
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_a      :			int					=							logging.get_logger(__name__)

_a      :			List[Any]					=							{
    """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}






class      _UpperCAmelCase				(					_snake_case		,					_snake_case):
    __lowercase							: List[Any]  =					"""resnet"""
    __lowercase							: List[str]  =					["""basic""", """bottleneck"""]
    def __init__(    self						,    snake_case_=3						,    snake_case_=64						,    snake_case_=[2_56, 5_12, 10_24, 20_48]						,    snake_case_=[3, 4, 6, 3]						,    snake_case_="bottleneck"						,    snake_case_="relu"						,    snake_case_=False						,    snake_case_=None						,    snake_case_=None						,    **snake_case_						,    ):
      super().__init__(**snake_case_     )
      if layer_type not in self.layer_types:
        raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types     )}'     )
      _snake_case     :						Any						=   num_channels
      _snake_case     :						List[str]						=   embedding_size
      _snake_case     :						Union[str, Any]						=   hidden_sizes
      _snake_case     :						int						=   depths
      _snake_case     :						Union[str, Any]						=   layer_type
      _snake_case     :						Union[str, Any]						=   hidden_act
      _snake_case     :						Tuple						=   downsample_in_first_stage
      _snake_case     :						List[str]						=   ["stem"] + [F'stage{idx}' for idx in range(1						,    len(snake_case_     ) + 1     )]
      _snake_case			,					_snake_case     :						str						=   get_aligned_output_features_output_indices(
          out_features=snake_case_						,    out_indices=snake_case_						,    stage_names=self.stage_names     )






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: Dict  =					version.parse("""1.11""")
    @property
    def 					lowerCamelCase__	(    self     ):
      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
          ]     )


    @property
    def 					lowerCamelCase__	(    self     ):
      return 1E-3



"""simple docstring"""
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






@skip_mps
class      _UpperCAmelCase				(					_snake_case		,					unittest.TestCase):
    __lowercase							: Any  =					TextToVideoSDPipeline
    __lowercase							: str  =					TEXT_TO_IMAGE_PARAMS
    __lowercase							: int  =					TEXT_TO_IMAGE_BATCH_PARAMS
    # No `output_type`.
    __lowercase							: Optional[int]  =					frozenset(
        [
            """num_inference_steps""",
            """generator""",
            """latents""",
            """return_dict""",
            """callback""",
            """callback_steps""",
        ])
    def 					lowerCamelCase__	(    self     ):
      torch.manual_seed(0     )
      _snake_case     :						str						=   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						,    )
      _snake_case     :						List[Any]						=   DDIMScheduler(
          beta_start=0.00085						,    beta_end=0.012						,    beta_schedule="scaled_linear"						,    clip_sample=snake_case_						,    set_alpha_to_one=snake_case_						,    )
      torch.manual_seed(0     )
      _snake_case     :						Union[str, 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=1_28						,    )
      torch.manual_seed(0     )
      _snake_case     :						Optional[Any]						=   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						,    )
      _snake_case     :						Tuple						=   CLIPTextModel(snake_case_     )
      _snake_case     :						Optional[int]						=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

      _snake_case     :						Any						=   {
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
      }
      return components
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_=0     ):
      if str(snake_case_     ).startswith("mps"     ):
        _snake_case     :						str						=   torch.manual_seed(snake_case_     )
      else:
        _snake_case     :						Union[str, Any]						=   torch.Generator(device=snake_case_     ).manual_seed(snake_case_     )
      _snake_case     :						str						=   {
          "prompt": "A painting of a squirrel eating a burger",
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "pt",
      }
      return inputs
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						int						=   "cpu"  # ensure determinism for the device-dependent torch.Generator
      _snake_case     :						Optional[Any]						=   self.get_dummy_components()
      _snake_case     :						Tuple						=   TextToVideoSDPipeline(**snake_case_     )
      _snake_case     :						List[str]						=   sd_pipe.to(snake_case_     )
      sd_pipe.set_progress_bar_config(disable=snake_case_     )

      _snake_case     :						int						=   self.get_dummy_inputs(snake_case_     )
      _snake_case     :						Union[str, Any]						=   "np"
      _snake_case     :						Dict						=   sd_pipe(**snake_case_     ).frames
      _snake_case     :						Any						=   frames[0][-3:, -3:, -1]

      assert frames[0].shape == (64, 64, 3)
      _snake_case     :						Dict						=   np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
    def 					lowerCamelCase__	(    self     ):
      self._test_attention_slicing_forward_pass(test_mean_pixel_difference=snake_case_						,    expected_max_diff=3E-3     )
    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()						,    reason="XFormers attention is only available with CUDA and `xformers` installed"						,    )
    def 					lowerCamelCase__	(    self     ):
      self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case_						,    expected_max_diff=1E-2     )
    @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."     )
    def 					lowerCamelCase__	(    self     ):
      pass
    @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."     )
    def 					lowerCamelCase__	(    self     ):
      pass
    @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."     )
    def 					lowerCamelCase__	(    self     ):
      pass


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






@slow
@skip_mps
class      _UpperCAmelCase				(					unittest.TestCase):
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"     )

      _snake_case     :						int						=   TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b"     )
      _snake_case     :						str						=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
      _snake_case     :						Tuple						=   pipe.to("cuda"     )

      _snake_case     :						List[Any]						=   "Spiderman is surfing"
      _snake_case     :						Optional[int]						=   torch.Generator(device="cpu"     ).manual_seed(0     )

      _snake_case     :						int						=   pipe(snake_case_						,    generator=snake_case_						,    num_inference_steps=25						,    output_type="pt"     ).frames
      _snake_case     :						int						=   video_frames.cpu().numpy()

      assert np.abs(expected_video - video     ).mean() < 5E-2


    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Any						=   load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"     )

      _snake_case     :						str						=   TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b"     )
      _snake_case     :						int						=   pipe.to("cuda"     )

      _snake_case     :						Any						=   "Spiderman is surfing"
      _snake_case     :						str						=   torch.Generator(device="cpu"     ).manual_seed(0     )

      _snake_case     :						Any						=   pipe(snake_case_						,    generator=snake_case_						,    num_inference_steps=2						,    output_type="pt"     ).frames
      _snake_case     :						Optional[int]						=   video_frames.cpu().numpy()

      assert np.abs(expected_video - video     ).mean() < 5E-2

