


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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

_lowercase       :      Dict	=	{"vocab_file": "sentencepiece.bpe.model"}

_lowercase       :      Union[str, Any]	=	{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

_lowercase       :      Dict	=	{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

_lowercase       :      int	=	"▁"






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= VOCAB_FILES_NAMES
				lowerCAmelCase 		= PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase 		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase 		= ['input_ids', 'attention_mask']

				def __init__(						self				,   a__				,   a__="<s>"				,   a__="</s>"				,   a__="</s>"				,   a__="<s>"				,   a__="<unk>"				,   a__="<pad>"				,   a__="<mask>"				,   a__ = None				,   **a__				,   )	->				None:
					# Mask token behave like a normal word, i.e. include the space before it
					A								=				AddedToken(a__				,   lstrip=a__				,   rstrip=a__   ) if isinstance(a__				,   a__   ) else mask_token

					A								=				{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=a__				,   eos_token=a__				,   unk_token=a__				,   sep_token=a__				,   cls_token=a__				,   pad_token=a__				,   mask_token=a__				,   sp_model_kwargs=self.sp_model_kwargs				,   **a__				,   )

					A								=				vocab_file
					A								=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
					self.sp_model.Load(str(a__   )   )

					A								=				{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

					A								=				len(self.sp_model   ) - 1
					A								=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				List[int]:

					if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					A								=				[self.cls_token_id]
					A								=				[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None				,   a__ = False   )	->				List[int]:
					if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=a__				,   token_ids_a=a__				,   already_has_special_tokens=a__   )

					if token_ids_a is None:
						return [1] + ([0] * len(a__   )) + [1]
					return [1] + ([0] * len(a__   )) + [1, 1] + ([0] * len(a__   )) + [1]

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				List[int]:
					A								=				[self.sep_token_id]
					A								=				[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]

				@property
				def      _UpperCAmelCase					(						self   )	->				Tuple:
					return len(self.sp_model   )

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A								=				{self.convert_ids_to_tokens(a__   ): i for i in range(self.vocab_size   )}
					vocab.update(self.added_tokens_encoder   )
					return vocab

				def      _UpperCAmelCase					(						self				,   a__   )	->				List[str]:
					return self.sp_model.encode(a__				,   out_type=a__   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				Tuple:
					if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
					A								=				self.sp_model.PieceToId(a__   )

					return spm_id if spm_id else self.unk_token_id

				def      _UpperCAmelCase					(						self				,   a__   )	->				List[Any]:
					if index in self.fairseq_ids_to_tokens:
						return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(a__   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				List[str]:
					A								=				[]
					A								=				""""""
					A								=				False
					for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
							if not prev_is_special:
								out_string += " "
							out_string += self.sp_model.decode(a__   ) + token
							A								=				True
							A								=				[]
						else:
							current_sub_tokens.append(a__   )
							A								=				False
					out_string += self.sp_model.decode(a__   )
					return out_string.strip()

				def __getstate__(						self   )	->				int:
					A								=				self.__dict__.copy()
					A								=				None
					return state

				def __setstate__(						self				,   a__   )	->				Optional[Any]:
					A								=				d

					# for backward compatibility
					if not hasattr(self				,   """sp_model_kwargs"""   ):
						A								=				{}

					A								=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
					self.sp_model.Load(self.vocab_file   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				Tuple[str]:
					if not os.path.isdir(a__   ):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
						return
					A								=				os.path.join(
					    a__				,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

					if os.path.abspath(self.vocab_file   ) != os.path.abspath(a__   ) and os.path.isfile(self.vocab_file   ):
						copyfile(self.vocab_file				,   a__   )
					elif not os.path.isfile(self.vocab_file   ):
						with open(a__				,   """wb"""   ) as fi:
							A								=				self.sp_model.serialized_model_proto()
							fi.write(a__   )

					return (out_vocab_file,)










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       _UpperCamelCase    (					__snake_case    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= ConsistencyModelPipeline
				lowerCAmelCase 		= UNCONDITIONAL_IMAGE_GENERATION_PARAMS
				lowerCAmelCase 		= UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

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

				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				UNetaDModel.from_pretrained(
					    """diffusers/consistency-models-test"""				,   subfolder="""test_unet"""				,   )
					return unet

				@property
				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				UNetaDModel.from_pretrained(
					    """diffusers/consistency-models-test"""				,   subfolder="""test_unet_class_cond"""				,   )
					return unet

				def      _UpperCAmelCase					(						self				,   a__=False   )	->				Tuple:
					if class_cond:
						A								=				self.dummy_cond_unet
					else:
						A								=				self.dummy_uncond_unet

					# Default to CM multistep sampler
					A								=				CMStochasticIterativeScheduler(
					    num_train_timesteps=40				,   sigma_min=0.0_02				,   sigma_max=80.0				,   )

					A								=				{
					    """unet""": unet,
					    """scheduler""": scheduler,
					}

					return components

				def      _UpperCAmelCase					(						self				,   a__				,   a__=0   )	->				Tuple:
					if str(a__   ).startswith("""mps"""   ):
						A								=				torch.manual_seed(a__   )
					else:
						A								=				torch.Generator(device=a__   ).manual_seed(a__   )

					A								=				{
					    """batch_size""": 1,
					    """num_inference_steps""": None,
					    """timesteps""": [22, 0],
					    """generator""": generator,
					    """output_type""": """np""",
					}

					return inputs

				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A								=				self.get_dummy_components()
					A								=				ConsistencyModelPipeline(**a__   )
					A								=				pipe.to(a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_dummy_inputs(a__   )
					A								=				pipe(**a__   ).images
					assert image.shape == (1, 32, 32, 3)

					A								=				image[0, -3:, -3:, -1]
					A								=				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   )	->				Optional[int]:
					A								=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A								=				self.get_dummy_components(class_cond=a__   )
					A								=				ConsistencyModelPipeline(**a__   )
					A								=				pipe.to(a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_dummy_inputs(a__   )
					A								=				0
					A								=				pipe(**a__   ).images
					assert image.shape == (1, 32, 32, 3)

					A								=				image[0, -3:, -3:, -1]
					A								=				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   )	->				Optional[int]:
					A								=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A								=				self.get_dummy_components()
					A								=				ConsistencyModelPipeline(**a__   )
					A								=				pipe.to(a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_dummy_inputs(a__   )
					A								=				1
					A								=				None
					A								=				pipe(**a__   ).images
					assert image.shape == (1, 32, 32, 3)

					A								=				image[0, -3:, -3:, -1]
					A								=				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   )	->				str:
					A								=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A								=				self.get_dummy_components(class_cond=a__   )
					A								=				ConsistencyModelPipeline(**a__   )
					A								=				pipe.to(a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_dummy_inputs(a__   )
					A								=				1
					A								=				None
					A								=				0
					A								=				pipe(**a__   ).images
					assert image.shape == (1, 32, 32, 3)

					A								=				image[0, -3:, -3:, -1]
					A								=				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       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				Tuple:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()

				def      _UpperCAmelCase					(						self				,   a__=0				,   a__=False				,   a__="cpu"				,   a__=torch.floataa				,   a__=(1, 3, 64, 64)   )	->				str:
					A								=				torch.manual_seed(a__   )

					A								=				{
					    """num_inference_steps""": None,
					    """timesteps""": [22, 0],
					    """class_labels""": 0,
					    """generator""": generator,
					    """output_type""": """np""",
					}

					if get_fixed_latents:
						A								=				self.get_fixed_latents(seed=a__				,   device=a__				,   dtype=a__				,   shape=a__   )
						A								=				latents

					return inputs

				def      _UpperCAmelCase					(						self				,   a__=0				,   a__="cpu"				,   a__=torch.floataa				,   a__=(1, 3, 64, 64)   )	->				Optional[Any]:
					if type(a__   ) == str:
						A								=				torch.device(a__   )
					A								=				torch.Generator(device=a__   ).manual_seed(a__   )
					A								=				randn_tensor(a__				,   generator=a__				,   device=a__				,   dtype=a__   )
					return latents

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,   subfolder="""diffusers_cd_imagenet64_l2"""   )
					A								=				CMStochasticIterativeScheduler(
					    num_train_timesteps=40				,   sigma_min=0.0_02				,   sigma_max=80.0				,   )
					A								=				ConsistencyModelPipeline(unet=a__				,   scheduler=a__   )
					pipe.to(torch_device=a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_inputs()
					A								=				pipe(**a__   ).images
					assert image.shape == (1, 64, 64, 3)

					A								=				image[0, -3:, -3:, -1]

					A								=				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   )	->				Dict:
					A								=				UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,   subfolder="""diffusers_cd_imagenet64_l2"""   )
					A								=				CMStochasticIterativeScheduler(
					    num_train_timesteps=40				,   sigma_min=0.0_02				,   sigma_max=80.0				,   )
					A								=				ConsistencyModelPipeline(unet=a__				,   scheduler=a__   )
					pipe.to(torch_device=a__   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_inputs()
					A								=				1
					A								=				None
					A								=				pipe(**a__   ).images
					assert image.shape == (1, 64, 64, 3)

					A								=				image[0, -3:, -3:, -1]

					A								=				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   )	->				Union[str, Any]:
					A								=				UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,   subfolder="""diffusers_cd_imagenet64_l2"""   )
					A								=				CMStochasticIterativeScheduler(
					    num_train_timesteps=40				,   sigma_min=0.0_02				,   sigma_max=80.0				,   )
					A								=				ConsistencyModelPipeline(unet=a__				,   scheduler=a__   )
					pipe.to(torch_device=a__				,   torch_dtype=torch.floataa   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				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__   ):
						A								=				pipe(**a__   ).images
					assert image.shape == (1, 64, 64, 3)

					A								=				image[0, -3:, -3:, -1]

					A								=				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   )	->				List[Any]:
					A								=				UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,   subfolder="""diffusers_cd_imagenet64_l2"""   )
					A								=				CMStochasticIterativeScheduler(
					    num_train_timesteps=40				,   sigma_min=0.0_02				,   sigma_max=80.0				,   )
					A								=				ConsistencyModelPipeline(unet=a__				,   scheduler=a__   )
					pipe.to(torch_device=a__				,   torch_dtype=torch.floataa   )
					pipe.set_progress_bar_config(disable=a__   )

					A								=				self.get_inputs(get_fixed_latents=a__				,   device=a__   )
					A								=				1
					A								=				None
					# Ensure usage of flash attention in torch 2.0
					with sdp_kernel(enable_flash=a__				,   enable_math=a__				,   enable_mem_efficient=a__   ):
						A								=				pipe(**a__   ).images
					assert image.shape == (1, 64, 64, 3)

					A								=				image[0, -3:, -3:, -1]

					A								=				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



