'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   KandinskyVaaControlnetPipeline
       SCREAMING_SNAKE_CASE 		=   ["image_embeds", "negative_image_embeds", "hint"]
       SCREAMING_SNAKE_CASE 		=   ["image_embeds", "negative_image_embeds", "hint"]
       SCREAMING_SNAKE_CASE 		=   [
           "generator",
           "height",
           "width",
           "latents",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       SCREAMING_SNAKE_CASE 		=   False

       @property
       def _lowerCAmelCase(						self			)  ->  List[str]:
              return 32

       @property
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
              return 32

       @property
       def _lowerCAmelCase(						self			)  ->  str:
              return self.time_input_dim

       @property
       def _lowerCAmelCase(						self			)  ->  Any:
              return self.time_input_dim * 4

       @property
       def _lowerCAmelCase(						self			)  ->  int:
              return 100

       @property
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
              torch.manual_seed(0			)

              lowercase__    :   Tuple						= {
                  '''in_channels''': 8,
                  # Out channels is double in channels because predicts mean and variance
                  '''out_channels''': 8,
                  '''addition_embed_type''': '''image_hint''',
                  '''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''': '''image_proj''',
                  '''cross_attention_dim''': self.cross_attention_dim,
                  '''attention_head_dim''': 4,
                  '''resnet_time_scale_shift''': '''scale_shift''',
                  '''class_embed_type''': None,
              }

              lowercase__    :   Optional[Any]						= UNetaDConditionModel(**__lowerCAmelCase			)
              return model

       @property
       def _lowerCAmelCase(						self			)  ->  int:
              return {
                  "block_out_channels": [32, 32, 64, 64],
                  "down_block_types": [
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                      "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
                  "vq_embed_dim": 4,
              }

       @property
       def _lowerCAmelCase(						self			)  ->  List[Any]:
              torch.manual_seed(0			)
              lowercase__    :   Any						= VQModel(**self.dummy_movq_kwargs			)
              return model

       def _lowerCAmelCase(						self			)  ->  Dict:
              lowercase__    :   Dict						= self.dummy_unet
              lowercase__    :   List[str]						= self.dummy_movq

              lowercase__    :   Optional[Any]						= DDIMScheduler(
                  num_train_timesteps=1000     ,       beta_schedule='''linear'''     ,       beta_start=0.0_0_0_8_5     ,       beta_end=0.0_1_2     ,       clip_sample=__lowerCAmelCase     ,       set_alpha_to_one=__lowerCAmelCase     ,       steps_offset=1     ,       prediction_type='''epsilon'''     ,       thresholding=__lowerCAmelCase     ,       )

              lowercase__    :   Tuple						= {
                  '''unet''': unet,
                  '''scheduler''': scheduler,
                  '''movq''': movq,
              }
              return components

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=0			)  ->  Any:
              lowercase__    :   Union[str, Any]						= floats_tensor((1, self.text_embedder_hidden_size)     ,       rng=random.Random(__lowerCAmelCase			)			).to(__lowerCAmelCase			)
              lowercase__    :   Dict						= floats_tensor((1, self.text_embedder_hidden_size)     ,       rng=random.Random(seed + 1			)			).to(
                  __lowerCAmelCase			)

              # create hint
              lowercase__    :   Any						= floats_tensor((1, 3, 64, 64)     ,       rng=random.Random(__lowerCAmelCase			)			).to(__lowerCAmelCase			)

              if str(__lowerCAmelCase			).startswith('''mps'''			):
                     lowercase__    :   str						= torch.manual_seed(__lowerCAmelCase			)
              else:
                     lowercase__    :   List[Any]						= torch.Generator(device=__lowerCAmelCase			).manual_seed(__lowerCAmelCase			)
              lowercase__    :   Tuple						= {
                  '''image_embeds''': image_embeds,
                  '''negative_image_embeds''': negative_image_embeds,
                  '''hint''': hint,
                  '''generator''': generator,
                  '''height''': 64,
                  '''width''': 64,
                  '''guidance_scale''': 4.0,
                  '''num_inference_steps''': 2,
                  '''output_type''': '''np''',
              }
              return inputs


       def _lowerCAmelCase(						self			)  ->  List[str]:
              lowercase__    :   List[str]						= '''cpu'''

              lowercase__    :   Tuple						= self.get_dummy_components()

              lowercase__    :   str						= self.pipeline_class(**__lowerCAmelCase			)
              lowercase__    :   Optional[Any]						= pipe.to(__lowerCAmelCase			)

              pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

              lowercase__    :   Dict						= pipe(**self.get_dummy_inputs(__lowerCAmelCase			)			)
              lowercase__    :   List[str]						= output.images

              lowercase__    :   List[str]						= pipe(
                  **self.get_dummy_inputs(__lowerCAmelCase			)     ,       return_dict=__lowerCAmelCase     ,       )[0]

              lowercase__    :   Dict						= image[0, -3:, -3:, -1]
              lowercase__    :   Tuple						= image_from_tuple[0, -3:, -3:, -1]

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

              lowercase__    :   Tuple						= np.array(
                  [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]			)

              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()}"""



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





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  Tuple:
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()


       def _lowerCAmelCase(						self			)  ->  Optional[int]:
              lowercase__    :   List[str]						= load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''			)

              lowercase__    :   Dict						= load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/kandinskyv22/hint_image_cat.png'''			)
              lowercase__    :   Any						= torch.from_numpy(np.array(__lowerCAmelCase			)			).float() / 255.0
              lowercase__    :   List[str]						= hint.permute(2     ,       0     ,       1			).unsqueeze(0			)

              lowercase__    :   Dict						= KandinskyVaaPriorPipeline.from_pretrained(
                  '''kandinsky-community/kandinsky-2-2-prior'''     ,       torch_dtype=torch.floataa			)
              pipe_prior.to(__lowerCAmelCase			)

              lowercase__    :   Optional[int]						= KandinskyVaaControlnetPipeline.from_pretrained(
                  '''kandinsky-community/kandinsky-2-2-controlnet-depth'''     ,       torch_dtype=torch.floataa			)
              lowercase__    :   Optional[int]						= pipeline.to(__lowerCAmelCase			)
              pipeline.set_progress_bar_config(disable=__lowerCAmelCase			)

              lowercase__    :   Union[str, Any]						= '''A robot, 4k photo'''

              lowercase__    :   List[Any]						= torch.Generator(device='''cuda'''			).manual_seed(0			)
              lowercase__    :   List[str]						= pipe_prior(
                  __lowerCAmelCase     ,       generator=__lowerCAmelCase     ,       num_inference_steps=5     ,       negative_prompt=''''''     ,       ).to_tuple()

              lowercase__    :   Union[str, Any]						= torch.Generator(device='''cuda'''			).manual_seed(0			)
              lowercase__    :   Union[str, Any]						= pipeline(
                  image_embeds=__lowerCAmelCase     ,       negative_image_embeds=__lowerCAmelCase     ,       hint=__lowerCAmelCase     ,       generator=__lowerCAmelCase     ,       num_inference_steps=100     ,       output_type='''np'''     ,       )

              lowercase__    :   Tuple						= output.images[0]

              assert image.shape == (512, 512, 3)

              assert_mean_pixel_difference(__lowerCAmelCase     ,       __lowerCAmelCase			)


'''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


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


__a:			Union[str, Any]	       =       logging.get_logger(__name__)

__a:			Tuple	       =       {"""tokenizer_file""": """tokenizer.json"""}

__a:			Union[str, Any]	       =       {
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE 		=   ["input_ids", "attention_mask"]
       SCREAMING_SNAKE_CASE 		=   None

       def __init__(						self     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase="<unk>"     ,       __lowerCAmelCase="<s>"     ,       __lowerCAmelCase="</s>"     ,       __lowerCAmelCase="<pad>"     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=False     ,       **__lowerCAmelCase     ,       )  ->  Union[str, Any]:
        super().__init__(
            __lowerCAmelCase     ,       __lowerCAmelCase     ,       tokenizer_file=__lowerCAmelCase     ,       unk_token=__lowerCAmelCase     ,       bos_token=__lowerCAmelCase     ,       eos_token=__lowerCAmelCase     ,       pad_token=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase     ,       clean_up_tokenization_spaces=__lowerCAmelCase     ,       **__lowerCAmelCase     ,       )
        lowercase__    :   int						= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
        if pre_tok_state.get('''add_prefix_space'''     ,       __lowerCAmelCase			) != add_prefix_space:
         lowercase__    :   int						= getattr(__lowerCAmelCase     ,       pre_tok_state.pop('''type'''			)			)
         lowercase__    :   Tuple						= add_prefix_space
         lowercase__    :   List[str]						= pre_tok_class(**__lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= add_prefix_space

       def _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  BatchEncoding:
        lowercase__    :   Dict						= kwargs.get('''is_split_into_words'''     ,       __lowerCAmelCase			)
        if not (self.add_prefix_space or not is_split_into_words):
         raise Exception(
             F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
             ''' pretokenized inputs.'''			)

        return super()._batch_encode_plus(*__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  BatchEncoding:
        lowercase__    :   str						= kwargs.get('''is_split_into_words'''     ,       __lowerCAmelCase			)

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

        return super()._encode_plus(*__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Tuple[str]:
        lowercase__    :   List[Any]						= self._tokenizer.model.save(__lowerCAmelCase     ,       name=__lowerCAmelCase			)
        return tuple(__lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[int]:
        lowercase__    :   Dict						= []
        for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase			) + [self.eos_token_id]			)

        if len(__lowerCAmelCase			) > self.model_max_length:
         lowercase__    :   Optional[Any]						= input_ids[-self.model_max_length :]
        return input_ids

