowerCAmelCase            =				frozenset(
       [
           """num_inference_steps""",
           """generator""",
           """latents""",
           """return_dict""",
           """callback""",
           """callback_steps""",
       ]						)
   def 			__magic_name__			(			self				):
          torch.manual_seed(0				)
          lowercase    :				int       		=							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	,    )
          lowercase    :				int       		=							DDIMScheduler(
              beta_start=0.0_0_0_8_5	,    beta_end=0.0_1_2	,    beta_schedule="scaled_linear"	,    clip_sample=_a	,    set_alpha_to_one=_a	,    )
          torch.manual_seed(0				)
          lowercase    :				List[Any]       		=							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=128	,    )
          torch.manual_seed(0				)
          lowercase    :				Tuple       		=							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=1_000	,    hidden_act="gelu"	,    projection_dim=512	,    )
          lowercase    :				Tuple       		=							CLIPTextModel(_a				)
          lowercase    :				Union[str, Any]       		=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

          lowercase    :				List[str]       		=							{
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
          }
          return components
   def 			__magic_name__			(			self	,    _a	,    _a=0				):
          # 3 frames
          lowercase    :				Optional[int]       		=							floats_tensor((1, 3, 3, 32, 32)	,    rng=random.Random(_a				)				).to(_a				)

          if str(_a				).startswith("mps"				):
                 lowercase    :				Union[str, Any]       		=							torch.manual_seed(_a				)
          else:
                 lowercase    :				List[Any]       		=							torch.Generator(device=_a				).manual_seed(_a				)
          lowercase    :				Optional[Any]       		=							{
              "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 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							"cpu"  # ensure determinism for the device-dependent torch.Generator
          lowercase    :				Optional[int]       		=							self.get_dummy_components()
          lowercase    :				str       		=							VideoToVideoSDPipeline(**_a				)
          lowercase    :				Any       		=							sd_pipe.to(_a				)
          sd_pipe.set_progress_bar_config(disable=_a				)

          lowercase    :				int       		=							self.get_dummy_inputs(_a				)
          lowercase    :				int       		=							"np"
          lowercase    :				int       		=							sd_pipe(**_a				).frames
          lowercase    :				List[Any]       		=							frames[0][-3:, -3:, -1]

          assert frames[0].shape == (32, 32, 3)
          lowercase    :				str       		=							np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]				)

          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 			__magic_name__			(			self				):
          self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_a	,    expected_max_diff=5E-3				)
   @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."				)
   def 			__magic_name__			(			self				):
          pass

   def 			__magic_name__			(			self				):
          return super().test_progress_bar()


@slow
@skip_mps
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_XL"	,    torch_dtype=torch.floataa				)
          pipe.enable_model_cpu_offload()

          # 10 frames
          lowercase    :				Union[str, Any]       		=							torch.Generator(device="cpu"				).manual_seed(0				)
          lowercase    :				Dict       		=							torch.randn((1, 10, 3, 1_024, 576)	,    generator=_a				)
          lowercase    :				str       		=							video.to("cuda"				)

          lowercase    :				Dict       		=							"Spiderman is surfing"

          lowercase    :				Optional[int]       		=							pipe(_a	,    video=_a	,    generator=_a	,    num_inference_steps=3	,    output_type="pt"				).frames

          lowercase    :				Optional[int]       		=							np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]				)
          assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array				).sum() < 1E-2




"""simple docstring"""




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Dict     , __snake_case				:       Any  ) ->				Any:
       # Initialise PyTorch model
       lowercase    :				Union[str, Any]       		=							RemBertConfig.from_json_file(__snake_case  )
       print("Building PyTorch model from configuration: {}".format(str(__snake_case  )  )  )
       lowercase    :				str       		=							RemBertModel(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(__snake_case     , __snake_case     , __snake_case  )

       # Save pytorch-model
       print("Save PyTorch model to {}".format(__snake_case  )  )
       torch.save(model.state_dict()     , __snake_case  )


if __name__ == "__main__":
   _A			:       str   		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   _A			:       List[str]   		=       parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)


