


'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 			UpperCAmelCase			(							_a		,      _a		,      unittest.TestCase):
 _lowerCamelCase			:       List[Any]            =       IFSuperResolutionPipeline
 _lowerCamelCase			:       List[Any]            =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
 _lowerCamelCase			:       Optional[int]            =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 _lowerCamelCase			:       int            =       PipelineTesterMixin.required_optional_params - {"""latents"""}

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    return self._get_superresolution_dummy_components()

 def 		lowercase_    (			self			:						int,       a_			:						Optional[int],       a_			:						Dict=0       ):





    """simple docstring"""



    if str(__lowerCamelCase       ).startswith("mps"       ):
       UpperCamelCase__			    =    torch.manual_seed(__lowerCamelCase       )
    else:
       UpperCamelCase__			    =    torch.Generator(device=__lowerCamelCase       ).manual_seed(__lowerCamelCase       )

    UpperCamelCase__			    =    floats_tensor((1, 3, 32, 32),       rng=random.Random(__lowerCamelCase       )       ).to(__lowerCamelCase       )

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

    return inputs

 @unittest.skipIf(
     torch_device != "cuda" or not is_xformers_available(),       reason="XFormers attention is only available with CUDA and `xformers` installed",       )
 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    self._test_save_load_optional_components()

 @unittest.skipIf(torch_device != "cuda",       reason="float16 requires CUDA"       )
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    super().test_save_load_floataa(expected_max_diff=1e-1       )

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    self._test_save_load_local()

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    self._test_inference_batch_single_identical(
        expected_max_diff=1e-2,       )







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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
	UpperCAmelCase_					:		str           = None


UpperCAmelCase_					:		int           = logging.get_logger(__name__)

UpperCAmelCase_					:		List[str]           = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_					:		Any           = {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase_					:		Any           = {
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Dict	      =    VOCAB_FILES_NAMES
	snake_case__				:	Optional[int]	      =    PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:	str	      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:	int	      =    ["""input_ids""", """attention_mask"""]
	snake_case__				:	Dict	      =    XLMRobertaTokenizer





	def __init__(						self							:   List[str]					,    __lowerCamelCase							:   List[Any]=None					,    __lowerCamelCase							:   int=None					,    __lowerCamelCase							:   Dict="<s>"					,    __lowerCamelCase							:   Optional[int]="</s>"					,    __lowerCamelCase							:   int="</s>"					,    __lowerCamelCase							:   Dict="<s>"					,    __lowerCamelCase							:   Optional[Any]="<unk>"					,    __lowerCamelCase							:   Union[str, Any]="<pad>"					,    __lowerCamelCase							:   Any="<mask>"					,    **__lowerCamelCase							:   List[str]					,    ):
		# Mask token behave like a normal word, i.e. include the space before it
		UpperCamelCase	:int		=      AddedToken(__lowerCamelCase					,    lstrip=__lowerCamelCase					,    rstrip=__lowerCamelCase      ) if isinstance(__lowerCamelCase					,    __lowerCamelCase      ) else mask_token

		super().__init__(
		    __lowerCamelCase					,    tokenizer_file=__lowerCamelCase					,    bos_token=__lowerCamelCase					,    eos_token=__lowerCamelCase					,    sep_token=__lowerCamelCase					,    cls_token=__lowerCamelCase					,    unk_token=__lowerCamelCase					,    pad_token=__lowerCamelCase					,    mask_token=__lowerCamelCase					,    **__lowerCamelCase					,    )

		UpperCamelCase	:Optional[int]		=      vocab_file
		UpperCamelCase	:List[Any]		=      False if not self.vocab_file else True





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):

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





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):
		UpperCamelCase	:str		=      [self.sep_token_id]
		UpperCamelCase	:int		=      [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 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Optional[str] = None      ):
		if not self.can_save_slow_tokenizer:
			raise ValueError(
			    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
			    """tokenizer."""      )

		if not os.path.isdir(__lowerCamelCase      ):
			logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""      )
			return
		UpperCamelCase	:Union[str, Any]		=      os.path.join(
		    __lowerCamelCase					,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

		if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCamelCase      ):
			copyfile(self.vocab_file					,    __lowerCamelCase      )

		return (out_vocab_file,)
