





import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_		  =			get_tests_dir("""fixtures/spiece.model""")

@require_sentencepiece
@require_tokenizers
class 						UpperCamelCase__					( __lowercase				, unittest.TestCase  ):



     '''simple docstring'''

     __snake_case       :      Dict						      =     ErnieMTokenizer
     __snake_case       :      List[Any]						      =     False
     __snake_case       :      Optional[int]						      =     True
     __snake_case       :      Any						      =     False
     __snake_case       :      int						      =     False






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




           '''simple docstring'''


           super().setUp()

           # We have a SentencePiece fixture for testing
           SCREAMING_SNAKE_CASE						=		ErnieMTokenizer(__A		,unk_token="""<unk>"""		,pad_token="""<pad>"""			)
           tokenizer.save_pretrained(self.tmpdirname			)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		"""this is a test"""
           SCREAMING_SNAKE_CASE						=		"""this is a test"""
           return input_text, output_text






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		"""<pad>"""
           SCREAMING_SNAKE_CASE						=		0

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






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




           '''simple docstring'''


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

           self.assertEqual(vocab_keys[0]		,"""<pad>"""			)
           self.assertEqual(vocab_keys[1]		,"""<unk>"""			)
           self.assertEqual(vocab_keys[-1]		,"""▁eloquent"""			)
           self.assertEqual(len(__A			)		,30000			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]			)	->						int:




           '''simple docstring'''


           self.assertEqual(self.get_tokenizer().vocab_size		,30000			)






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




           '''simple docstring'''


           if not self.test_rust_tokenizer:
                 return

           SCREAMING_SNAKE_CASE						=		self.get_tokenizer()
           SCREAMING_SNAKE_CASE						=		self.get_rust_tokenizer()

           SCREAMING_SNAKE_CASE						=		"""I was born in 92000, and this is falsé."""

           SCREAMING_SNAKE_CASE						=		tokenizer.tokenize(__A			)
           SCREAMING_SNAKE_CASE						=		rust_tokenizer.tokenize(__A			)
           self.assertListEqual(__A		,__A			)

           SCREAMING_SNAKE_CASE						=		tokenizer.encode(__A		,add_special_tokens=__A			)
           SCREAMING_SNAKE_CASE						=		rust_tokenizer.encode(__A		,add_special_tokens=__A			)
           self.assertListEqual(__A		,__A			)

           SCREAMING_SNAKE_CASE						=		self.get_rust_tokenizer()
           SCREAMING_SNAKE_CASE						=		tokenizer.encode(__A			)
           SCREAMING_SNAKE_CASE						=		rust_tokenizer.encode(__A			)
           self.assertListEqual(__A		,__A			)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ErnieMTokenizer(__A		,do_lower_case=__A		,unk_token="""<unk>"""		,pad_token="""<pad>"""			)

           SCREAMING_SNAKE_CASE						=		tokenizer.tokenize("""This is a test"""			)
           self.assertListEqual(__A		,["""▁this""", """▁is""", """▁a""", """▁test"""]			)

           self.assertListEqual(tokenizer.convert_tokens_to_ids(__A			)		,[48, 25, 21, 1289]			)

           SCREAMING_SNAKE_CASE						=		tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
           # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
           self.assertListEqual(
               __A		,["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]			)
           SCREAMING_SNAKE_CASE						=		tokenizer.convert_tokens_to_ids(__A			)
           self.assertListEqual(__A		,[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]			)

           SCREAMING_SNAKE_CASE						=		tokenizer.convert_ids_to_tokens(__A			)
           self.assertListEqual(
               __A		,["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]		,)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ErnieMTokenizer(__A		,unk_token="""<unk>"""		,pad_token="""<pad>"""			)

           SCREAMING_SNAKE_CASE						=		tokenizer.encode("""sequence builders"""			)
           SCREAMING_SNAKE_CASE						=		tokenizer.encode("""multi-sequence build"""			)

           SCREAMING_SNAKE_CASE						=		tokenizer.build_inputs_with_special_tokens(__A			)
           SCREAMING_SNAKE_CASE						=		tokenizer.build_inputs_with_special_tokens(__A		,__A			)

           assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
           assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
               tokenizer.sep_token_id
           ] + text_a + [tokenizer.sep_token_id]






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		{"""input_ids""": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 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, 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], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1]]}
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=__A		,model_name="""susnato/ernie-m-base_pytorch"""		,sequences=[
                   """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
                   """general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
                   """Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained """
                   """models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
                   """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
                   """conditioning on both left and right context in all layers.""",
                   """The quick brown fox jumps over the lazy dog.""",
               ]		,)



'''simple docstring'''

from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""






		def __init__( self		,    __A		,    __A		,    __A		,    __A		,    __A		,    )			->  Any:
								super().__init__()

								self.register_modules(vae=__A		,    text_encoder=__A		,    tokenizer=__A		,    unet=__A		,    scheduler=__A					)






		def 		UpperCamelCase__							( self		,    __A					)			->  List[Any]:
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.encode(tfms.ToTensor()(__A					).unsqueeze(0					).to(self.device					) * 2 - 1					)
														_lowerCAmelCase					    =0.18_215 * latent.latent_dist.sample()
								return latent






		def 		UpperCamelCase__							( self		,    __A					)			->  Dict:
								_lowerCAmelCase					    =(1 / 0.18_215) * latent
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.decode(__A					).sample
								_lowerCAmelCase					    =(img / 2 + 0.5).clamp(0		,    1					)
								_lowerCAmelCase					    =img.detach().cpu().permute(0		,    2		,    3		,    1					).numpy()
								_lowerCAmelCase					    =(img * 255).round().astype('uint8'					)
								return Image.fromarray(img[0]					)






		def 		UpperCamelCase__							( self		,    __A					)			->  str:
								_lowerCAmelCase					    =self.tokenizer(
								    __A		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    truncation=__A		,    return_tensors='pt'		,    )

								_lowerCAmelCase					    =self.text_encoder(text_input.input_ids.to(self.device					)					)[0]

								_lowerCAmelCase					    =self.tokenizer(
								    ''		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    truncation=__A		,    return_tensors='pt'		,    )

								_lowerCAmelCase					    =self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

								return torch.cat([uncond_embedding, text_embedding]					)






		def __call__( self		,    __A		,    __A		,    __A = 0.3		,    __A = 0.6		,    __A = 0.5		,    __A = 42		,    __A = 50		,    __A = 7.5		,    )			->  Image.Image:
								_lowerCAmelCase					    =steps - int(kmin * steps					)
								_lowerCAmelCase					    =steps - int(kmax * steps					)

								_lowerCAmelCase					    =self.prep_text(__A					)

								self.scheduler.set_timesteps(__A					)

								_lowerCAmelCase    ,					_lowerCAmelCase					    =img.size
								_lowerCAmelCase					    =self.encode(__A					)

								torch.manual_seed(__A					)
								_lowerCAmelCase					    =torch.randn(
								    (1, self.unet.config.in_channels, height // 8, width // 8)		,    ).to(self.device					)

								_lowerCAmelCase					    =self.scheduler.add_noise(
								    __A		,    __A		,    timesteps=self.scheduler.timesteps[tmax]		,    )

								_lowerCAmelCase					    =torch.cat([latents] * 2					)

								_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    self.scheduler.timesteps[tmax]					)

								with torch.no_grad():
														_lowerCAmelCase					    =self.unet(
														    __A		,    self.scheduler.timesteps[tmax]		,    encoder_hidden_states=__A		,    ).sample

								_lowerCAmelCase    ,					_lowerCAmelCase					    =pred.chunk(2					)
								_lowerCAmelCase					    =pred_uncond + guidance_scale * (pred_text - pred_uncond)

								_lowerCAmelCase					    =self.scheduler.step(__A		,    self.scheduler.timesteps[tmax]		,    __A					).prev_sample

								for i, t in enumerate(tqdm(self.scheduler.timesteps					)					):
														if i > tmax:
																				if i < tmin:  # layout generation phase
																										_lowerCAmelCase					    =self.scheduler.add_noise(
																										    __A		,    __A		,    timesteps=__A		,    )

																										_lowerCAmelCase					    =(mix_factor * latents) + (
																										    1 - mix_factor
																										) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																										_lowerCAmelCase					    =torch.cat([input] * 2					)

																				else:  # content generation phase
																										_lowerCAmelCase					    =torch.cat([latents] * 2					)

																				_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    __A					)

																				with torch.no_grad():
																										_lowerCAmelCase					    =self.unet(
																										    __A		,    __A		,    encoder_hidden_states=__A		,    ).sample

																				_lowerCAmelCase    ,					_lowerCAmelCase					    =pred.chunk(2					)
																				_lowerCAmelCase					    =pred_uncond + guidance_scale * (pred_text - pred_uncond)

																				_lowerCAmelCase					    =self.scheduler.step(__A		,    __A		,    __A					).prev_sample

								return self.decode(__A					)





