





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


snake_case_           =      False

class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				Tuple				):





         """simple docstring"""



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


      @property
      def        a			(self	:				str				):





         """simple docstring"""



         return 12


      @property
      def        a			(self	:				str				):





         """simple docstring"""



         return 12


      @property
      def        a			(self	:				List[Any]				):





         """simple docstring"""



         return 32


      @property
      def        a			(self	:				List[str]				):





         """simple docstring"""



         torch.manual_seed(0				)
         __snake_case								=       VQModel(
             block_out_channels=[32, 64]    ,      in_channels=3    ,      out_channels=3    ,      down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,      latent_channels=3    ,      num_vq_embeddings=self.num_embed    ,      vq_embed_dim=3    ,      )
         return model


      @property
      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
         return tokenizer


      @property
      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         torch.manual_seed(0				)
         __snake_case								=       CLIPTextConfig(
             bos_token_id=0    ,      eos_token_id=2    ,      hidden_size=self.text_embedder_hidden_size    ,      intermediate_size=37    ,      layer_norm_eps=1E-05    ,      num_attention_heads=4    ,      num_hidden_layers=5    ,      pad_token_id=1    ,      vocab_size=1000    ,      )
         return CLIPTextModel(__SCREAMING_SNAKE_CASE				)


      @property
      def        a			(self	:				List[str]				):





         """simple docstring"""



         torch.manual_seed(0				)

         __snake_case								=       12
         __snake_case								=       12

         __snake_case								=       {
             '''attention_bias''': True,
             '''cross_attention_dim''': 32,
             '''attention_head_dim''': height * width,
             '''num_attention_heads''': 1,
             '''num_vector_embeds''': self.num_embed,
             '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
             '''norm_num_groups''': 32,
             '''sample_size''': width,
             '''activation_fn''': '''geglu-approximate''',
         }

         __snake_case								=       TransformeraDModel(**__SCREAMING_SNAKE_CASE				)
         return model


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       '''cpu'''

         __snake_case								=       self.dummy_vqvae
         __snake_case								=       self.dummy_text_encoder
         __snake_case								=       self.dummy_tokenizer
         __snake_case								=       self.dummy_transformer
         __snake_case								=       VQDiffusionScheduler(self.num_embed				)
         __snake_case								=       LearnedClassifierFreeSamplingEmbeddings(learnable=__SCREAMING_SNAKE_CASE				)

         __snake_case								=       VQDiffusionPipeline(
             vqvae=__SCREAMING_SNAKE_CASE    ,      text_encoder=__SCREAMING_SNAKE_CASE    ,      tokenizer=__SCREAMING_SNAKE_CASE    ,      transformer=__SCREAMING_SNAKE_CASE    ,      scheduler=__SCREAMING_SNAKE_CASE    ,      learned_classifier_free_sampling_embeddings=__SCREAMING_SNAKE_CASE    ,      )
         __snake_case								=       pipe.to(__SCREAMING_SNAKE_CASE				)
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE				)

         __snake_case								=       '''teddy bear playing in the pool'''

         __snake_case								=       torch.Generator(device=__SCREAMING_SNAKE_CASE				).manual_seed(0				)
         __snake_case								=       pipe([prompt]    ,      generator=__SCREAMING_SNAKE_CASE    ,      num_inference_steps=2    ,      output_type='''np'''				)
         __snake_case								=       output.images

         __snake_case								=       torch.Generator(device=__SCREAMING_SNAKE_CASE				).manual_seed(0				)
         __snake_case								=       pipe(
             [prompt]    ,      generator=__SCREAMING_SNAKE_CASE    ,      output_type='''np'''    ,      return_dict=__SCREAMING_SNAKE_CASE    ,      num_inference_steps=2				)[0]

         __snake_case								=       image[0, -3:, -3:, -1]
         __snake_case								=       image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         __snake_case								=       np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       '''cpu'''

         __snake_case								=       self.dummy_vqvae
         __snake_case								=       self.dummy_text_encoder
         __snake_case								=       self.dummy_tokenizer
         __snake_case								=       self.dummy_transformer
         __snake_case								=       VQDiffusionScheduler(self.num_embed				)
         __snake_case								=       LearnedClassifierFreeSamplingEmbeddings(
             learnable=__SCREAMING_SNAKE_CASE    ,      hidden_size=self.text_embedder_hidden_size    ,      length=tokenizer.model_max_length				)

         __snake_case								=       VQDiffusionPipeline(
             vqvae=__SCREAMING_SNAKE_CASE    ,      text_encoder=__SCREAMING_SNAKE_CASE    ,      tokenizer=__SCREAMING_SNAKE_CASE    ,      transformer=__SCREAMING_SNAKE_CASE    ,      scheduler=__SCREAMING_SNAKE_CASE    ,      learned_classifier_free_sampling_embeddings=__SCREAMING_SNAKE_CASE    ,      )
         __snake_case								=       pipe.to(__SCREAMING_SNAKE_CASE				)
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE				)

         __snake_case								=       '''teddy bear playing in the pool'''

         __snake_case								=       torch.Generator(device=__SCREAMING_SNAKE_CASE				).manual_seed(0				)
         __snake_case								=       pipe([prompt]    ,      generator=__SCREAMING_SNAKE_CASE    ,      num_inference_steps=2    ,      output_type='''np'''				)
         __snake_case								=       output.images

         __snake_case								=       torch.Generator(device=__SCREAMING_SNAKE_CASE				).manual_seed(0				)
         __snake_case								=       pipe(
             [prompt]    ,      generator=__SCREAMING_SNAKE_CASE    ,      output_type='''np'''    ,      return_dict=__SCREAMING_SNAKE_CASE    ,      num_inference_steps=2				)[0]

         __snake_case								=       image[0, -3:, -3:, -1]
         __snake_case								=       image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         __snake_case								=       np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 2.0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2






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


      def        a			(self	:				str				):





         """simple docstring"""



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


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''				)

         __snake_case								=       VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''				)
         __snake_case								=       pipeline.to(__SCREAMING_SNAKE_CASE				)
         pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE				)

         # requires GPU generator for gumbel softmax
         # don't use GPU generator in tests though
         __snake_case								=       torch.Generator(device=__SCREAMING_SNAKE_CASE				).manual_seed(0				)
         __snake_case								=       pipeline(
             '''teddy bear playing in the pool'''    ,      num_images_per_prompt=1    ,      generator=__SCREAMING_SNAKE_CASE    ,      output_type='''np'''    ,      )

         __snake_case								=       output.images[0]

         assert image.shape == (256, 256, 3)
         assert np.abs(expected_image - image				).max() < 2.0










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


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {'tokenizer_file': 'tokenizer.json'}

snake_case_           =      {
    '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 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    List[str]							=   VOCAB_FILES_NAMES
      A_						:    Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
      A_						:    str							=   ['input_ids', 'attention_mask']
      A_						:    Optional[Any]							=   None


      def __init__(self	:				Optional[int]    ,      a__	:				int=None    ,      a__	:				str=None    ,      a__	:				Any=None    ,      a__	:				List[Any]="<unk>"    ,      a__	:				List[Any]="<s>"    ,      a__	:				Optional[int]="</s>"    ,      a__	:				List[str]="<pad>"    ,      a__	:				Union[str, Any]=False    ,      a__	:				str=False    ,      **a__	:				Optional[Any]    ,      ):





         """simple docstring"""



         super().__init__(
             a__    ,      a__    ,      tokenizer_file=a__    ,      unk_token=a__    ,      bos_token=a__    ,      eos_token=a__    ,      pad_token=a__    ,      add_prefix_space=a__    ,      clean_up_tokenization_spaces=a__    ,      **a__    ,      )
         __snake_case								=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
         if pre_tok_state.get('''add_prefix_space'''    ,      a__				) != add_prefix_space:
            __snake_case								=       getattr(a__    ,      pre_tok_state.pop('''type'''				)				)
            __snake_case								=       add_prefix_space
            __snake_case								=       pre_tok_class(**a__				)

         __snake_case								=       add_prefix_space


      def        a			(self	:				int    ,      *a__	:				Tuple    ,      **a__	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       kwargs.get('''is_split_into_words'''    ,      a__				)
         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(*a__    ,      **a__				)


      def        a			(self	:				List[str]    ,      *a__	:				List[str]    ,      **a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       kwargs.get('''is_split_into_words'''    ,      a__				)

         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(*a__    ,      **a__				)


      def        a			(self	:				List[Any]    ,      a__	:				str    ,      a__	:				Optional[str] = None				):





         """simple docstring"""



         __snake_case								=       self._tokenizer.model.save(a__    ,      name=a__				)
         return tuple(a__				)


      def        a			(self	:				Tuple    ,      a__	:				"Conversation"				):





         """simple docstring"""



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

         if len(a__				) > self.model_max_length:
            __snake_case								=       input_ids[-self.model_max_length :]
         return input_ids

