





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCAmelCase_  							=				False






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   pass






@nightly
@require_torch_gpu
class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       int      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       int      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=2,	output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(__magic_name__							)
           UpperCamelCase__							:       Union[str, Any]      =   VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[Any]      =   generator.manual_seed(0							)
       UpperCamelCase__							:       str      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=2,	output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       str      =   VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion''',	torch_dtype=torch.floataa							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[int]      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       Optional[int]      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=50,	output_type='''numpy'''							).images

       UpperCamelCase__							:       int      =   image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__							:       int      =   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]							)

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








import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
  if is_torch_available():
    import torch
  if is_tf_available():
    import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_  							=				{
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'Salesforce/codegen-350M-mono': (
            'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase_  							=				{
    'Salesforce/codegen-350M-mono': 2048,
}






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Union[str, Any]     	=	VOCAB_FILES_NAMES
   a		:							List[str]     	=	PRETRAINED_VOCAB_FILES_MAP
   a		:							Optional[int]     	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a		:							List[Any]     	=	["input_ids", "attention_mask"]
   a		:							Any     	=	CodeGenTokenizer




   def __init__(					self,	__magic_name__=None,	__magic_name__=None,	__magic_name__=None,	__magic_name__="<|endoftext|>",	__magic_name__="<|endoftext|>",	__magic_name__="<|endoftext|>",	__magic_name__=False,	**__magic_name__,	)				->   Optional[int]:






       """simple docstring"""
       super().__init__(
           __magic_name__,	__magic_name__,	tokenizer_file=__magic_name__,	unk_token=__magic_name__,	bos_token=__magic_name__,	eos_token=__magic_name__,	add_prefix_space=__magic_name__,	**__magic_name__,	)

       if kwargs.pop('''add_bos_token''',	__magic_name__							):
           UpperCamelCase__							:       List[Any]      =   kwargs.pop('''name_or_path''',	''''''							)
           raise ValueError(
               '''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
               '''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
               f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
               f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
               '''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
               ''' so that the fast tokenizer works correctly.'''							)

       UpperCamelCase__							:       Dict      =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
       if pre_tok_state.get('''add_prefix_space''',	__magic_name__							) != add_prefix_space:
           UpperCamelCase__							:       List[str]      =   getattr(__magic_name__,	pre_tok_state.pop('''type'''							)							)
           UpperCamelCase__							:       int      =   add_prefix_space
           UpperCamelCase__							:       List[str]      =   pre_tok_class(**__magic_name__							)

       UpperCamelCase__							:       List[Any]      =   add_prefix_space




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   kwargs.get('''is_split_into_words''',	__magic_name__							)
       assert self.add_prefix_space or not is_split_into_words, (
           f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   kwargs.get('''is_split_into_words''',	__magic_name__							)

       assert self.add_prefix_space or not is_split_into_words, (
           f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   Tuple[str]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self._tokenizer.model.save(__magic_name__,	name=__magic_name__							)
       return tuple(__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = False,	__magic_name__ = None,	__magic_name__ = None,	**__magic_name__,	)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   super().decode(
           token_ids=__magic_name__,	skip_special_tokens=__magic_name__,	clean_up_tokenization_spaces=__magic_name__,	**__magic_name__,	)

       if truncate_before_pattern is not None and len(__magic_name__							) > 0:
           UpperCamelCase__							:       str      =   self.truncate(__magic_name__,	__magic_name__							)

       return decoded_text




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   List[str]:






       """simple docstring"""
       def find_re(__magic_name__,	__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Dict      =   pattern.search(__magic_name__,	__magic_name__							)
           return m.start() if m else -1

       UpperCamelCase__							:       Union[str, Any]      =   [re.compile(__magic_name__,	re.MULTILINE							) for pattern in truncate_before_pattern]

       UpperCamelCase__							:       List[Any]      =   list(re.finditer('''^print''',	__magic_name__,	re.MULTILINE							)							)

       if len(__magic_name__							) > 1:
           UpperCamelCase__							:       Tuple      =   completion[: prints[1].start()]

       UpperCamelCase__							:       Optional[int]      =   list(re.finditer('''^def''',	__magic_name__,	re.MULTILINE							)							)

       if len(__magic_name__							) > 1:
           UpperCamelCase__							:       Optional[Any]      =   completion[: defs[1].start()]

       UpperCamelCase__							:       str      =   0

       UpperCamelCase__							:       Tuple      =   [
           pos for pos in [find_re(__magic_name__,	__magic_name__,	__magic_name__							) for terminal in terminals] if pos != -1
       ]

       if len(__magic_name__							) > 0:
           return completion[: min(__magic_name__							)]
       else:
           return completion

