





'''simple docstring'''





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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


_lowerCAmelCase 				=     logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
_lowerCAmelCase 				=     {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

_lowerCAmelCase 				=     {
    """allenai/led-base-16384""": 16_384,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def     _lowerCAmelCase		(  ):
   '''simple docstring'''


   A_     :				Optional[Any]			      =	(
       list(range(ord("""!"""     )  ,ord("""~"""     ) + 1     )     ) + list(range(ord("""¡"""     )  ,ord("""¬"""     ) + 1     )     ) + list(range(ord("""®"""     )  ,ord("""ÿ"""     ) + 1     )     )
   )
   A_     :				str			      =	bs[:]
   A_     :				List[str]			      =	0
   for b in range(2**8     ):
      if b not in bs:
         bs.append(_lowerCAmelCase     )
         cs.append(2**8 + n     )
         n += 1
   A_     :				Tuple			      =	[chr(_lowerCAmelCase     ) for n in cs]
   return dict(zip(_lowerCAmelCase  ,_lowerCAmelCase     )     )



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Any			      =	set()
   A_     :				Dict			      =	word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)     )
      A_     :				Optional[int]			      =	char
   return pairs







class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							VOCAB_FILES_NAMES
      a						       =							PRETRAINED_VOCAB_FILES_MAP
      a						       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a						       =							['''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_     :				Dict			      =	AddedToken(a__      ,       lstrip=a__      ,       rstrip=a__       ) if isinstance(a__      ,       a__       ) else bos_token
         A_     :				Tuple			      =	AddedToken(a__      ,       lstrip=a__      ,       rstrip=a__       ) if isinstance(a__      ,       a__       ) else eos_token
         A_     :				Dict			      =	AddedToken(a__      ,       lstrip=a__      ,       rstrip=a__       ) if isinstance(a__      ,       a__       ) else sep_token
         A_     :				Tuple			      =	AddedToken(a__      ,       lstrip=a__      ,       rstrip=a__       ) if isinstance(a__      ,       a__       ) else cls_token
         A_     :				Dict			      =	AddedToken(a__      ,       lstrip=a__      ,       rstrip=a__       ) if isinstance(a__      ,       a__       ) else unk_token
         A_     :				Dict			      =	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_     :				Tuple			      =	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_     :				Optional[Any]			      =	json.load(a__       )
         A_     :				Tuple			      =	{v: k for k, v in self.encoder.items()}
         A_     :				Union[str, Any]			      =	errors  # how to handle errors in decoding
         A_     :				List[Any]			      =	bytes_to_unicode()
         A_     :				Union[str, Any]			      =	{v: k for k, v in self.byte_encoder.items()}
         with open(a__      ,       encoding="""utf-8"""       ) as merges_handle:
            A_     :				Any			      =	merges_handle.read().split("""\n"""       )[1:-1]
         A_     :				Optional[int]			      =	[tuple(merge.split()       ) for merge in bpe_merges]
         A_     :				Union[str, Any]			      =	dict(zip(a__      ,       range(len(a__       )       )       )       )
         A_     :				Optional[int]			      =	{}
         A_     :				Union[str, Any]			      =	add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         A_     :				List[str]			      =	re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""       )
      @property
      # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
      def 		_lowerCamelCase      (		self       ):
         return len(self.encoder       )
      def 		_lowerCamelCase      (		self       ):
         return dict(self.encoder      ,       **self.added_tokens_encoder       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         if token in self.cache:
            return self.cache[token]
         A_     :				Any			      =	tuple(a__       )
         A_     :				Union[str, Any]			      =	get_pairs(a__       )

         if not pairs:
            return token

         while True:
            A_     :				List[Any]			      =	min(a__      ,       key=lambda a__       : self.bpe_ranks.get(a__      ,       float("""inf"""       )       )       )
            if bigram not in self.bpe_ranks:
               break
            A_     :				Any			      =	bigram
            A_     :				Optional[Any]			      =	[]
            A_     :				str			      =	0
            while i < len(a__       ):
               try:
                  A_     :				List[Any]			      =	word.index(a__      ,       a__       )
               except ValueError:
                  new_word.extend(word[i:]       )
                  break
               else:
                  new_word.extend(word[i:j]       )
                  A_     :				Tuple			      =	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_     :				Union[str, Any]			      =	tuple(a__       )
            A_     :				Dict			      =	new_word
            if len(a__       ) == 1:
               break
            else:
               A_     :				Any			      =	get_pairs(a__       )
         A_     :				Optional[Any]			      =	""" """.join(a__       )
         A_     :				str			      =	word
         return word
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Dict			      =	[]
         for token in re.findall(self.pat      ,       a__       ):
            A_     :				Any			      =	"""""".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 		_lowerCamelCase      (		self      ,       a__       ):
         return self.encoder.get(a__      ,       self.encoder.get(self.unk_token       )       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         return self.decoder.get(a__       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				str			      =	"""""".join(a__       )
         A_     :				int			      =	bytearray([self.byte_decoder[c] for c in text]       ).decode("""utf-8"""      ,       errors=self.errors       )
         return text
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         if not os.path.isdir(a__       ):
            logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
            return
         A_     :				List[Any]			      =	os.path.join(
             a__      ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
         A_     :				Union[str, Any]			      =	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_     :				List[str]			      =	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_     :				Optional[Any]			      =	token_index
               writer.write(""" """.join(a__       ) + """\n"""       )
               index += 1

         return vocab_file, merge_file
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         A_     :				Any			      =	[self.cls_token_id]
         A_     :				List[str]			      =	[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
      def 		_lowerCamelCase      (		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 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         A_     :				List[str]			      =	[self.sep_token_id]
         A_     :				Tuple			      =	[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 		_lowerCamelCase      (		self      ,       a__      ,       a__=False      ,       **a__       ):
         A_     :				Dict			      =	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_     :				int			      =	""" """ + text
         return (text, kwargs)






      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None      ,       a__ = PaddingStrategy.DO_NOT_PAD      ,       a__ = None      ,       a__ = None      ,       ):
         A_     :				Union[str, Any]			      =	super()._pad(
             encoded_inputs=a__      ,       max_length=a__      ,       padding_strategy=a__      ,       pad_to_multiple_of=a__      ,       return_attention_mask=a__      ,       )

         # Load from model defaults
         if return_attention_mask is None:
            A_     :				List[str]			      =	"""attention_mask""" in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
            A_     :				Optional[int]			      =	encoded_inputs[self.model_input_names[0]]
            # `global_attention_mask` need to have the same length as other (sequential) inputs.
            A_     :				List[str]			      =	len(encoded_inputs["""global_attention_mask"""]       ) != len(a__       )

            if needs_to_be_padded:
               A_     :				Any			      =	len(a__       ) - len(encoded_inputs["""global_attention_mask"""]       )

               if self.padding_side == "right":
                  # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                  A_     :				Optional[int]			      =	(
                      encoded_inputs["""global_attention_mask"""] + [-1] * difference
                  )
               elif self.padding_side == "left":
                  A_     :				Any			      =	[-1] * difference + encoded_inputs[
                      """global_attention_mask"""
                  ]
               else:
                  raise ValueError("""Invalid padding strategy:""" + str(self.padding_side       )       )

         return encoded_inputs













import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    _UpperCAmelCase							(		_lowerCamelCase			,       _lowerCamelCase			,       _lowerCamelCase			,       unittest.TestCase     ):
      a						       =							AltDiffusionPipeline
      a						       =							TEXT_TO_IMAGE_PARAMS
      a						       =							TEXT_TO_IMAGE_BATCH_PARAMS
      a						       =							TEXT_TO_IMAGE_IMAGE_PARAMS
      a						       =							TEXT_TO_IMAGE_IMAGE_PARAMS
      def 		_lowerCamelCase      (		self       ):
         torch.manual_seed(0       )
         A_     :				Optional[int]			      =	UNetaDConditionModel(
             block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,       cross_attention_dim=32      ,       )
         A_     :				List[Any]			      =	DDIMScheduler(
             beta_start=0.0_0085      ,       beta_end=0.012      ,       beta_schedule="""scaled_linear"""      ,       clip_sample=a__      ,       set_alpha_to_one=a__      ,       )
         torch.manual_seed(0       )
         A_     :				int			      =	AutoencoderKL(
             block_out_channels=[32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,       latent_channels=4      ,       )

         # TODO: address the non-deterministic text encoder (fails for save-load tests)
         # torch.manual_seed(0)
         # text_encoder_config = RobertaSeriesConfig(
         #     hidden_size=32,
         #     project_dim=32,
         #     intermediate_size=37,
         #     layer_norm_eps=1e-05,
         #     num_attention_heads=4,
         #     num_hidden_layers=5,
         #     vocab_size=5002,
         # )
         # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

         torch.manual_seed(0       )
         A_     :				Optional[int]			      =	CLIPTextConfig(
             bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       projection_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=5002      ,       )
         A_     :				Optional[Any]			      =	CLIPTextModel(a__       )

         A_     :				Union[str, Any]			      =	XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""       )
         A_     :				str			      =	77

         A_     :				Optional[Any]			      =	{
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=0       ):
         if str(a__       ).startswith("""mps"""       ):
            A_     :				Union[str, Any]			      =	torch.manual_seed(a__       )
         else:
            A_     :				Dict			      =	torch.Generator(device=a__       ).manual_seed(a__       )
         A_     :				Dict			      =	{
             """prompt""": """A painting of a squirrel eating a burger""",
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
         }
         return inputs
      def 		_lowerCamelCase      (		self       ):
         super().test_attention_slicing_forward_pass(expected_max_diff=3E-3       )
      def 		_lowerCamelCase      (		self       ):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

         A_     :				Union[str, Any]			      =	self.get_dummy_components()
         torch.manual_seed(0       )
         A_     :				List[str]			      =	RobertaSeriesConfig(
             hidden_size=32      ,       project_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       vocab_size=5002      ,       )
         # TODO: remove after fixing the non-deterministic text encoder
         A_     :				Dict			      =	RobertaSeriesModelWithTransformation(a__       )
         A_     :				Any			      =	text_encoder

         A_     :				Tuple			      =	AltDiffusionPipeline(**a__       )
         A_     :				int			      =	alt_pipe.to(a__       )
         alt_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Union[str, Any]			      =	self.get_dummy_inputs(a__       )
         A_     :				Tuple			      =	"""A photo of an astronaut"""
         A_     :				Dict			      =	alt_pipe(**a__       )
         A_     :				List[str]			      =	output.images
         A_     :				List[Any]			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         A_     :				Tuple			      =	np.array(
             [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]       )

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






      def 		_lowerCamelCase      (		self       ):
         A_     :				Tuple			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

         A_     :				Union[str, Any]			      =	self.get_dummy_components()
         A_     :				str			      =	PNDMScheduler(skip_prk_steps=a__       )
         torch.manual_seed(0       )
         A_     :				Any			      =	RobertaSeriesConfig(
             hidden_size=32      ,       project_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       vocab_size=5002      ,       )
         # TODO: remove after fixing the non-deterministic text encoder
         A_     :				int			      =	RobertaSeriesModelWithTransformation(a__       )
         A_     :				Any			      =	text_encoder
         A_     :				Any			      =	AltDiffusionPipeline(**a__       )
         A_     :				int			      =	alt_pipe.to(a__       )
         alt_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Union[str, Any]			      =	self.get_dummy_inputs(a__       )
         A_     :				Union[str, Any]			      =	alt_pipe(**a__       )
         A_     :				List[str]			      =	output.images
         A_     :				List[Any]			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         A_     :				List[Any]			      =	np.array(
             [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]       )

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






@slow
@require_torch_gpu
class    _UpperCAmelCase							(		unittest.TestCase     ):
      def 		_lowerCamelCase      (		self       ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
      def 		_lowerCamelCase      (		self       ):
         # make sure here that pndm scheduler skips prk
         A_     :				Any			      =	AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,       safety_checker=a__       )
         A_     :				Optional[int]			      =	alt_pipe.to(a__       )
         alt_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Tuple			      =	"""A painting of a squirrel eating a burger"""
         A_     :				Tuple			      =	torch.manual_seed(0       )
         A_     :				int			      =	alt_pipe([prompt]      ,       generator=a__      ,       guidance_scale=6.0      ,       num_inference_steps=20      ,       output_type="""np"""       )

         A_     :				Optional[Any]			      =	output.images

         A_     :				int			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         A_     :				str			      =	np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]       )

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






      def 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""      ,       subfolder="""scheduler"""       )

         A_     :				int			      =	AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,       scheduler=a__      ,       safety_checker=a__       )
         A_     :				Dict			      =	alt_pipe.to(a__       )
         alt_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Optional[int]			      =	"""A painting of a squirrel eating a burger"""
         A_     :				Any			      =	torch.manual_seed(0       )

         A_     :				List[str]			      =	alt_pipe([prompt]      ,       generator=a__      ,       num_inference_steps=2      ,       output_type="""numpy"""       )
         A_     :				List[Any]			      =	output.images

         A_     :				Tuple			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         A_     :				Any			      =	np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]       )

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






