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        UpperCamelCase						(				lowerCAmelCase__	,	unittest.TestCase				):
						a__		:Tuple      =			VideoToVideoSDPipeline
						a__		:Tuple      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'''video'''}				) - {'''image''', '''width''', '''height'''}
						a__		:List[Any]      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''video'''}				) - {'''image'''}
						a__		:int      =			PipelineTesterMixin.required_optional_params - {'''latents'''}
						a__		:Optional[int]      =			False

						# No `output_type`.
						a__		:Optional[Any]      =			frozenset(
						    [
						        '''num_inference_steps''',
						        '''generator''',
						        '''latents''',
						        '''return_dict''',
						        '''callback''',
						        '''callback_steps''',
						    ]				)





						def        A_							(self						)				->				Optional[int]:
												torch.manual_seed(0						)
												UpperCamelCase_		:							Union[str, Any]  =		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			,     )
												UpperCamelCase_		:							int  =		DDIMScheduler(
												    beta_start=0.00_085			,     beta_end=0.012			,     beta_schedule="""scaled_linear"""			,     clip_sample=__UpperCAmelCase			,     set_alpha_to_one=__UpperCAmelCase			,     )
												torch.manual_seed(0						)
												UpperCamelCase_		:							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						)
												UpperCamelCase_		:							List[str]  =		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=1_000			,     hidden_act="""gelu"""			,     projection_dim=512			,     )
												UpperCamelCase_		:							Dict  =		CLIPTextModel(__UpperCAmelCase						)
												UpperCamelCase_		:							str  =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

												UpperCamelCase_		:							Union[str, Any]  =		{
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """vae""": vae,
												    """text_encoder""": text_encoder,
												    """tokenizer""": tokenizer,
												}
												return components





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=0						)				->				List[Any]:
												UpperCamelCase_		:							Tuple  =		floats_tensor((1, 3, 3, 32, 32)			,     rng=random.Random(__UpperCAmelCase						)						).to(__UpperCAmelCase						)

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

												UpperCamelCase_		:							List[str]  =		self.get_dummy_inputs(__UpperCAmelCase						)
												UpperCamelCase_		:							str  =		"""np"""
												UpperCamelCase_		:							List[str]  =		sd_pipe(**__UpperCAmelCase						).frames
												UpperCamelCase_		:							Optional[Any]  =		frames[0][-3:, -3:, -1]

												assert frames[0].shape == (32, 32, 3)
												UpperCamelCase_		:							Optional[int]  =		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        A_							(self						)				->				Dict:
												self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__UpperCAmelCase			,     expected_max_diff=5E-3						)





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





						@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""						)
						def        A_							(self						)				->				int:
												pass





						@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""						)
						def        A_							(self						)				->				Tuple:
												pass





						def        A_							(self						)				->				List[Any]:
												return super().test_progress_bar()


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





						def        A_							(self						)				->				str:
												UpperCamelCase_		:							Tuple  =		VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL"""			,     torch_dtype=torch.floataa						)
												pipe.enable_model_cpu_offload()

												# 10 frames
												UpperCamelCase_		:							Optional[Any]  =		torch.Generator(device="""cpu"""						).manual_seed(0						)
												UpperCamelCase_		:							List[Any]  =		torch.randn((1, 10, 3, 1_024, 576)			,     generator=__UpperCAmelCase						)
												UpperCamelCase_		:							int  =		video.to("""cuda"""						)

												UpperCamelCase_		:							Optional[Any]  =		"""Spiderman is surfing"""

												UpperCamelCase_		:							List[str]  =		pipe(__UpperCAmelCase			,     video=__UpperCAmelCase			,     generator=__UpperCAmelCase			,     num_inference_steps=3			,     output_type="""pt"""						).frames

												UpperCamelCase_		:							Optional[Any]  =		np.array([-1.0_458_984, -1.1_279_297, -0.9_663_086, -0.91_503_906, -0.75_097_656]						)
												assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array						).sum() < 1E-2











import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 			__lowerCAmelCase  (						lowerCAmelCase__	,						unittest.TestCase ):
 lowerCAmelCase__							       =	XLMTokenizer
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __lowerCamelCase    				=     [
        '''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>''',
    ]
    __lowerCamelCase    				=     dict(zip(__UpperCAmelCase						,					range(len(__UpperCAmelCase					)					)					)					)
    __lowerCamelCase    				=     ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''vocab_file''']					)
    __lowerCamelCase    				=     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 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''lower newer'''
    __lowerCamelCase    				=     '''lower newer'''
    return input_text, output_text




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer(self.vocab_file						,					self.merges_file					)

    __lowerCamelCase    				=     '''lower'''
    __lowerCamelCase    				=     ['''low''', '''er</w>''']
    __lowerCamelCase    				=     tokenizer.tokenize(__UpperCAmelCase					)
    self.assertListEqual(__UpperCAmelCase						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     tokens + ['''<unk>''']
    __lowerCamelCase    				=     [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase					)						,					__UpperCAmelCase					)




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''					)

    __lowerCamelCase    				=     tokenizer.encode('''sequence builders'''						,					add_special_tokens=__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.encode('''multi-sequence build'''						,					add_special_tokens=__UpperCAmelCase					)

    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						,					__UpperCAmelCase					)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]






