




import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class __snake_case			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    Dict          =  BarthezTokenizer
   UpperCAmelCase__  :    Optional[Any]          =  BarthezTokenizerFast
   UpperCAmelCase__  :    Union[str, Any]          =  True
   UpperCAmelCase__  :    Optional[Any]          =  True







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        super().setUp()

        UpperCAmelCase_          =							BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''')
        tokenizer.save_pretrained(self.tmpdirname)
        tokenizer.save_pretrained(self.tmpdirname					,						legacy_format=_snake_case)
        UpperCAmelCase_          =							tokenizer







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							'''<pad>'''
        UpperCAmelCase_          =							1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(_snake_case)					,						_snake_case)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(_snake_case)					,						_snake_case)







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0]					,						'''<s>''')
        self.assertEqual(vocab_keys[1]					,						'''<pad>''')
        self.assertEqual(vocab_keys[-1]					,						'''<mask>''')
        self.assertEqual(len(_snake_case)					,						101122)







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

        """simple docstring"""


        self.assertEqual(self.get_tokenizer().vocab_size					,						101122)







   @require_torch
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_          =							[0, 57, 3018, 70307, 91, 2]

        UpperCAmelCase_          =							self.tokenizer(
            _snake_case					,						max_length=len(_snake_case)					,						padding=_snake_case					,						truncation=_snake_case					,						return_tensors='''pt''')
        self.assertIsInstance(_snake_case					,						_snake_case)

        self.assertEqual((2, 6)					,						batch.input_ids.shape)
        self.assertEqual((2, 6)					,						batch.attention_mask.shape)
        UpperCAmelCase_          =							batch.input_ids.tolist()[0]
        self.assertListEqual(_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        if not self.test_rust_tokenizer:
             return

        UpperCAmelCase_          =							self.get_tokenizer()
        UpperCAmelCase_          =							self.get_rust_tokenizer()

        UpperCAmelCase_          =							'''I was born in 92000, and this is falsé.'''

        UpperCAmelCase_          =							tokenizer.tokenize(_snake_case)
        UpperCAmelCase_          =							rust_tokenizer.tokenize(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)

        UpperCAmelCase_          =							tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
        UpperCAmelCase_          =							rust_tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)

        UpperCAmelCase_          =							self.get_rust_tokenizer()
        UpperCAmelCase_          =							tokenizer.encode(_snake_case)
        UpperCAmelCase_          =							rust_tokenizer.encode(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)







   @slow
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							{'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # moussaKam/mbarthez is a french model. So we also use french texts.
        UpperCAmelCase_          =							[
            '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
            '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
            '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
            '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
            '''telles que la traduction et la synthèse de texte.''',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=_snake_case					,						model_name='''moussaKam/mbarthez'''					,						revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''					,						sequences=_snake_case					,						)














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					(       A__		,unittest.TestCase	):

				"""simple docstring"""




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




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__    =			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,  )
								lowercase__    =			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  )
								lowercase__    =			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  )
								lowercase__    =			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,  )
								lowercase__    =			CLIPTextModel(lowerCamelCase  )
								lowercase__    =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

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




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    int=0  ):

								'''simple docstring'''
								if str(lowerCamelCase  ).startswith('''mps'''  ):
												lowercase__    =			torch.manual_seed(lowerCamelCase  )
								else:
												lowercase__    =			torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )
								lowercase__    =			{
								    '''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 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__    =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowercase__    =			self.get_dummy_components()
								lowercase__    =			TextToVideoSDPipeline(**lowerCamelCase  )
								lowercase__    =			sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

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

								assert frames[0].shape == (64, 64, 3)
								lowercase__    =			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 				lowercase__   (     self						:    str  ):

								'''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 				lowercase__   (     self						:    Optional[int]  ):

								'''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 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								pass




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

								'''simple docstring'''
								pass




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

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								return super().test_progress_bar()



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

				"""simple docstring"""




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy'''  )

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

								lowercase__    =			'''Spiderman is surfing'''
								lowercase__    =			torch.Generator(device='''cpu'''  ).manual_seed(0  )

								lowercase__    =			pipe(lowerCamelCase,  generator=lowerCamelCase,  num_inference_steps=25,  output_type='''pt'''  ).frames
								lowercase__    =			video_frames.cpu().numpy()

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




				def 				lowercase__   (     self						:    int  ):

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

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

								lowercase__    =			'''Spiderman is surfing'''
								lowercase__    =			torch.Generator(device='''cpu'''  ).manual_seed(0  )

								lowercase__    =			pipe(lowerCamelCase,  generator=lowerCamelCase,  num_inference_steps=2,  output_type='''pt'''  ).frames
								lowercase__    =			video_frames.cpu().numpy()

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




