

"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class    __a							(UpperCAmelCase__      ,		unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE  :int				=       KandinskyInpaintPipeline
     _SCREAMING_SNAKE_CASE  :Union[str, Any]				=       ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
     _SCREAMING_SNAKE_CASE  :Optional[int]				=       [
         """prompt""",
         """negative_prompt""",
         """image_embeds""",
         """negative_image_embeds""",
         """image""",
         """mask_image""",
     ]
     _SCREAMING_SNAKE_CASE  :Union[str, Any]				=       [
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     _SCREAMING_SNAKE_CASE  :Optional[Any]				=       False

     @property
     def     _a	(  self     )     ->					Union[str, Any]:


           """simple docstring"""


           return 32

     @property
     def     _a	(  self     )     ->					Optional[Any]:


           """simple docstring"""


           return 32

     @property
     def     _a	(  self     )     ->					str:


           """simple docstring"""


           return self.time_input_dim

     @property
     def     _a	(  self     )     ->					Union[str, Any]:


           """simple docstring"""


           return self.time_input_dim * 4

     @property
     def     _a	(  self     )     ->					Tuple:


           """simple docstring"""


           return 100

     @property
     def     _a	(  self     )     ->					List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					str											=		XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""     )
           return tokenizer

     @property
     def     _a	(  self     )     ->					Any:


           """simple docstring"""


           torch.manual_seed(0     )
           SCREAMING_SNAKE_CASE__      :					str											=		MCLIPConfig(
               numDims=self.cross_attention_dim		,			transformerDimensions=self.text_embedder_hidden_size		,			hidden_size=self.text_embedder_hidden_size		,			intermediate_size=37		,			num_attention_heads=4		,			num_hidden_layers=5		,			vocab_size=1_005		,			)

           SCREAMING_SNAKE_CASE__      :					Any											=		MultilingualCLIP(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		text_encoder.eval()

           return text_encoder

     @property
     def     _a	(  self     )     ->					str:


           """simple docstring"""


           torch.manual_seed(0     )

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		{
               '''in_channels''': 9,
               # Out channels is double in channels because predicts mean and variance
               '''out_channels''': 8,
               '''addition_embed_type''': '''text_image''',
               '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
               '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
               '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
               '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
               '''layers_per_block''': 1,
               '''encoder_hid_dim''': self.text_embedder_hidden_size,
               '''encoder_hid_dim_type''': '''text_image_proj''',
               '''cross_attention_dim''': self.cross_attention_dim,
               '''attention_head_dim''': 4,
               '''resnet_time_scale_shift''': '''scale_shift''',
               '''class_embed_type''': None,
           }

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		UNetaDConditionModel(**lowercase_     )
           return model

     @property
     def     _a	(  self     )     ->					List[str]:


           """simple docstring"""


           return {
               "block_out_channels": [32, 64],
               "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 12,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }

     @property
     def     _a	(  self     )     ->					List[Any]:


           """simple docstring"""


           torch.manual_seed(0     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		VQModel(**self.dummy_movq_kwargs     )
           return model

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					List[Any]											=		self.dummy_text_encoder
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		self.dummy_tokenizer
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		self.dummy_unet
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		self.dummy_movq

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		DDIMScheduler(
               num_train_timesteps=1_000		,			beta_schedule="""linear"""		,			beta_start=0.00_085		,			beta_end=0.012		,			clip_sample=lowercase_		,			set_alpha_to_one=lowercase_		,			steps_offset=1		,			prediction_type="""epsilon"""		,			thresholding=lowercase_		,			)

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		{
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''movq''': movq,
           }

           return components

     def     _a	(  self		,			_a		,			_a=0     )     ->					List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(lowercase_     )     ).to(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					int											=		floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(seed + 1     )     ).to(lowercase_     )
           # create init_image
           SCREAMING_SNAKE_CASE__      :					Tuple											=		floats_tensor((1, 3, 64, 64)		,			rng=random.Random(lowercase_     )     ).to(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		image.cpu().permute(0		,			2		,			3		,			1     )[0]
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		Image.fromarray(np.uinta(lowercase_     )     ).convert("""RGB"""     ).resize((256, 256)     )
           # create mask
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		np.ones((64, 64)		,			dtype=np.floataa     )
           SCREAMING_SNAKE_CASE__      :					Any											=		0

           if str(lowercase_     ).startswith("""mps"""     ):
                 SCREAMING_SNAKE_CASE__      :					Optional[int]											=		torch.manual_seed(lowercase_     )
           else:
                 SCREAMING_SNAKE_CASE__      :					int											=		torch.Generator(device=lowercase_     ).manual_seed(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Dict											=		{
               '''prompt''': '''horse''',
               '''image''': init_image,
               '''mask_image''': mask,
               '''image_embeds''': image_embeds,
               '''negative_image_embeds''': negative_image_embeds,
               '''generator''': generator,
               '''height''': 64,
               '''width''': 64,
               '''num_inference_steps''': 2,
               '''guidance_scale''': 4.0,
               '''output_type''': '''np''',
           }
           return inputs

     def     _a	(  self     )     ->					str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		'''cpu'''

           SCREAMING_SNAKE_CASE__      :					List[str]											=		self.get_dummy_components()

           SCREAMING_SNAKE_CASE__      :					int											=		self.pipeline_class(**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					str											=		pipe.to(lowercase_     )

           pipe.set_progress_bar_config(disable=lowercase_     )

           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		pipe(**self.get_dummy_inputs(lowercase_     )     )
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		output.images

           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		pipe(
               **self.get_dummy_inputs(lowercase_     )		,			return_dict=lowercase_		,			)[0]

           SCREAMING_SNAKE_CASE__      :					int											=		image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE__      :					str											=		image_from_tuple[0, -3:, -3:, -1]

           print(f'''image.shape {image.shape}'''     )

           assert image.shape == (1, 64, 64, 3)

           SCREAMING_SNAKE_CASE__      :					str											=		np.array(
               [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]     )

           assert (
               np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
           ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
           assert (
               np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2
           ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





     def     _a	(  self     )     ->					str:


           """simple docstring"""


           super().test_inference_batch_single_identical(expected_max_diff=3E-3     )





@slow
@require_torch_gpu
class    __a							(unittest.TestCase):






     '''simple docstring'''

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


           """simple docstring"""


           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()





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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					int											=		load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""     )

           SCREAMING_SNAKE_CASE__      :					str											=		load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""     )
           SCREAMING_SNAKE_CASE__      :					Tuple											=		np.ones((768, 768)		,			dtype=np.floataa     )
           SCREAMING_SNAKE_CASE__      :					str											=		0

           SCREAMING_SNAKE_CASE__      :					Tuple											=		'''a hat'''

           SCREAMING_SNAKE_CASE__      :					List[str]											=		KandinskyPriorPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-1-prior"""		,			torch_dtype=torch.floataa     )
           pipe_prior.to(lowercase_     )

           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		KandinskyInpaintPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-1-inpaint"""		,			torch_dtype=torch.floataa     )
           SCREAMING_SNAKE_CASE__      :					List[str]											=		pipeline.to(lowercase_     )
           pipeline.set_progress_bar_config(disable=lowercase_     )

           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		torch.Generator(device="""cpu"""     ).manual_seed(0     )
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		pipe_prior(
               lowercase_		,			generator=lowercase_		,			num_inference_steps=5		,			negative_prompt=""""""		,			).to_tuple()

           SCREAMING_SNAKE_CASE__      :					str											=		pipeline(
               lowercase_		,			image=lowercase_		,			mask_image=lowercase_		,			image_embeds=lowercase_		,			negative_image_embeds=lowercase_		,			generator=lowercase_		,			num_inference_steps=100		,			height=768		,			width=768		,			output_type="""np"""		,			)

           SCREAMING_SNAKE_CASE__      :					Tuple											=		output.images[0]

           assert image.shape == (768, 768, 3)

           assert_mean_pixel_difference(lowercase_		,			lowercase_     )












"""simple docstring"""






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


UpperCAmelCase_   : Optional[int]   			= logging.get_logger(__name__)

UpperCAmelCase_   : Dict   			= {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_   : Dict   			= {
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase_   : List[str]   			= {
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  VOCAB_FILES_NAMES
      __UpperCamelCase	 =  PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase	 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase	 =  ["input_ids", "attention_mask"]
      __UpperCamelCase	 =  GPTaTokenizer
      def __init__(						self     :  Optional[int]						,	lowercase_     :  int=None						,	lowercase_     :  List[str]=None						,	lowercase_     :  Union[str, Any]=None						,	lowercase_     :  Tuple="<|endoftext|>"						,	lowercase_     :  str="<|endoftext|>"						,	lowercase_     :  Dict="<|endoftext|>"						,	lowercase_     :  Tuple=False						,	**lowercase_     :  Optional[int]						,	):




          '''simple docstring'''


          super().__init__(
              lowercase_						,	lowercase_						,	tokenizer_file=lowercase_						,	unk_token=lowercase_						,	bos_token=lowercase_						,	eos_token=lowercase_						,	add_prefix_space=lowercase_						,	**lowercase_						,	)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		kwargs.pop('''add_bos_token'''						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
          if pre_tok_state.get('''add_prefix_space'''						,	lowercase_) != add_prefix_space:
              SCREAMING_SNAKE_CASE_      :  int    						=		getattr(lowercase_						,	pre_tok_state.pop('''type'''))
              SCREAMING_SNAKE_CASE_      :  str    						=		add_prefix_space
              SCREAMING_SNAKE_CASE_      :  Dict    						=		pre_tok_class(**lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		add_prefix_space
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	*lowercase_     :  List[Any]						,	**lowercase_     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		kwargs.get('''is_split_into_words'''						,	lowercase_)
          assert self.add_prefix_space or not is_split_into_words, (
              F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	*lowercase_     :  List[str]						,	**lowercase_     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		kwargs.get('''is_split_into_words'''						,	lowercase_)

          assert self.add_prefix_space or not is_split_into_words, (
              F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  str						,	lowercase_     :  Optional[str] = None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self._tokenizer.model.save(lowercase_						,	name=lowercase_)
          return tuple(lowercase_)



      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  "Conversation"):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		[]
          for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase_						,	add_special_tokens=lowercase_) + [self.eos_token_id])

          if len(lowercase_) > self.model_max_length:
              SCREAMING_SNAKE_CASE_      :  Any    						=		input_ids[-self.model_max_length :]
          return input_ids


