



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_lowercase						  =		logging.get_logger(__name__)


_lowercase						  =		{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

_lowercase						  =		{
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
}

_lowercase						  =		{
    "allenai/longformer-base-4096": 4096,
    "allenai/longformer-large-4096": 4096,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4096,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4096,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4096,
}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 		lowerCAmelCase__    (					)->  Optional[int]:
    A__		 =					(
        list(range(ord('''!'''	) ,       ord('''~'''	) + 1	)	) + list(range(ord('''¡'''	) ,       ord('''¬'''	) + 1	)	) + list(range(ord('''®'''	) ,       ord('''ÿ'''	) + 1	)	)
    )
    A__		 =					bs[:]
    A__		 =					0
    for b in range(2**8	):
        if b not in bs:
            bs.append(UpperCamelCase_	)
            cs.append(2**8 + n	)
            n += 1
    A__		 =					[chr(UpperCamelCase_	) for n in cs]
    return dict(zip(UpperCamelCase_ ,       UpperCamelCase_	)	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Union[str, Any]	)->  List[str]:
    A__		 =					set()
    A__		 =					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)	)
        A__		 =					char
    return pairs



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				VOCAB_FILES_NAMES
     UpperCamelCase__		    =				PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase__		    =				['''input_ids''', '''attention_mask''']




     def __init__(				self ,						a__ ,						a__ ,						a__="replace" ,						a__="<s>" ,						a__="</s>" ,						a__="</s>" ,						a__="<s>" ,						a__="<unk>" ,						a__="<pad>" ,						a__="<mask>" ,						a__=False ,						**a__ ,						):
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else bos_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else eos_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else sep_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else cls_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else unk_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else pad_token

         # 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

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

         with open(a__ ,						encoding='''utf-8''') as vocab_handle:
             A__		 =					json.load(a__)
         A__		 =					{v: k for k, v in self.encoder.items()}
         A__		 =					errors  # how to handle errors in decoding
         A__		 =					bytes_to_unicode()
         A__		 =					{v: k for k, v in self.byte_encoder.items()}
         with open(a__ ,						encoding='''utf-8''') as merges_handle:
             A__		 =					merges_handle.read().split('''\n''')[1:-1]
         A__		 =					[tuple(merge.split()) for merge in bpe_merges]
         A__		 =					dict(zip(a__ ,						range(len(a__))))
         A__		 =					{}
         A__		 =					add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         A__		 =					re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''')




     @property
     def 		snake_case_		(				self):
         return len(self.encoder)




     def 		snake_case_		(				self):
         return dict(self.encoder ,						**self.added_tokens_encoder)




     def 		snake_case_		(				self ,						a__):
         if token in self.cache:
             return self.cache[token]
         A__		 =					tuple(a__)
         A__		 =					get_pairs(a__)

         if not pairs:
             return token

         while True:
             A__		 =					min(a__ ,						key=lambda a__: self.bpe_ranks.get(a__ ,						float('''inf''')))
             if bigram not in self.bpe_ranks:
                 break
             A__ ,	A__		 =					bigram
             A__		 =					[]
             A__		 =					0
             while i < len(a__):
                 try:
                     A__		 =					word.index(a__ ,						a__)
                 except ValueError:
                     new_word.extend(word[i:])
                     break
                 else:
                     new_word.extend(word[i:j])
                     A__		 =					j

                 if word[i] == first and i < len(a__) - 1 and word[i + 1] == second:
                     new_word.append(first + second)
                     i += 2
                 else:
                     new_word.append(word[i])
                     i += 1
             A__		 =					tuple(a__)
             A__		 =					new_word
             if len(a__) == 1:
                 break
             else:
                 A__		 =					get_pairs(a__)
         A__		 =					''' '''.join(a__)
         A__		 =					word
         return word




     def 		snake_case_		(				self ,						a__):
         A__		 =					[]
         for token in re.findall(self.pat ,						a__):
             A__		 =					''''''.join(
                 self.byte_encoder[b] for b in token.encode('''utf-8'''))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
             bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a__).split(''' '''))
         return bpe_tokens




     def 		snake_case_		(				self ,						a__):
         return self.encoder.get(a__ ,						self.encoder.get(self.unk_token))




     def 		snake_case_		(				self ,						a__):
         return self.decoder.get(a__)




     def 		snake_case_		(				self ,						a__):
         A__		 =					''''''.join(a__)
         A__		 =					bytearray([self.byte_decoder[c] for c in text]).decode('''utf-8''' ,						errors=self.errors)
         return text




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         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'''])
         A__		 =					os.path.join(
             a__ ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''])

         with open(a__ ,						'''w''' ,						encoding='''utf-8''') as f:
             f.write(json.dumps(self.encoder ,						indent=2 ,						sort_keys=a__ ,						ensure_ascii=a__) + '''\n''')

         A__		 =					0
         with open(a__ ,						'''w''' ,						encoding='''utf-8''') as writer:
             writer.write('''#version: 0.2\n''')
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,						key=lambda a__: kv[1]):
                 if index != token_index:
                     logger.warning(
                         F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                         ''' Please check that the tokenizer is not corrupted!''')
                     A__		 =					token_index
                 writer.write(''' '''.join(a__) + '''\n''')
                 index += 1

         return vocab_file, merge_file




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         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 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = False):
         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 		snake_case_		(				self ,						a__ ,						a__ = None):
         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]




     def 		snake_case_		(				self ,						a__ ,						a__=False ,						**a__):
         A__		 =					kwargs.pop('''add_prefix_space''' ,						self.add_prefix_space)
         if (is_split_into_words or add_prefix_space) and (len(a__) > 0 and not text[0].isspace()):
             A__		 =					''' ''' + text
         return (text, kwargs)





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     _UpperCAmelCase			(       A__			,      A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				IFPipeline
     UpperCamelCase__		    =				TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     UpperCamelCase__		    =				TEXT_TO_IMAGE_BATCH_PARAMS
     UpperCamelCase__		    =				PipelineTesterMixin.required_optional_params - {'''latents'''}




     def 		snake_case_		(				self):
         return self._get_dummy_components()




     def 		snake_case_		(				self ,						a__ ,						a__=0):
         if str(a__).startswith('''mps'''):
             A__		 =					torch.manual_seed(a__)
         else:
             A__		 =					torch.Generator(device=a__).manual_seed(a__)

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

         return inputs




     def 		snake_case_		(				self):
         self._test_save_load_optional_components()




     @unittest.skipIf(torch_device != '''cuda''' ,						reason='''float16 requires CUDA''')
     def 		snake_case_		(				self):
         # 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 		snake_case_		(				self):
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




     def 		snake_case_		(				self):
         self._test_save_load_local()




     def 		snake_case_		(				self):
         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 		snake_case_		(				self):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)







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




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




     def 		snake_case_		(				self):
         # if

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

         A__		 =					IFSuperResolutionPipeline.from_pretrained(
             '''DeepFloyd/IF-II-L-v1.0''' ,						variant='''fp16''' ,						torch_dtype=torch.floataa ,						text_encoder=a__ ,						tokenizer=a__)

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('''cuda''')

         A__ ,	A__		 =					pipe_a.encode_prompt('''anime turtle''' ,						device='''cuda''')

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

         A__		 =					None
         A__		 =					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(a__ ,						a__ ,						a__ ,						a__)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         A__		 =					IFImgaImgPipeline(**pipe_a.components)
         A__		 =					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(a__ ,						a__ ,						a__ ,						a__)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         A__		 =					IFInpaintingPipeline(**pipe_a.components)
         A__		 =					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(a__ ,						a__ ,						a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)
         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_3 * 1_0**9

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

         # pipeline 2

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

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




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_0 * 1_0**9

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

         # pipeline 2

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						original_image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         A__		 =					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(a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(1)).to(a__)

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)
         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						mask_image=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_0 * 1_0**9

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

         # pipeline 2

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(1)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						mask_image=a__ ,						original_image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         A__		 =					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(a__ ,						a__)







def 		lowerCAmelCase__    (					)->  List[Any]:
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()
