import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


lowerCamelCase	:	List[Any]				=   logging.get_logger(__name__)


lowerCamelCase	:	List[str]				=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCamelCase	:	List[Any]				=   {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

lowerCamelCase	:	int				=   {
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						VOCAB_FILES_NAMES
      _snake_case												=						PRETRAINED_VOCAB_FILES_MAP
      _snake_case												=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case												=						["""input_ids""", """attention_mask"""]
      _snake_case												=						BartTokenizer

      def __init__(				self    ,				A=None    ,				A=None    ,				A=None    ,				A="replace"    ,				A="<s>"    ,				A="</s>"    ,				A="</s>"    ,				A="<s>"    ,				A="<unk>"    ,				A="<pad>"    ,				A="<mask>"    ,				A=False    ,				A=True    ,				**A    ,				)		->    Union[str, Any]:
         super().__init__(
             A    ,				A    ,				tokenizer_file=A    ,				errors=A    ,				bos_token=A    ,				eos_token=A    ,				sep_token=A    ,				cls_token=A    ,				unk_token=A    ,				pad_token=A    ,				mask_token=A    ,				add_prefix_space=A    ,				trim_offsets=A    ,				**A    ,				)

         snake_case				:    Optional[int]           =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
         if pre_tok_state.get("""add_prefix_space"""    ,				A     ) != add_prefix_space:
            snake_case				:    Optional[int]           =	getattr(A    ,				pre_tok_state.pop("""type"""     )     )
            snake_case				:    List[str]           =	add_prefix_space
            snake_case				:    Any           =	pre_tok_class(**A     )

         snake_case				:    List[Any]           =	add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         snake_case				:    int           =	"""post_processor"""
         snake_case				:    List[str]           =	getattr(self.backend_tokenizer    ,				A    ,				A     )
         if tokenizer_component_instance:
            snake_case				:    List[str]           =	json.loads(tokenizer_component_instance.__getstate__()     )

            # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
            if "sep" in state:
               snake_case				:    Optional[Any]           =	tuple(state["""sep"""]     )
            if "cls" in state:
               snake_case				:    Any           =	tuple(state["""cls"""]     )

            snake_case				:    Any           =	False

            if state.get("""add_prefix_space"""    ,				A     ) != add_prefix_space:
               snake_case				:    List[str]           =	add_prefix_space
               snake_case				:    int           =	True

            if state.get("""trim_offsets"""    ,				A     ) != trim_offsets:
               snake_case				:    Optional[int]           =	trim_offsets
               snake_case				:    List[Any]           =	True

            if changes_to_apply:
               snake_case				:    Any           =	getattr(A    ,				state.pop("""type"""     )     )
               snake_case				:    List[Any]           =	component_class(**A     )
               setattr(self.backend_tokenizer    ,				A    ,				A     )

      @property
      def      UpperCAmelCase				(				self     )		->    str:
         if self._mask_token is None:
            if self.verbose:
               logger.error("""Using mask_token, but it is not set yet."""     )
            return None
         return str(self._mask_token     )

      @mask_token.setter
      def      UpperCAmelCase				(				self    ,				A     )		->    Any:
         snake_case				:    Any           =	AddedToken(A    ,				lstrip=A    ,				rstrip=A     ) if isinstance(A    ,				A     ) else value
         snake_case				:    List[str]           =	value

      def      UpperCAmelCase				(				self    ,				*A    ,				**A     )		->    BatchEncoding:
         snake_case				:    int           =	kwargs.get("""is_split_into_words"""    ,				A     )

         if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                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      UpperCAmelCase				(				self    ,				*A    ,				**A     )		->    BatchEncoding:
         snake_case				:    Optional[Any]           =	kwargs.get("""is_split_into_words"""    ,				A     )

         if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                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      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    Tuple[str]:
         snake_case				:    Optional[int]           =	self._tokenizer.model.save(A    ,				name=A     )
         return tuple(A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A=None     )		->    List[str]:
         snake_case				:    List[Any]           =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
            return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

      def      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    List[int]:
         snake_case				:    Optional[int]           =	[self.sep_token_id]
         snake_case				:    Optional[int]           =	[self.cls_token_id]

         if token_ids_a is None:
            return len(cls + token_ids_a + sep     ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]











import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 						__lowercase		(UpperCamelCase__					,		unittest.TestCase							):




      """simple docstring"""





      _snake_case												=						ConsistencyModelPipeline
      _snake_case												=						UNCONDITIONAL_IMAGE_GENERATION_PARAMS
      _snake_case												=						UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

      # Override required_optional_params to remove num_images_per_prompt
      _snake_case												=						frozenset(
          [
              """num_inference_steps""",
              """generator""",
              """latents""",
              """output_type""",
              """return_dict""",
              """callback""",
              """callback_steps""",
          ]							)

      @property
      def      UpperCAmelCase				(				self     )		->    List[str]:
         snake_case				:    Dict           =	UNetaDModel.from_pretrained(
             """diffusers/consistency-models-test"""    ,				subfolder="""test_unet"""    ,				)
         return unet

      @property
      def      UpperCAmelCase				(				self     )		->    Any:
         snake_case				:    Optional[int]           =	UNetaDModel.from_pretrained(
             """diffusers/consistency-models-test"""    ,				subfolder="""test_unet_class_cond"""    ,				)
         return unet

      def      UpperCAmelCase				(				self    ,				A=False     )		->    Optional[Any]:
         if class_cond:
            snake_case				:    List[str]           =	self.dummy_cond_unet
         else:
            snake_case				:    Optional[Any]           =	self.dummy_uncond_unet

         # Default to CM multistep sampler
         snake_case				:    Optional[Any]           =	CMStochasticIterativeScheduler(
             num_train_timesteps=4_0    ,				sigma_min=0.0_02    ,				sigma_max=80.0    ,				)

         snake_case				:    Dict           =	{
             """unet""": unet,
             """scheduler""": scheduler,
         }

         return components

      def      UpperCAmelCase				(				self    ,				A    ,				A=0     )		->    Optional[int]:
         if str(A     ).startswith("""mps"""     ):
            snake_case				:    Union[str, Any]           =	torch.manual_seed(A     )
         else:
            snake_case				:    Dict           =	torch.Generator(device=A     ).manual_seed(A     )

         snake_case				:    Tuple           =	{
             """batch_size""": 1,
             """num_inference_steps""": None,
             """timesteps""": [2_2, 0],
             """generator""": generator,
             """output_type""": """np""",
         }

         return inputs

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         snake_case				:    Union[str, Any]           =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         snake_case				:    List[Any]           =	self.get_dummy_components()
         snake_case				:    Dict           =	ConsistencyModelPipeline(**A     )
         snake_case				:    int           =	pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    Any           =	self.get_dummy_inputs(A     )
         snake_case				:    Tuple           =	pipe(**A     ).images
         assert image.shape == (1, 3_2, 3_2, 3)

         snake_case				:    Dict           =	image[0, -3:, -3:, -1]
         snake_case				:    Tuple           =	np.array([0.35_72, 0.62_73, 0.40_31, 0.39_61, 0.43_21, 0.57_30, 0.52_66, 0.47_80, 0.50_04]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         snake_case				:    Optional[int]           =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         snake_case				:    Tuple           =	self.get_dummy_components(class_cond=A     )
         snake_case				:    Dict           =	ConsistencyModelPipeline(**A     )
         snake_case				:    List[Any]           =	pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    str           =	self.get_dummy_inputs(A     )
         snake_case				:    Optional[int]           =	0
         snake_case				:    List[Any]           =	pipe(**A     ).images
         assert image.shape == (1, 3_2, 3_2, 3)

         snake_case				:    Optional[int]           =	image[0, -3:, -3:, -1]
         snake_case				:    Dict           =	np.array([0.35_72, 0.62_73, 0.40_31, 0.39_61, 0.43_21, 0.57_30, 0.52_66, 0.47_80, 0.50_04]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3

      def      UpperCAmelCase				(				self     )		->    Dict:
         snake_case				:    str           =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         snake_case				:    Optional[int]           =	self.get_dummy_components()
         snake_case				:    List[str]           =	ConsistencyModelPipeline(**A     )
         snake_case				:    Dict           =	pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    Dict           =	self.get_dummy_inputs(A     )
         snake_case				:    Tuple           =	1
         snake_case				:    Optional[Any]           =	None
         snake_case				:    List[Any]           =	pipe(**A     ).images
         assert image.shape == (1, 3_2, 3_2, 3)

         snake_case				:    Optional[Any]           =	image[0, -3:, -3:, -1]
         snake_case				:    List[str]           =	np.array([0.50_04, 0.50_04, 0.49_94, 0.50_08, 0.49_76, 0.50_18, 0.49_90, 0.49_82, 0.49_87]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3

      def      UpperCAmelCase				(				self     )		->    Optional[Any]:
         snake_case				:    Optional[int]           =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         snake_case				:    Dict           =	self.get_dummy_components(class_cond=A     )
         snake_case				:    List[Any]           =	ConsistencyModelPipeline(**A     )
         snake_case				:    List[Any]           =	pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    Optional[Any]           =	self.get_dummy_inputs(A     )
         snake_case				:    Optional[Any]           =	1
         snake_case				:    Any           =	None
         snake_case				:    Optional[int]           =	0
         snake_case				:    List[Any]           =	pipe(**A     ).images
         assert image.shape == (1, 3_2, 3_2, 3)

         snake_case				:    Optional[int]           =	image[0, -3:, -3:, -1]
         snake_case				:    List[str]           =	np.array([0.50_04, 0.50_04, 0.49_94, 0.50_08, 0.49_76, 0.50_18, 0.49_90, 0.49_82, 0.49_87]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3




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




      """simple docstring"""





      def      UpperCAmelCase				(				self     )		->    Dict:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

      def      UpperCAmelCase				(				self    ,				A=0    ,				A=False    ,				A="cpu"    ,				A=torch.floataa    ,				A=(1, 3, 6_4, 6_4)     )		->    int:
         snake_case				:    Union[str, Any]           =	torch.manual_seed(A     )

         snake_case				:    Tuple           =	{
             """num_inference_steps""": None,
             """timesteps""": [2_2, 0],
             """class_labels""": 0,
             """generator""": generator,
             """output_type""": """np""",
         }

         if get_fixed_latents:
            snake_case				:    Optional[int]           =	self.get_fixed_latents(seed=A    ,				device=A    ,				dtype=A    ,				shape=A     )
            snake_case				:    int           =	latents

         return inputs

      def      UpperCAmelCase				(				self    ,				A=0    ,				A="cpu"    ,				A=torch.floataa    ,				A=(1, 3, 6_4, 6_4)     )		->    Any:
         if type(A     ) == str:
            snake_case				:    List[str]           =	torch.device(A     )
         snake_case				:    Any           =	torch.Generator(device=A     ).manual_seed(A     )
         snake_case				:    Dict           =	randn_tensor(A    ,				generator=A    ,				device=A    ,				dtype=A     )
         return latents

      def      UpperCAmelCase				(				self     )		->    Dict:
         snake_case				:    List[Any]           =	UNetaDModel.from_pretrained("""diffusers/consistency_models"""    ,				subfolder="""diffusers_cd_imagenet64_l2"""     )
         snake_case				:    Optional[int]           =	CMStochasticIterativeScheduler(
             num_train_timesteps=4_0    ,				sigma_min=0.0_02    ,				sigma_max=80.0    ,				)
         snake_case				:    Any           =	ConsistencyModelPipeline(unet=A    ,				scheduler=A     )
         pipe.to(torch_device=A     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    Tuple           =	self.get_inputs()
         snake_case				:    List[str]           =	pipe(**A     ).images
         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case				:    Optional[Any]           =	image[0, -3:, -3:, -1]

         snake_case				:    Union[str, Any]           =	np.array([0.08_88, 0.08_81, 0.06_66, 0.04_79, 0.02_92, 0.01_95, 0.02_01, 0.01_63, 0.02_54]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2e-2

      def      UpperCAmelCase				(				self     )		->    Tuple:
         snake_case				:    Tuple           =	UNetaDModel.from_pretrained("""diffusers/consistency_models"""    ,				subfolder="""diffusers_cd_imagenet64_l2"""     )
         snake_case				:    int           =	CMStochasticIterativeScheduler(
             num_train_timesteps=4_0    ,				sigma_min=0.0_02    ,				sigma_max=80.0    ,				)
         snake_case				:    List[str]           =	ConsistencyModelPipeline(unet=A    ,				scheduler=A     )
         pipe.to(torch_device=A     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    Union[str, Any]           =	self.get_inputs()
         snake_case				:    Tuple           =	1
         snake_case				:    List[str]           =	None
         snake_case				:    Any           =	pipe(**A     ).images
         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case				:    List[Any]           =	image[0, -3:, -3:, -1]

         snake_case				:    Union[str, Any]           =	np.array([0.03_40, 0.01_52, 0.00_63, 0.02_67, 0.02_21, 0.01_07, 0.04_16, 0.01_86, 0.02_17]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2e-2

      @require_torch_a
      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         snake_case				:    List[Any]           =	UNetaDModel.from_pretrained("""diffusers/consistency_models"""    ,				subfolder="""diffusers_cd_imagenet64_l2"""     )
         snake_case				:    Optional[Any]           =	CMStochasticIterativeScheduler(
             num_train_timesteps=4_0    ,				sigma_min=0.0_02    ,				sigma_max=80.0    ,				)
         snake_case				:    List[Any]           =	ConsistencyModelPipeline(unet=A    ,				scheduler=A     )
         pipe.to(torch_device=A    ,				torch_dtype=torch.floataa     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    List[str]           =	self.get_inputs(get_fixed_latents=A    ,				device=A     )
         # Ensure usage of flash attention in torch 2.0
         with sdp_kernel(enable_flash=A    ,				enable_math=A    ,				enable_mem_efficient=A     ):
            snake_case				:    Tuple           =	pipe(**A     ).images
         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case				:    Union[str, Any]           =	image[0, -3:, -3:, -1]

         snake_case				:    int           =	np.array([0.18_75, 0.14_28, 0.12_89, 0.21_51, 0.20_92, 0.14_77, 0.18_77, 0.16_41, 0.13_53]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3

      @require_torch_a
      def      UpperCAmelCase				(				self     )		->    Optional[Any]:
         snake_case				:    Optional[Any]           =	UNetaDModel.from_pretrained("""diffusers/consistency_models"""    ,				subfolder="""diffusers_cd_imagenet64_l2"""     )
         snake_case				:    Any           =	CMStochasticIterativeScheduler(
             num_train_timesteps=4_0    ,				sigma_min=0.0_02    ,				sigma_max=80.0    ,				)
         snake_case				:    Dict           =	ConsistencyModelPipeline(unet=A    ,				scheduler=A     )
         pipe.to(torch_device=A    ,				torch_dtype=torch.floataa     )
         pipe.set_progress_bar_config(disable=A     )

         snake_case				:    Optional[int]           =	self.get_inputs(get_fixed_latents=A    ,				device=A     )
         snake_case				:    Union[str, Any]           =	1
         snake_case				:    Dict           =	None
         # Ensure usage of flash attention in torch 2.0
         with sdp_kernel(enable_flash=A    ,				enable_math=A    ,				enable_mem_efficient=A     ):
            snake_case				:    List[Any]           =	pipe(**A     ).images
         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case				:    List[Any]           =	image[0, -3:, -3:, -1]

         snake_case				:    Dict           =	np.array([0.16_63, 0.19_48, 0.22_75, 0.16_80, 0.12_04, 0.12_45, 0.18_58, 0.13_38, 0.20_95]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3





