

"""simple docstring"""




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


SCREAMING_SNAKE_CASE_     :			Any	  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     :			Any	  =			{'''vocab_file''': '''sentencepiece.bpe.model'''}

SCREAMING_SNAKE_CASE_     :			int	  =			{
    '''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'''
        ),
    },
}

SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			{
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			'''▁'''


class 		_A       (			__snake_case					):
      __a								=	VOCAB_FILES_NAMES
      __a								=	PRETRAINED_VOCAB_FILES_MAP
      __a								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a								=	['input_ids', 'attention_mask']





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__="<s>"    ,						SCREAMING_SNAKE_CASE__="</s>"    ,						SCREAMING_SNAKE_CASE__="</s>"    ,						SCREAMING_SNAKE_CASE__="<s>"    ,						SCREAMING_SNAKE_CASE__="<unk>"    ,						SCREAMING_SNAKE_CASE__="<pad>"    ,						SCREAMING_SNAKE_CASE__="<mask>"    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					str:
         lowerCamelCase__						=			AddedToken(__UpperCamelCase    ,						lstrip=__UpperCamelCase    ,						rstrip=__UpperCamelCase		) if isinstance(__UpperCamelCase    ,						__UpperCamelCase		) else mask_token

         lowerCamelCase__						=			{} if sp_model_kwargs is None else sp_model_kwargs

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

         lowerCamelCase__						=			vocab_file
         lowerCamelCase__						=			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(str(__UpperCamelCase		)		)

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

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					Tuple:


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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = False		)   ->					Optional[Any]:


         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__UpperCamelCase    ,						token_ids_a=__UpperCamelCase    ,						already_has_special_tokens=__UpperCamelCase		)

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					Optional[Any]:
         lowerCamelCase__						=			[self.sep_token_id]
         lowerCamelCase__						=			[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 					_lowerCamelCase  (  self		)   ->					List[Any]:
         return len(self.sp_model		)





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			{self.convert_ids_to_tokens(__UpperCamelCase		): i for i in range(self.vocab_size		)}
         vocab.update(self.added_tokens_encoder		)
         return vocab





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					str:
         return self.sp_model.encode(__UpperCamelCase    ,						out_type=__UpperCamelCase		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Any:


         if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
         lowerCamelCase__						=			self.sp_model.PieceToId(__UpperCamelCase		)

         return spm_id if spm_id else self.unk_token_id





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					str:


         if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(__UpperCamelCase		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			[]
         lowerCamelCase__						=			""
         lowerCamelCase__						=			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(__UpperCamelCase		) + token
               lowerCamelCase__						=			True
               lowerCamelCase__						=			[]
            else:
               current_sub_tokens.append(__UpperCamelCase		)
               lowerCamelCase__						=			False
         out_string += self.sp_model.decode(__UpperCamelCase		)
         return out_string.strip()





      def __getstate__(  self		)   ->					Any:
         lowerCamelCase__						=			self.__dict__.copy()
         lowerCamelCase__						=			None
         return state





      def __setstate__(  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Any:
         lowerCamelCase__						=			d

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

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					int:


         if not os.path.isdir(__UpperCamelCase		):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
            return
         lowerCamelCase__						=			os.path.join(
             __UpperCamelCase    ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

         return (out_vocab_file,)







"""simple docstring"""




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin


@skip_mps
class 		_A       (			__a				,       __a				,       unittest.TestCase					):
      __a								=	IFPipeline
      __a								=	TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
      __a								=	TEXT_TO_IMAGE_BATCH_PARAMS
      __a								=	PipelineTesterMixin.required_optional_params - {'latents'}





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         return self._get_dummy_components()





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=0		)   ->					Union[str, Any]:
         if str(SCREAMING_SNAKE_CASE__		).startswith("mps"		):
            lowerCamelCase__						=			torch.manual_seed(SCREAMING_SNAKE_CASE__		)
         else:
            lowerCamelCase__						=			torch.Generator(device=SCREAMING_SNAKE_CASE__		).manual_seed(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			{
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "output_type": "numpy",
         }

         return inputs





      def 					_lowerCamelCase  (  self		)   ->					str:
         self._test_save_load_optional_components()





      @unittest.skipIf(torch_device != "cuda"    ,						reason="float16 requires CUDA"		)
      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1e-1		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         self._test_save_load_local()





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2    ,						)





      @unittest.skipIf(
          torch_device != "cuda" or not is_xformers_available()    ,						reason="XFormers attention is only available with CUDA and `xformers` installed"    ,						)
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)






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





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





      def 					_lowerCamelCase  (  self		)   ->					str:
         # if

         lowerCamelCase__						=			IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"    ,						variant="fp16"    ,						torch_dtype=torch.floataa		)

         lowerCamelCase__						=			IFSuperResolutionPipeline.from_pretrained(
             "DeepFloyd/IF-II-L-v1.0"    ,						variant="fp16"    ,						torch_dtype=torch.floataa    ,						text_encoder=SCREAMING_SNAKE_CASE__    ,						tokenizer=SCREAMING_SNAKE_CASE__		)

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to("cuda"		)

         lowerCamelCase__					,       lowerCamelCase__						=			pipe_a.encode_prompt("anime turtle"    ,						device="cuda"		)

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         lowerCamelCase__						=			None
         lowerCamelCase__						=			None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

         self._test_if(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         lowerCamelCase__						=			IFImgaImgPipeline(**pipe_a.components		)
         lowerCamelCase__						=			IFImgaImgSuperResolutionPipeline(**pipe_a.components		)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

         self._test_if_imgaimg(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         lowerCamelCase__						=			IFInpaintingPipeline(**pipe_a.components		)
         lowerCamelCase__						=			IFInpaintingSuperResolutionPipeline(**pipe_a.components		)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

         self._test_if_inpainting(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					int:
         # pipeline 1

         _start_torch_memory_measurement()

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)
         lowerCamelCase__						=			pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE__    ,						negative_prompt_embeds=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=2    ,						generator=SCREAMING_SNAKE_CASE__    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

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

         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         assert mem_bytes < 13 * 10**9

         lowerCamelCase__						=			load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"		)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         # pipeline 2

         _start_torch_memory_measurement()

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)

         lowerCamelCase__						=			floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE__    ,						negative_prompt_embeds=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						generator=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=2    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

         assert image.shape == (256, 256, 3)

         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         lowerCamelCase__						=			load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"		)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         # pipeline 1

         _start_torch_memory_measurement()

         lowerCamelCase__						=			floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)

         lowerCamelCase__						=			pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE__    ,						negative_prompt_embeds=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=2    ,						generator=SCREAMING_SNAKE_CASE__    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

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

         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         lowerCamelCase__						=			load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"		)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         # pipeline 2

         _start_torch_memory_measurement()

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)

         lowerCamelCase__						=			floats_tensor((1, 3, 256, 256)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE__    ,						negative_prompt_embeds=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						original_image=SCREAMING_SNAKE_CASE__    ,						generator=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=2    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

         assert image.shape == (256, 256, 3)

         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         lowerCamelCase__						=			load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"		)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         # pipeline 1

         _start_torch_memory_measurement()

         lowerCamelCase__						=			floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(1		)		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)
         lowerCamelCase__						=			pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE__    ,						negative_prompt_embeds=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						mask_image=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=2    ,						generator=SCREAMING_SNAKE_CASE__    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

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

         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         lowerCamelCase__						=			load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"		)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         # pipeline 2

         _start_torch_memory_measurement()

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)

         lowerCamelCase__						=			floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			floats_tensor((1, 3, 256, 256)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			floats_tensor((1, 3, 256, 256)    ,						rng=random.Random(1		)		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE__    ,						negative_prompt_embeds=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						mask_image=SCREAMING_SNAKE_CASE__    ,						original_image=SCREAMING_SNAKE_CASE__    ,						generator=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=2    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

         assert image.shape == (256, 256, 3)

         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         lowerCamelCase__						=			load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"		)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)






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




   """simple docstring"""
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()




