





from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching



def    __lowercase   (      ) ->   Optional[int]:


      '''simple docstring'''


      import os as original_os
      from os import path as original_path
      from os import rename as original_rename
      from os.path import dirname as original_dirname
      from os.path import join as original_join

      assert _test_patching.os is original_os
      assert _test_patching.path is original_path
      assert _test_patching.join is original_join

      assert _test_patching.renamed_os is original_os
      assert _test_patching.renamed_path is original_path
      assert _test_patching.renamed_join is original_join

      SCREAMING_SNAKE_CASE		=						"""__test_patch_submodule_mock__"""
      with patch_submodule(_test_patching ,						"""os.path.join""" ,						_SCREAMING_SNAKE_CASE	):
            # Every way to access os.path.join must be patched, and the rest must stay untouched

            # check os.path.join
            assert isinstance(_test_patching.os ,						_PatchedModuleObj	)
            assert isinstance(_test_patching.os.path ,						_PatchedModuleObj	)
            assert _test_patching.os.path.join is mock

            # check path.join
            assert isinstance(_test_patching.path ,						_PatchedModuleObj	)
            assert _test_patching.path.join is mock

            # check join
            assert _test_patching.join is mock

            # check that the other attributes are untouched
            assert _test_patching.os.rename is original_rename
            assert _test_patching.path.dirname is original_dirname
            assert _test_patching.os.path.dirname is original_dirname

            # Even renamed modules or objects must be patched

            # check renamed_os.path.join
            assert isinstance(_test_patching.renamed_os ,						_PatchedModuleObj	)
            assert isinstance(_test_patching.renamed_os.path ,						_PatchedModuleObj	)
            assert _test_patching.renamed_os.path.join is mock

            # check renamed_path.join
            assert isinstance(_test_patching.renamed_path ,						_PatchedModuleObj	)
            assert _test_patching.renamed_path.join is mock

            # check renamed_join
            assert _test_patching.renamed_join is mock

            # check that the other attributes are untouched
            assert _test_patching.renamed_os.rename is original_rename
            assert _test_patching.renamed_path.dirname is original_dirname
            assert _test_patching.renamed_os.path.dirname is original_dirname

      # check that everthing is back to normal when the patch is over

      assert _test_patching.os is original_os
      assert _test_patching.path is original_path
      assert _test_patching.join is original_join

      assert _test_patching.renamed_os is original_os
      assert _test_patching.renamed_path is original_path
      assert _test_patching.renamed_join is original_join



def    __lowercase   (      ) ->   str:


      '''simple docstring'''


      assert _test_patching.open is open

      SCREAMING_SNAKE_CASE		=						"""__test_patch_submodule_builtin_mock__"""
      # _test_patching has "open" in its globals
      assert _test_patching.open is open
      with patch_submodule(_test_patching ,						"""open""" ,						_SCREAMING_SNAKE_CASE	):
            assert _test_patching.open is mock

      # check that everthing is back to normal when the patch is over

      assert _test_patching.open is open



def    __lowercase   (      ) ->   Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						"""__test_patch_submodule_missing_mock__"""
      with patch_submodule(_test_patching ,						"""pandas.read_csv""" ,						_SCREAMING_SNAKE_CASE	):
            pass



def    __lowercase   (      ) ->   Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						"""__test_patch_submodule_missing_builtin_mock__"""
      # _test_patching doesn't have "len" in its globals
      assert getattr(_test_patching ,						"""len""" ,						_SCREAMING_SNAKE_CASE	) is None
      with patch_submodule(_test_patching ,						"""len""" ,						_SCREAMING_SNAKE_CASE	):
            assert _test_patching.len is mock
      assert _test_patching.len is len



def    __lowercase   (      ) ->   List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						"""__test_patch_submodule_start_and_stop_mock__"""
      SCREAMING_SNAKE_CASE		=						patch_submodule(_test_patching ,						"""open""" ,						_SCREAMING_SNAKE_CASE	)
      assert _test_patching.open is open
      patch.start()
      assert _test_patching.open is mock
      patch.stop()
      assert _test_patching.open is open



def    __lowercase   (      ) ->   Any:


      '''simple docstring'''


      from os import rename as original_rename
      from os.path import dirname as original_dirname
      from os.path import join as original_join

      SCREAMING_SNAKE_CASE		=						"""__test_patch_submodule_successive_join__"""
      SCREAMING_SNAKE_CASE		=						"""__test_patch_submodule_successive_dirname__"""
      SCREAMING_SNAKE_CASE		=						"""__test_patch_submodule_successive_rename__"""
      assert _test_patching.os.path.join is original_join
      assert _test_patching.os.path.dirname is original_dirname
      assert _test_patching.os.rename is original_rename

      with patch_submodule(_test_patching ,						"""os.path.join""" ,						_SCREAMING_SNAKE_CASE	):
            with patch_submodule(_test_patching ,						"""os.rename""" ,						_SCREAMING_SNAKE_CASE	):
                  with patch_submodule(_test_patching ,						"""os.path.dirname""" ,						_SCREAMING_SNAKE_CASE	):
                        assert _test_patching.os.path.join is mock_join
                        assert _test_patching.os.path.dirname is mock_dirname
                        assert _test_patching.os.rename is mock_rename

    # try another order
      with patch_submodule(_test_patching ,						"""os.rename""" ,						_SCREAMING_SNAKE_CASE	):
            with patch_submodule(_test_patching ,						"""os.path.join""" ,						_SCREAMING_SNAKE_CASE	):
                  with patch_submodule(_test_patching ,						"""os.path.dirname""" ,						_SCREAMING_SNAKE_CASE	):
                        assert _test_patching.os.path.join is mock_join
                        assert _test_patching.os.path.dirname is mock_dirname
                        assert _test_patching.os.rename is mock_rename

      assert _test_patching.os.path.join is original_join
      assert _test_patching.os.path.dirname is original_dirname
      assert _test_patching.os.rename is original_rename



def    __lowercase   (      ) ->   str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						"""__test_patch_submodule_doesnt_exist_mock__"""
      with patch_submodule(_test_patching ,						"""__module_that_doesn_exist__.__attribute_that_doesn_exist__""" ,						_SCREAMING_SNAKE_CASE	):
            pass
      with patch_submodule(_test_patching ,						"""os.__attribute_that_doesn_exist__""" ,						_SCREAMING_SNAKE_CASE	):
            pass









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__ (      lowerCAmelCase_							,      unittest.TestCase   ):



     '''simple docstring'''

     __snake_case    :	List[str]			=			TextToVideoSDPipeline
     __snake_case    :	int			=			TEXT_TO_IMAGE_PARAMS
     __snake_case    :	Tuple			=			TEXT_TO_IMAGE_BATCH_PARAMS
     # No `output_type`.
     __snake_case    :	Dict			=			frozenset(
         [
             "num_inference_steps",
             "generator",
             "latents",
             "return_dict",
             "callback",
             "callback_steps",
         ]   )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Optional[int]:




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						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						,)
           SCREAMING_SNAKE_CASE		=						DDIMScheduler(
               beta_start=0.00085						,beta_end=0.012						,beta_schedule="""scaled_linear"""						,clip_sample=lowerCamelCase__						,set_alpha_to_one=lowerCamelCase__						,)
           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						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       )
           SCREAMING_SNAKE_CASE		=						CLIPTextConfig(
               bos_token_id=0						,eos_token_id=2						,hidden_size=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						,hidden_act="""gelu"""						,projection_dim=512						,)
           SCREAMING_SNAKE_CASE		=						CLIPTextModel(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    Optional[Any]						,lowerCamelCase__						:    int=0       )     ->		List[Any]:




           '''simple docstring'''


           if str(lowerCamelCase__       ).startswith("""mps"""       ):
                 SCREAMING_SNAKE_CASE		=						torch.manual_seed(lowerCamelCase__       )
           else:
                 SCREAMING_SNAKE_CASE		=						torch.Generator(device=lowerCamelCase__       ).manual_seed(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						{
               """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 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						sd_pipe.to(lowerCamelCase__       )
           sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

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

           assert frames[0].shape == (64, 64, 3)
           SCREAMING_SNAKE_CASE		=						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 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		int:




           '''simple docstring'''


           self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__						,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 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Any:




           '''simple docstring'''


           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__						,expected_max_diff=1e-2       )






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




           '''simple docstring'''


           pass






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




           '''simple docstring'''


           pass






     @unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""       )
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Tuple:




           '''simple docstring'''


           pass






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]       )     ->		Optional[Any]:




           '''simple docstring'''


           return super().test_progress_bar()

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



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"""       )

           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""       )
           SCREAMING_SNAKE_CASE		=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
           SCREAMING_SNAKE_CASE		=						pipe.to("""cuda"""       )

           SCREAMING_SNAKE_CASE		=						"""Spiderman is surfing"""
           SCREAMING_SNAKE_CASE		=						torch.Generator(device="""cpu"""       ).manual_seed(0       )

           SCREAMING_SNAKE_CASE		=						pipe(lowerCamelCase__						,generator=lowerCamelCase__						,num_inference_steps=25						,output_type="""pt"""       ).frames
           SCREAMING_SNAKE_CASE		=						video_frames.cpu().numpy()

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Any:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""       )
           SCREAMING_SNAKE_CASE		=						pipe.to("""cuda"""       )

           SCREAMING_SNAKE_CASE		=						"""Spiderman is surfing"""
           SCREAMING_SNAKE_CASE		=						torch.Generator(device="""cpu"""       ).manual_seed(0       )

           SCREAMING_SNAKE_CASE		=						pipe(lowerCamelCase__						,generator=lowerCamelCase__						,num_inference_steps=2						,output_type="""pt"""       ).frames
           SCREAMING_SNAKE_CASE		=						video_frames.cpu().numpy()

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


