

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		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				Dict									=							TextToVideoSDPipeline
			__snake_case    :				int									=							TEXT_TO_IMAGE_PARAMS
			__snake_case    :				Optional[Any]									=							TEXT_TO_IMAGE_BATCH_PARAMS
			# No `output_type`.
			__snake_case    :				int									=							frozenset(
			    [
			        "num_inference_steps",
			        "generator",
			        "latents",
			        "return_dict",
			        "callback",
			        "callback_steps",
			    ]   )







			def 							UpperCamelCase				(     self:  Tuple					):







									'''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=0.0_00_85				,     beta_end=0.0_12				,     beta_schedule="""scaled_linear"""				,     clip_sample=UpperCAmelCase_				,     set_alpha_to_one=UpperCAmelCase_				,     )
									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-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_000				,     hidden_act="""gelu"""				,     projection_dim=512				,     )
									_SCREAMING_SNAKE_CASE     				=  CLIPTextModel(UpperCAmelCase_					)
									_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 							UpperCamelCase				(     self:  Any				,     UpperCAmelCase_:  Any				,     UpperCAmelCase_:  Dict=0					):







									'''simple docstring'''




									if str(UpperCAmelCase_					).startswith("""mps"""					):
															_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									else:
															_SCREAMING_SNAKE_CASE     				=  torch.Generator(device=UpperCAmelCase_					).manual_seed(UpperCAmelCase_					)
									_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 							UpperCamelCase				(     self:  Dict					):







									'''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(**UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_inputs(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  """np"""
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(**UpperCAmelCase_					).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 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									self._test_attention_slicing_forward_pass(test_mean_pixel_difference=UpperCAmelCase_				,     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 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=UpperCAmelCase_				,     expected_max_diff=1E-2					)







			@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""					)
			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									pass







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







									'''simple docstring'''




									pass







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







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									return super().test_progress_bar()



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







			def 							UpperCamelCase				(     self:  Dict					):







									'''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(UpperCAmelCase_				,     generator=UpperCAmelCase_				,     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 							UpperCamelCase				(     self:  List[str]					):







									'''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(UpperCAmelCase_				,     generator=UpperCAmelCase_				,     num_inference_steps=2				,     output_type="""pt"""					).frames
									_SCREAMING_SNAKE_CASE     				=  video_frames.cpu().numpy()

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









import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				Optional[Any]									=							OpenAIGPTTokenizer
			__snake_case    :				Dict									=							OpenAIGPTTokenizerFast
			__snake_case    :				Optional[Any]									=							True
			__snake_case    :				Dict									=							False







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									_SCREAMING_SNAKE_CASE     				=  [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """w</w>""",
									    """r</w>""",
									    """t</w>""",
									    """lo""",
									    """low""",
									    """er</w>""",
									    """low</w>""",
									    """lowest</w>""",
									    """newer</w>""",
									    """wider</w>""",
									    """<unk>""",
									]
									_SCREAMING_SNAKE_CASE     				=  dict(zip(UpperCAmelCase_				,     range(len(UpperCAmelCase_					)					)					)					)
									_SCREAMING_SNAKE_CASE     				=  ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""vocab_file"""]					)
									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""merges_file"""]					)
									with open(self.vocab_file				,     """w"""					) as fp:
															fp.write(json.dumps(UpperCAmelCase_					)					)
									with open(self.merges_file				,     """w"""					) as fp:
															fp.write("""\n""".join(UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  List[Any]				,     UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									return "lower newer", "lower newer"







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OpenAIGPTTokenizer(self.vocab_file				,     self.merges_file					)

									_SCREAMING_SNAKE_CASE     				=  """lower"""
									_SCREAMING_SNAKE_CASE     				=  ["""low""", """er</w>"""]
									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize(UpperCAmelCase_					)
									self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokens + ["""<unk>"""]
									_SCREAMING_SNAKE_CASE     				=  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Union[str, Any]=15					):







									'''simple docstring'''




									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_					)

																					# Simple input
																					_SCREAMING_SNAKE_CASE     				=  """This is a simple input"""
																					_SCREAMING_SNAKE_CASE     				=  ["""This is a simple input 1""", """This is a simple input 2"""]
																					_SCREAMING_SNAKE_CASE     				=  ("""This is a simple input""", """This is a pair""")
																					_SCREAMING_SNAKE_CASE     				=  [
																					    ("""This is a simple input 1""", """This is a simple input 2"""),
																					    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																					]

																					# Simple input tests
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Simple input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Simple input
																					self.assertRaises(
																					    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""				,     )

																					# Pair input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Pair input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Pair input
																					self.assertRaises(
																					    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""				,     )







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass



@require_ftfy
@require_spacy
@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			pass

