
import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class __snake_case						( _lowerCamelCase   ,      unittest.TestCase	):
    _a						:		Dict=							LEDTokenizer
    _a						:		Union[str, Any]=							LEDTokenizerFast
    _a						:		Tuple=							True
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           super().setUp()
           lowercase				:	Optional[Any]			      = [
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """<unk>""",
           ]
           lowercase				:	List[str]			      = dict(zip(__UpperCamelCase						,range(len(__UpperCamelCase ) ) ) )
           lowercase				:	List[str]			      = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           lowercase				:	Dict			      = {"""unk_token""": """<unk>"""}

           lowercase				:	str			      = os.path.join(self.tmpdirname						,VOCAB_FILES_NAMES["""vocab_file"""] )
           lowercase				:	Tuple			      = os.path.join(self.tmpdirname						,VOCAB_FILES_NAMES["""merges_file"""] )
           with open(self.vocab_file						,"""w"""						,encoding="""utf-8""" ) as fp:
                  fp.write(json.dumps(__UpperCamelCase ) + """\n""" )
           with open(self.merges_file						,"""w"""						,encoding="""utf-8""" ) as fp:
                  fp.write("""\n""".join(__UpperCamelCase ) )
    def  _SCREAMING_SNAKE_CASE			(    self						,**snake_case ):



           '''simple docstring'''







           kwargs.update(self.special_tokens_map )
           return self.tokenizer_class.from_pretrained(self.tmpdirname						,**__UpperCamelCase )
    def  _SCREAMING_SNAKE_CASE			(    self						,**snake_case ):



           '''simple docstring'''







           kwargs.update(self.special_tokens_map )
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,**__UpperCamelCase )
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''simple docstring'''







           return "lower newer", "lower newer"
    @cached_property
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           return LEDTokenizer.from_pretrained("""allenai/led-base-16384""" )
    @cached_property
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384""" )
    @require_torch
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	int			      = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           lowercase				:	Any			      = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowercase				:	Tuple			      = tokenizer(__UpperCamelCase						,max_length=len(__UpperCamelCase )						,padding=__UpperCamelCase						,return_tensors="""pt""" )
                  self.assertIsInstance(__UpperCamelCase						,__UpperCamelCase )

                  self.assertEqual((2, 9)						,batch.input_ids.shape )
                  self.assertEqual((2, 9)						,batch.attention_mask.shape )
                  lowercase				:	Any			      = batch.input_ids.tolist()[0]
                  self.assertListEqual(__UpperCamelCase						,__UpperCamelCase )
    @require_torch
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	str			      = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowercase				:	int			      = tokenizer(__UpperCamelCase						,padding=__UpperCamelCase						,return_tensors="""pt""" )
                  self.assertIn("""input_ids"""						,__UpperCamelCase )
                  self.assertIn("""attention_mask"""						,__UpperCamelCase )
                  self.assertNotIn("""labels"""						,__UpperCamelCase )
                  self.assertNotIn("""decoder_attention_mask"""						,__UpperCamelCase )
    @require_torch
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	str			      = [
               """Summary of the text.""",
               """Another summary.""",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowercase				:	Optional[int]			      = tokenizer(text_target=__UpperCamelCase						,max_length=32						,padding="""max_length"""						,return_tensors="""pt""" )
                  self.assertEqual(32						,targets["""input_ids"""].shape[1] )
    @require_torch
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowercase				:	List[str]			      = tokenizer(
                      ["""I am a small frog""" * 1024, """I am a small frog"""]						,padding=__UpperCamelCase						,truncation=__UpperCamelCase						,return_tensors="""pt""" )
                  self.assertIsInstance(__UpperCamelCase						,__UpperCamelCase )
                  self.assertEqual(batch.input_ids.shape						,(2, 5122) )
    @require_torch
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	Optional[Any]			      = ["""A long paragraph for summarization."""]
           lowercase				:	List[str]			      = [
               """Summary of the text.""",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowercase				:	str			      = tokenizer(__UpperCamelCase						,return_tensors="""pt""" )
                  lowercase				:	Dict			      = tokenizer(text_target=__UpperCamelCase						,return_tensors="""pt""" )
                  lowercase				:	Optional[int]			      = inputs["""input_ids"""]
                  lowercase				:	int			      = targets["""input_ids"""]
                  self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
                  self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
                  self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
                  self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
    @require_torch
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowercase				:	str			      = ["""Summary of the text.""", """Another summary."""]
                  lowercase				:	List[Any]			      = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

                  lowercase				:	Tuple			      = tokenizer(__UpperCamelCase						,padding=__UpperCamelCase )
                  lowercase				:	Optional[Any]			      = [[0] * len(__UpperCamelCase ) for x in encoded_output["""input_ids"""]]
                  lowercase				:	Dict			      = tokenizer.pad(__UpperCamelCase )
                  self.assertSequenceEqual(outputs["""global_attention_mask"""]						,__UpperCamelCase )
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           pass
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
                         lowercase				:	Optional[int]			      = self.rust_tokenizer_class.from_pretrained(__UpperCamelCase						,**__UpperCamelCase )
                         lowercase				:	Optional[Any]			      = self.tokenizer_class.from_pretrained(__UpperCamelCase						,**__UpperCamelCase )
                         lowercase				:	Dict			      = """A, <mask> AllenNLP sentence."""
                         lowercase				:	Union[str, Any]			      = tokenizer_r.encode_plus(__UpperCamelCase						,add_special_tokens=__UpperCamelCase						,return_token_type_ids=__UpperCamelCase )
                         lowercase				:	Union[str, Any]			      = tokenizer_p.encode_plus(__UpperCamelCase						,add_special_tokens=__UpperCamelCase						,return_token_type_ids=__UpperCamelCase )
                         self.assertEqual(sum(tokens_r["""token_type_ids"""] )						,sum(tokens_p["""token_type_ids"""] ) )
                         self.assertEqual(
                             sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] )						,sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] )						,)

                         lowercase				:	Optional[int]			      = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
                         lowercase				:	List[str]			      = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
                         self.assertSequenceEqual(tokens_p["""input_ids"""]						,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
                         self.assertSequenceEqual(tokens_r["""input_ids"""]						,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )

                         self.assertSequenceEqual(
                             __UpperCamelCase						,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
                         self.assertSequenceEqual(
                             __UpperCamelCase						,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )





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

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


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     List[Any]:
											torch.manual_seed(0       )
											A      						=    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						, )
											A      						=    DDIMScheduler(
											    beta_start=0.0_0_0_8_5						, beta_end=0.0_1_2						, beta_schedule='scaled_linear'						, clip_sample=__UpperCamelCase						, set_alpha_to_one=__UpperCamelCase						, )
											torch.manual_seed(0       )
											A      						=    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       )
											A      						=    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						, )
											A      						=    CLIPTextModel(__UpperCamelCase       )
											A      						=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

											A      						=    {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											}
											return components


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  str=0       )		->     Optional[int]:
											# 3 frames
											A      						=    floats_tensor((1, 3, 3, 32, 32)						, rng=random.Random(__UpperCamelCase       )       ).to(__UpperCamelCase       )

											if str(__UpperCamelCase       ).startswith('mps'       ):
																		A      						=    torch.manual_seed(__UpperCamelCase       )
											else:
																		A      						=    torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
											A      						=    {
											    '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 __UpperCamelCase						(							self      :  Optional[int]       )		->     str:
											A      						=    'cpu'  # ensure determinism for the device-dependent torch.Generator
											A      						=    self.get_dummy_components()
											A      						=    VideoToVideoSDPipeline(**__UpperCamelCase       )
											A      						=    sd_pipe.to(__UpperCamelCase       )
											sd_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    self.get_dummy_inputs(__UpperCamelCase       )
											A      						=    'np'
											A      						=    sd_pipe(**__UpperCamelCase       ).frames
											A      						=    frames[0][-3:, -3:, -1]

											assert frames[0].shape == (32, 32, 3)
											A      						=    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 __UpperCamelCase						(							self      :  int       )		->     Tuple:
											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 __UpperCamelCase						(							self      :  Optional[int]       )		->     Any:
											pass


				@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'       )
				def __UpperCamelCase						(							self      :  str       )		->     Any:
											pass


				@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'       )
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Optional[Any]:
											pass


				def __UpperCamelCase						(							self      :  Any       )		->     Any:
											return super().test_progress_bar()





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


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Dict:
											A      						=    VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL'						, torch_dtype=torch.floataa       )
											pipe.enable_model_cpu_offload()

											# 10 frames
											A      						=    torch.Generator(device='cpu'       ).manual_seed(0       )
											A      						=    torch.randn((1, 10, 3, 1_024, 576)						, generator=__UpperCamelCase       )
											A      						=    video.to('cuda'       )

											A      						=    'Spiderman is surfing'

											A      						=    pipe(__UpperCamelCase						, video=__UpperCamelCase						, generator=__UpperCamelCase						, num_inference_steps=3						, output_type='pt'       ).frames

											A      						=    np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]       )
											assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array       ).sum() < 1e-2