





"""simple docstring"""





from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase = None    , ):



         '''simple docstring'''

         super().__init__()
         self.register_modules(transformer=__UpperCAmelCase    , vae=__UpperCAmelCase    , scheduler=__UpperCAmelCase      )

         # create a imagenet -> id dictionary for easier use
         lowerCAmelCase__			:Optional[int]											=							{}
         if idalabel is not None:
           for key, value in idalabel.items():
             for label in value.split(','      ):
               lowerCAmelCase__			:Dict											=							int(__UpperCAmelCase      )
           lowerCAmelCase__			:Any											=							dict(sorted(self.labels.items()      )      )






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         if not isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
           lowerCAmelCase__			:Optional[int]											=							list(__UpperCAmelCase      )

         for l in label:
           if l not in self.labels:
             raise ValueError(
                 F"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."      )

         return [self.labels[l] for l in label]






       @torch.no_grad()
       def __call__(    self    , __UpperCAmelCase    , __UpperCAmelCase = 4.0    , __UpperCAmelCase = None    , __UpperCAmelCase = 5_0    , __UpperCAmelCase = "pil"    , __UpperCAmelCase = True    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							len(__UpperCAmelCase      )
         lowerCAmelCase__			:Tuple											=							self.transformer.config.sample_size
         lowerCAmelCase__			:Union[str, Any]											=							self.transformer.config.in_channels

         lowerCAmelCase__			:Any											=							randn_tensor(
             shape=(batch_size, latent_channels, latent_size, latent_size)    , generator=__UpperCAmelCase    , device=self.device    , dtype=self.transformer.dtype    , )
         lowerCAmelCase__			:Tuple											=							torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

         lowerCAmelCase__			:Tuple											=							torch.tensor(__UpperCAmelCase    , device=self.device      ).reshape(-1      )
         lowerCAmelCase__			:Optional[int]											=							torch.tensor([1_0_0_0] * batch_size    , device=self.device      )
         lowerCAmelCase__			:int											=							torch.cat([class_labels, class_null]    , 0      ) if guidance_scale > 1 else class_labels

         # set step values
         self.scheduler.set_timesteps(__UpperCAmelCase      )

         for t in self.progress_bar(self.scheduler.timesteps      ):
           if guidance_scale > 1:
             lowerCAmelCase__			:Tuple											=							latent_model_input[: len(__UpperCAmelCase      ) // 2]
             lowerCAmelCase__			:List[str]											=							torch.cat([half, half]    , dim=0      )
           lowerCAmelCase__			:List[str]											=							self.scheduler.scale_model_input(__UpperCAmelCase    , __UpperCAmelCase      )

           lowerCAmelCase__			:Tuple											=							t
           if not torch.is_tensor(__UpperCAmelCase      ):
             # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
             # This would be a good case for the `match` statement (Python 3.10+)
             lowerCAmelCase__			:List[str]											=							latent_model_input.device.type == 'mps'
             if isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
               lowerCAmelCase__			:List[str]											=							torch.floataa if is_mps else torch.floataa
             else:
               lowerCAmelCase__			:List[str]											=							torch.intaa if is_mps else torch.intaa
             lowerCAmelCase__			:Optional[Any]											=							torch.tensor([timesteps]    , dtype=__UpperCAmelCase    , device=latent_model_input.device      )
           elif len(timesteps.shape      ) == 0:
             lowerCAmelCase__			:Optional[int]											=							timesteps[None].to(latent_model_input.device      )
           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           lowerCAmelCase__			:Optional[int]											=							timesteps.expand(latent_model_input.shape[0]      )
           # predict noise model_output
           lowerCAmelCase__			:Optional[int]											=							self.transformer(
               __UpperCAmelCase    , timestep=__UpperCAmelCase    , class_labels=__UpperCAmelCase      ).sample

           # perform guidance
           if guidance_scale > 1:
             lowerCAmelCase__							,				lowerCAmelCase__			:Any											=							noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             lowerCAmelCase__							,				lowerCAmelCase__			:Union[str, Any]											=							torch.split(__UpperCAmelCase    , len(__UpperCAmelCase      ) // 2    , dim=0      )

             lowerCAmelCase__			:Any											=							uncond_eps + guidance_scale * (cond_eps - uncond_eps)
             lowerCAmelCase__			:int											=							torch.cat([half_eps, half_eps]    , dim=0      )

             lowerCAmelCase__			:Union[str, Any]											=							torch.cat([eps, rest]    , dim=1      )

           # learned sigma
           if self.transformer.config.out_channels // 2 == latent_channels:
             lowerCAmelCase__							,				lowerCAmelCase__			:List[str]											=							torch.split(__UpperCAmelCase    , __UpperCAmelCase    , dim=1      )
           else:
             lowerCAmelCase__			:str											=							noise_pred

           # compute previous image: x_t -> x_t-1
           lowerCAmelCase__			:Dict											=							self.scheduler.step(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ).prev_sample

         if guidance_scale > 1:
           lowerCAmelCase__							,				lowerCAmelCase__			:str											=							latent_model_input.chunk(2    , dim=0      )
         else:
           lowerCAmelCase__			:Optional[int]											=							latent_model_input

         lowerCAmelCase__			:Tuple											=							1 / self.vae.config.scaling_factor * latents
         lowerCAmelCase__			:Dict											=							self.vae.decode(__UpperCAmelCase      ).sample

         lowerCAmelCase__			:Union[str, Any]											=							(samples / 2 + 0.5).clamp(0    , 1      )

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         lowerCAmelCase__			:List[str]											=							samples.cpu().permute(0    , 2    , 3    , 1      ).float().numpy()

         if output_type == "pil":
           lowerCAmelCase__			:int											=							self.numpy_to_pil(__UpperCAmelCase      )

         if not return_dict:
           return (samples,)

         return ImagePipelineOutput(images=__UpperCAmelCase      )







"""simple docstring"""





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


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






@require_sentencepiece
@require_tokenizers
class 					_lowerCAmelCase   (     a  ,   unittest.TestCase       ):
       """simple docstring"""




       __magic_name__     :List[str]		  =					XGLMTokenizer
       __magic_name__     :Any		  =					XGLMTokenizerFast
       __magic_name__     :Dict		  =					True
       __magic_name__     :Union[str, Any]		  =					True






       def      snake_case						(    self      ):



         '''simple docstring'''

         super().setUp()

         # We have a SentencePiece fixture for testing
         lowerCAmelCase__			:int											=							XGLMTokenizer(__UpperCAmelCase    , keep_accents=__UpperCAmelCase      )
         tokenizer.save_pretrained(self.tmpdirname      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							'<pad>'
         lowerCAmelCase__			:int											=							1

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							list(self.get_tokenizer().get_vocab().keys()      )

         self.assertEqual(vocab_keys[0]    , '<s>'      )
         self.assertEqual(vocab_keys[1]    , '<pad>'      )
         self.assertEqual(len(__UpperCAmelCase      )    , 1_0_0_8      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         self.assertEqual(self.get_tokenizer().vocab_size    , 1_0_0_8      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							XGLMTokenizer(__UpperCAmelCase    , keep_accents=__UpperCAmelCase      )

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

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__UpperCAmelCase      )    , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]    , )

         lowerCAmelCase__			:int											=							tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
         self.assertListEqual(
             __UpperCAmelCase    , [
                 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',
                 '.',
             ]    , )
         lowerCAmelCase__			:Tuple											=							tokenizer.convert_tokens_to_ids(__UpperCAmelCase      )
         self.assertListEqual(
             __UpperCAmelCase    , [
                 value + tokenizer.fairseq_offset
                 for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
             ]    , )

         lowerCAmelCase__			:Optional[int]											=							tokenizer.convert_ids_to_tokens(__UpperCAmelCase      )
         self.assertListEqual(
             __UpperCAmelCase    , [
                 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      snake_case						(    self      ):



         '''simple docstring'''

         return XGLMTokenizer.from_pretrained('facebook/xglm-564M'      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         with tempfile.NamedTemporaryFile() as f:
           shutil.copyfile(__UpperCAmelCase    , f.name      )
           lowerCAmelCase__			:Dict											=							XGLMTokenizer(f.name    , keep_accents=__UpperCAmelCase      )
           lowerCAmelCase__			:List[Any]											=							pickle.dumps(__UpperCAmelCase      )
         pickle.loads(__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         if not self.test_rust_tokenizer:
           return

         lowerCAmelCase__			:Optional[Any]											=							self.get_tokenizer()
         lowerCAmelCase__			:List[str]											=							self.get_rust_tokenizer()

         lowerCAmelCase__			:Optional[Any]											=							'I was born in 92000, and this is falsé.'

         lowerCAmelCase__			:Dict											=							tokenizer.tokenize(__UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							rust_tokenizer.tokenize(__UpperCAmelCase      )
         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:Optional[Any]											=							tokenizer.encode(__UpperCAmelCase    , add_special_tokens=__UpperCAmelCase      )
         lowerCAmelCase__			:List[Any]											=							rust_tokenizer.encode(__UpperCAmelCase    , add_special_tokens=__UpperCAmelCase      )
         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:int											=							self.get_rust_tokenizer()
         lowerCAmelCase__			:Dict											=							tokenizer.encode(__UpperCAmelCase      )
         lowerCAmelCase__			:Tuple											=							rust_tokenizer.encode(__UpperCAmelCase      )
         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       @slow
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							'Hello World!'
         lowerCAmelCase__			:Tuple											=							[2, 3_1_2_2_7, 4_4_4_7, 3_5]

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






       @slow
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							(
             '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'
         )
         # fmt: off
         lowerCAmelCase__			:List[str]											=							[2, 1_0_1_8, 6_7, 1_1, 1_9_8_8, 2_6_1_7, 5_6_3_1, 2_7_8, 1_1, 3_4_0_7, 4_8, 7_1_6_3_0, 2_8_0_8_5, 4, 3_2_3_4, 1_5_7, 1_3, 6, 5, 6, 4, 3_5_2_6, 7_6_8, 1_5, 6_5_9, 5_7, 2_9_8, 3_9_8_3, 8_6_4, 1_2_9, 2_1, 6, 5, 1_3_6_7_5, 3_7_7, 6_5_2, 7_5_8_0, 1_0_3_4_1, 1_5_5, 2_8_1_7, 4_2_2, 1_6_6_6, 7, 1_6_7_4, 5_3, 1_1_3, 2_0_2_2_7_7, 1_7_8_9_2, 3_3, 6_0, 8_7, 4, 3_2_3_4, 1_5_7, 6_1, 2_6_6_7, 5_2_3_7_6, 1_9, 8_8, 2_3, 7_3_5]
         # fmt: on

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






       @slow
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							{
             'input_ids': [[2, 1_0_8_8_2_5, 1_1_6_3, 1_5, 8_8_0_1_0, 4_7_3, 1_5_8_9_8, 1_5_7, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 2_3_8_0_2_1, 1_1_6_3, 5_3, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 5_3_2_8_3, 1_8_2_3_9_6, 8, 1_8_5_6_6, 1_6, 3_6_7_3_3, 4_1_0_1, 8, 2_3_0, 2_4_4_0_1_7, 1_2_2_5_5_3, 7, 1_5, 1_3_2_5_9_7, 4, 2_9_3, 1_2_5_1_1, 7_6_1_0, 4, 3_4_1_4, 1_3_2_5_9_7, 9, 4, 3_2_3_6_1, 3_6_2, 4, 7_3_4, 2_8_5_1_2, 3_2_5_6_9, 1_8, 4, 3_2_3_6_1, 2_6_0_9_6, 1_4_9_8_2, 7_3, 1_8_7_1_5, 2_1_4_3_3, 2_3_5_2_6_1, 1_5, 4_9_2, 1_2_4_2_7, 1_6, 5_3, 1_8_7_1_5, 2_1_4_3_3, 6_5_4_5_4, 1_5, 2_3_6_5_9, 5_6_3, 1_6, 2_7_8, 5_9_7, 2_8_4_3, 5_9_5, 7_9_3_1, 1_8_2_3_9_6, 6_4_1_8_6, 2_2, 8_8_6, 5_9_5, 1_3_2_9_8_1, 5_3, 2_5_5_4_0, 3_4_4_9, 4_3_9_8_2, 3_9_9_0_1, 5_9_5_1, 8_7_8, 3_3_0, 4, 2_7_6_9_4, 8_0_2_6_9, 3_1_2, 5_3, 6_5_1_7, 1_1_7_8_0, 6_1_1, 2_0_4_0_8, 5], [2, 6, 1_3_2_5_9_7, 6_7, 4_2_8_9_7, 3_3, 5_9_2, 8, 1_6_3_7_2_9, 2_5_5_4_0, 3_6_1, 1_3_6_9_9_7, 1_0_9_5_1_4, 1_7_3_2_3_0, 7, 5_0_1, 6_0, 1_0_2_9_1_3, 1_9_6, 5_6_3_1, 2_3_5, 6_3_2_4_3, 4_7_3, 6, 2_3_1_7_5_7, 7_4, 5_2_7_7, 7_9_0_5, 5_3, 3_0_9_5, 3_7_3_1_7, 2_2, 4_5_4, 1_8_3_8_7_4, 5], [2, 2_6_8, 3_1_2_9_8, 4_6_5_3_0, 6, 1_3_2_9_3_5, 4_3_8_3_1, 7, 5_9_7, 3_2, 2_4, 3_6_8_8, 9_8_6_5, 5]],
             '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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
         }  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=__UpperCAmelCase    , model_name='facebook/xglm-564M'    , padding=__UpperCAmelCase    , )
