




'''simple docstring'''



import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase_    	=				get_tests_dir("fixtures/test_sentencepiece.model")






@require_sentencepiece
@require_tokenizers
class 							_a				(			SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				Any        =						ReformerTokenizer
       A						:				Any        =						ReformerTokenizerFast
       A						:				Any        =						True
       A						:				Tuple        =						False
       A						:				Optional[Any]        =						True







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().setUp()

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						ReformerTokenizer(A,							keep_accents=A  )
             tokenizer.save_pretrained(self.tmpdirname  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						'<s>'
             SCREAMING_SNAKE_CASE   :      Dict    					=						1

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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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

             self.assertEqual(vocab_keys[0],							'<unk>'  )
             self.assertEqual(vocab_keys[1],							'<s>'  )
             self.assertEqual(vocab_keys[-1],							'j'  )
             self.assertEqual(len(A  ),							1_000  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             self.assertEqual(self.get_tokenizer().vocab_size,							1_000  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             if not self.test_rust_tokenizer:
                   return

             SCREAMING_SNAKE_CASE   :      str    					=						self.get_tokenizer()
             SCREAMING_SNAKE_CASE   :      int    					=						self.get_rust_tokenizer()

             SCREAMING_SNAKE_CASE   :      Tuple    					=						'I was born in 92000, and this is falsé.'

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.tokenize(A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						rust_tokenizer.tokenize(A  )
             self.assertListEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer.encode(A,							add_special_tokens=A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						rust_tokenizer.encode(A,							add_special_tokens=A  )
             self.assertListEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      int    					=						self.get_rust_tokenizer()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.encode(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						rust_tokenizer.encode(A  )
             self.assertListEqual(A,							A  )







       def        UpperCamelCase_							(      self,							A=15  ):







             '''simple docstring'''







             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
                         SCREAMING_SNAKE_CASE   :      str    					=						self.rust_tokenizer_class.from_pretrained(A,							**A  )

                         # Simple input
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						'This is a simple input'
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						['This is a simple input 1', 'This is a simple input 2']
                         SCREAMING_SNAKE_CASE   :      int    					=						('This is a simple input', 'This is a pair')
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						[
                             ('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(A,							tokenizer_r.encode,							A,							max_length=A,							padding='max_length'  )

                         # Simple input
                         self.assertRaises(A,							tokenizer_r.encode_plus,							A,							max_length=A,							padding='max_length'  )

                         # Simple input
                         self.assertRaises(
                             A,							tokenizer_r.batch_encode_plus,							A,							max_length=A,							padding='max_length',							)

                         # Pair input
                         self.assertRaises(A,							tokenizer_r.encode,							A,							max_length=A,							padding='max_length'  )

                         # Pair input
                         self.assertRaises(A,							tokenizer_r.encode_plus,							A,							max_length=A,							padding='max_length'  )

                         # Pair input
                         self.assertRaises(
                             A,							tokenizer_r.batch_encode_plus,							A,							max_length=A,							padding='max_length',							)







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						ReformerTokenizer(A,							keep_accents=A  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer.tokenize('This is a test'  )
             self.assertListEqual(A,							['▁This', '▁is', '▁a', '▁t', 'est']  )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(A  ),							[285, 46, 10, 170, 382],							)

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.tokenize('I was born in 92000, and this is falsé.'  )
             self.assertListEqual(
                 A,							[
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '9',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     'é',
                     '.',
                 ],							)
             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.convert_tokens_to_ids(A  )
             self.assertListEqual(
                 A,							[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],							)

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer.convert_ids_to_tokens(A  )
             self.assertListEqual(
                 A,							[
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '<unk>',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     '<unk>',
                     '.',
                 ],							)







       @cached_property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						'Hello World!'
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[126, 32, 262, 152, 38, 72, 287]

             self.assertListEqual(A,							self.big_tokenizer.encode(A  )  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						(
                 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
                 ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
             )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[
                 108,
                 265,
                 24,
                 111,
                 4,
                 258,
                 156,
                 35,
                 28,
                 275,
                 3,
                 259,
                 297,
                 260,
                 84,
                 4,
                 35,
                 110,
                 44,
                 8,
                 259,
                 91,
                 268,
                 21,
                 11,
                 209,
                 274,
                 109,
                 266,
                 277,
                 117,
                 86,
                 93,
                 315,
                 258,
                 278,
                 258,
                 277,
                 258,
                 0,
                 258,
                 288,
                 258,
                 319,
                 258,
                 0,
                 258,
                 0,
                 258,
                 0,
                 258,
                 0,
                 258,
                 287,
                 258,
                 315,
                 258,
                 289,
                 258,
                 278,
                 99,
                 269,
                 266,
                 262,
                 8,
                 259,
                 241,
                 4,
                 217,
                 230,
                 268,
                 266,
                 55,
                 168,
                 106,
                 75,
                 193,
                 266,
                 223,
                 27,
                 49,
                 26,
                 282,
                 25,
                 264,
                 299,
                 19,
                 26,
                 0,
                 258,
                 277,
                 117,
                 86,
                 93,
                 176,
                 183,
                 270,
                 11,
                 262,
                 42,
                 61,
                 265,
             ]

             self.assertListEqual(A,							self.big_tokenizer.encode(A  )  )







       @require_torch
       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             import torch

             from transformers import ReformerConfig, ReformerModel

             # Build sequence
             SCREAMING_SNAKE_CASE   :      List[str]    					=						list(self.big_tokenizer.get_vocab().keys()  )[:10]
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						' '.join(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						self.big_tokenizer.encode_plus(A,							return_tensors='pt'  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.big_tokenizer.batch_encode_plus([sequence, sequence],							return_tensors='pt'  )

             SCREAMING_SNAKE_CASE   :      str    					=						ReformerConfig()
             # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						encoded_sequence['input_ids'].shape
             SCREAMING_SNAKE_CASE   :      List[str]    					=						ReformerModel(A  )

             # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
             assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

             with torch.no_grad():
                   model(**A  )
                   model(**A  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						{'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], '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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
             # fmt: on

             # This tokenizer does not know some characters like ")".
             # That is the reason why we use very simple texts here.
             # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
             SCREAMING_SNAKE_CASE   :      Dict    					=						[
                 'This is a very simple sentence.',
                 'The quick brown fox jumps over the lazy dog.',
             ]

             self.tokenizer_integration_test_util(
                 expected_encoding=A,							model_name='google/reformer-crime-and-punishment',							revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a',							padding=A,							sequences=A,							)







"""simple docstring"""

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A_						:    List[Any]				=      argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
A_						:    Tuple				=      parser.parse_args()


A_						:    Optional[int]				=      'cpu'
A_						:    Dict				=      'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

A_						:    List[str]				=      'path-to-your-trained-model'
A_						:    Any				=      StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
							A_						:    Tuple				=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A_						:    Tuple				=      pipe.to(device)

# to channels last
A_						:    List[str]				=      pipe.unet.to(memory_format=torch.channels_last)
A_						:    List[str]				=      pipe.vae.to(memory_format=torch.channels_last)
A_						:    Optional[int]				=      pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
							A_						:    Union[str, Any]				=      pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
A_						:    List[Any]				=      torch.randn(2, 4, 64, 64)
A_						:    int				=      torch.rand(1) * 999
A_						:    int				=      torch.randn(2, 77, 768)
A_						:    Tuple				=      (sample, timestep, encoder_hidden_status)
try:
							A_						:    Any				=      ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
							A_						:    List[str]				=      ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A_						:    str				=      ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A_						:    Optional[int]				=      ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
							A_						:    List[str]				=      ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
A_						:    Union[str, Any]				=      666
A_						:    Optional[Any]				=      torch.Generator(device).manual_seed(seed)
A_						:    Union[str, Any]				=      {'generator': generator}
if args.steps is not None:
							A_						:    int				=      args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
							A_						:    int				=      pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')





