

"""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						):
		_UpperCAmelCase          =		TextToVideoSDPipeline
		_UpperCAmelCase          =		TEXT_TO_IMAGE_PARAMS
		_UpperCAmelCase          =		TEXT_TO_IMAGE_BATCH_PARAMS
		# No `output_type`.
		_UpperCAmelCase          =		frozenset(
		    [
		        "num_inference_steps",
		        "generator",
		        "latents",
		        "return_dict",
		        "callback",
		        "callback_steps",
		    ]						)

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						torch.manual_seed(0      )
						_lowerCAmelCase       :   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    ,)
						_lowerCAmelCase       :   List[Any]  =    DDIMScheduler(
						    beta_start=0.0_0_0_8_5    ,beta_end=0.0_1_2    ,beta_schedule='scaled_linear'    ,clip_sample=snake_case_    ,set_alpha_to_one=snake_case_    ,)
						torch.manual_seed(0      )
						_lowerCAmelCase       :   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=128    ,)
						torch.manual_seed(0      )
						_lowerCAmelCase       :   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=1000    ,hidden_act='gelu'    ,projection_dim=512    ,)
						_lowerCAmelCase       :   Tuple  =    CLIPTextModel(snake_case_      )
						_lowerCAmelCase       :   Optional[int]  =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

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

		def __lowerCamelCase		(		self    ,_A    ,_A=0      ):
						'''simple docstring'''

						if str(snake_case_      ).startswith('mps'      ):
										_lowerCAmelCase       :   str  =    torch.manual_seed(snake_case_      )
						else:
										_lowerCAmelCase       :   Union[str, Any]  =    torch.Generator(device=snake_case_      ).manual_seed(snake_case_      )
						_lowerCAmelCase       :   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      ):
						'''simple docstring'''

						_lowerCAmelCase       :   int  =    "cpu"  # ensure determinism for the device-dependent torch.Generator
						_lowerCAmelCase       :   Optional[Any]  =    self.get_dummy_components()
						_lowerCAmelCase       :   Tuple  =    TextToVideoSDPipeline(**snake_case_      )
						_lowerCAmelCase       :   List[str]  =    sd_pipe.to(snake_case_      )
						sd_pipe.set_progress_bar_config(disable=snake_case_      )

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

						assert frames[0].shape == (64, 64, 3)
						_lowerCAmelCase       :   Dict  =    np.array([1_5_8.0, 1_6_0.0, 1_5_3.0, 1_2_5.0, 1_0_0.0, 1_2_1.0, 1_1_1.0, 9_3.0, 1_1_3.0]      )

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

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						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      ):
						'''simple docstring'''

						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      ):
						'''simple docstring'''

						pass

		@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'      )
		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						pass

		@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'      )
		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						pass







		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						return super().test_progress_bar()







@slow
@skip_mps
class 	__UpperCamelCase    (    unittest.TestCase						):

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   List[Any]  =    load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy'      )

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

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

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

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







		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Any  =    load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy'      )

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

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

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

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








"""simple docstring"""






from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin







@dataclass
class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		42







class 	__UpperCamelCase    (    a__							,							a__						):

  @register_to_config
  def __init__(		self    ,_A = 32    ,_A = 64    ,_A = 20    ,_A = 768    ,_A=77    ,_A=4    ,_A = 0.0    ,_A = "silu"    ,_A = None    ,_A = None    ,_A = "linear"    ,_A = "prd"    ,_A = None    ,_A = None    ,_A = None    ,):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Any  =    num_attention_heads
      _lowerCAmelCase       :   Optional[int]  =    attention_head_dim
      _lowerCAmelCase       :   Tuple  =    num_attention_heads * attention_head_dim
      _lowerCAmelCase       :   Optional[Any]  =    additional_embeddings

      _lowerCAmelCase       :   Union[str, Any]  =    time_embed_dim or inner_dim
      _lowerCAmelCase       :   Union[str, Any]  =    embedding_proj_dim or embedding_dim
      _lowerCAmelCase       :   Optional[int]  =    clip_embed_dim or embedding_dim

      _lowerCAmelCase       :   int  =    Timesteps(_A    ,_A    ,0      )
      _lowerCAmelCase       :   int  =    TimestepEmbedding(_A    ,_A    ,out_dim=_A    ,act_fn=_A      )

      _lowerCAmelCase       :   List[Any]  =    nn.Linear(_A    ,_A      )

      if embedding_proj_norm_type is None:
          _lowerCAmelCase       :   Optional[Any]  =    None
      elif embedding_proj_norm_type == "layer":
          _lowerCAmelCase       :   List[Any]  =    nn.LayerNorm(_A      )
      else:
          raise ValueError(F"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""      )

      _lowerCAmelCase       :   Tuple  =    nn.Linear(_A    ,_A      )

      if encoder_hid_proj_type is None:
          _lowerCAmelCase       :   int  =    None
      elif encoder_hid_proj_type == "linear":
          _lowerCAmelCase       :   List[Any]  =    nn.Linear(_A    ,_A      )
      else:
          raise ValueError(F"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""      )

      _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,num_embeddings + additional_embeddings    ,_A      )      )

      if added_emb_type == "prd":
          _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,1    ,_A      )      )
      elif added_emb_type is None:
          _lowerCAmelCase       :   List[Any]  =    None
      else:
          raise ValueError(
              F"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."""      )

      _lowerCAmelCase       :   List[Any]  =    nn.ModuleList(
          [
              BasicTransformerBlock(
                  _A    ,_A    ,_A    ,dropout=_A    ,activation_fn='gelu'    ,attention_bias=_A    ,)
              for d in range(_A      )
          ]      )

      if norm_in_type == "layer":
          _lowerCAmelCase       :   Any  =    nn.LayerNorm(_A      )
      elif norm_in_type is None:
          _lowerCAmelCase       :   Any  =    None
      else:
          raise ValueError(F"""Unsupported norm_in_type: {norm_in_type}."""      )

      _lowerCAmelCase       :   Union[str, Any]  =    nn.LayerNorm(_A      )

      _lowerCAmelCase       :   int  =    nn.Linear(_A    ,_A      )

      _lowerCAmelCase       :   Any  =    torch.full(
          [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]    ,-1_0_0_0_0.0      )
      causal_attention_mask.triu_(1      )
      _lowerCAmelCase       :   Tuple  =    causal_attention_mask[None, ...]
      self.register_buffer('causal_attention_mask'    ,_A    ,persistent=_A      )

      _lowerCAmelCase       :   Tuple  =    nn.Parameter(torch.zeros(1    ,_A      )      )
      _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,_A      )      )

  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    {}

      def fn_recursive_add_processors(_A    ,_A    ,_A      ):
          if hasattr(_A    ,'set_processor'      ):
              _lowerCAmelCase       :   str  =    module.processor

          for sub_name, child in module.named_children():
              fn_recursive_add_processors(F"""{name}.{sub_name}"""    ,_A    ,_A      )

          return processors

      for name, module in self.named_children():
          fn_recursive_add_processors(_A    ,_A    ,_A      )

      return processors

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    len(self.attn_processors.keys()      )

      if isinstance(_A    ,_A      ) and len(_A      ) != count:
          raise ValueError(
              F"""A dict of processors was passed, but the number of processors {len(_A      )} does not match the"""
              F""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""      )

      def fn_recursive_attn_processor(_A    ,_A    ,_A      ):
          if hasattr(_A    ,'set_processor'      ):
              if not isinstance(_A    ,_A      ):
                  module.set_processor(_A      )
              else:
                  module.set_processor(processor.pop(F"""{name}.processor"""      )      )

          for sub_name, child in module.named_children():
              fn_recursive_attn_processor(F"""{name}.{sub_name}"""    ,_A    ,_A      )

      for name, module in self.named_children():
          fn_recursive_attn_processor(_A    ,_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.set_attn_processor(AttnProcessor()      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A = None    ,_A = None    ,_A = True    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    hidden_states.shape[0]

      _lowerCAmelCase       :   int  =    timestep
      if not torch.is_tensor(_A      ):
          _lowerCAmelCase       :   str  =    torch.tensor([timesteps]    ,dtype=torch.long    ,device=hidden_states.device      )
      elif torch.is_tensor(_A      ) and len(timesteps.shape      ) == 0:
          _lowerCAmelCase       :   Dict  =    timesteps[None].to(hidden_states.device      )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      _lowerCAmelCase       :   Optional[int]  =    timesteps * torch.ones(_A    ,dtype=timesteps.dtype    ,device=timesteps.device      )

      _lowerCAmelCase       :   Dict  =    self.time_proj(_A      )

      # timesteps does not contain any weights and will always return f32 tensors
      # but time_embedding might be fp16, so we need to cast here.
      _lowerCAmelCase       :   Any  =    timesteps_projected.to(dtype=self.dtype      )
      _lowerCAmelCase       :   Optional[Any]  =    self.time_embedding(_A      )

      if self.embedding_proj_norm is not None:
          _lowerCAmelCase       :   int  =    self.embedding_proj_norm(_A      )

      _lowerCAmelCase       :   str  =    self.embedding_proj(_A      )
      if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
          _lowerCAmelCase       :   str  =    self.encoder_hidden_states_proj(_A      )
      elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
          raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'      )

      _lowerCAmelCase       :   Any  =    self.proj_in(_A      )

      _lowerCAmelCase       :   Dict  =    self.positional_embedding.to(hidden_states.dtype      )

      _lowerCAmelCase       :   List[Any]  =    []
      _lowerCAmelCase       :   Optional[Any]  =    0

      if encoder_hidden_states is not None:
          additional_embeds.append(_A      )
          additional_embeddings_len += encoder_hidden_states.shape[1]

      if len(proj_embeddings.shape      ) == 2:
          _lowerCAmelCase       :   int  =    proj_embeddings[:, None, :]

      if len(hidden_states.shape      ) == 2:
          _lowerCAmelCase       :   Any  =    hidden_states[:, None, :]

      _lowerCAmelCase       :   int  =    additional_embeds + [
          proj_embeddings,
          time_embeddings[:, None, :],
          hidden_states,
      ]

      if self.prd_embedding is not None:
          _lowerCAmelCase       :   List[str]  =    self.prd_embedding.to(hidden_states.dtype      ).expand(_A    ,-1    ,-1      )
          additional_embeds.append(_A      )

      _lowerCAmelCase       :   List[str]  =    torch.cat(
          _A    ,dim=1    ,)

      # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
      _lowerCAmelCase       :   Tuple  =    additional_embeddings_len + proj_embeddings.shape[1] + 1
      if positional_embeddings.shape[1] < hidden_states.shape[1]:
          _lowerCAmelCase       :   Any  =    F.pad(
              _A    ,(
                  0,
                  0,
                  additional_embeddings_len,
                  self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
              )    ,value=0.0    ,)

      _lowerCAmelCase       :   int  =    hidden_states + positional_embeddings

      if attention_mask is not None:
          _lowerCAmelCase       :   Optional[Any]  =    (1 - attention_mask.to(hidden_states.dtype      )) * -1_0_0_0_0.0
          _lowerCAmelCase       :   Union[str, Any]  =    F.pad(_A    ,(0, self.additional_embeddings)    ,value=0.0      )
          _lowerCAmelCase       :   Tuple  =    (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype      )
          _lowerCAmelCase       :   Optional[Any]  =    attention_mask.repeat_interleave(self.config.num_attention_heads    ,dim=0      )

      if self.norm_in is not None:
          _lowerCAmelCase       :   Any  =    self.norm_in(_A      )

      for block in self.transformer_blocks:
          _lowerCAmelCase       :   int  =    block(_A    ,attention_mask=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.norm_out(_A      )

      if self.prd_embedding is not None:
          _lowerCAmelCase       :   Optional[int]  =    hidden_states[:, -1]
      else:
          _lowerCAmelCase       :   Any  =    hidden_states[:, additional_embeddings_len:]

      _lowerCAmelCase       :   Optional[int]  =    self.proj_to_clip_embeddings(_A      )

      if not return_dict:
          return (predicted_image_embedding,)

      return PriorTransformerOutput(predicted_image_embedding=_A      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    (prior_latents * self.clip_std) + self.clip_mean
      return prior_latents





