




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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

_lowerCamelCase			:	Union[str, Any]							 =     logging.get_logger(__name__)


_lowerCamelCase			:	Union[str, Any]							 =     {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_lowerCamelCase			:	List[str]							 =     {
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

_lowerCamelCase			:	Dict							 =     {"""facebook/blenderbot-3B""": 128}


class        UpperCamelCase_					(				UpperCAmelCase__  ):

				'''simple docstring'''






				UpperCAmelCase__													=   VOCAB_FILES_NAMES
				UpperCAmelCase__													=   PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase__													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase__													=   ['''input_ids''', '''attention_mask''']
				UpperCAmelCase__													=   BlenderbotTokenizer


				def __init__(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			str=None      ,				UpperCAmelCase__   :			Tuple=None      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			Dict="replace"      ,				UpperCAmelCase__   :			int="<s>"      ,				UpperCAmelCase__   :			str="</s>"      ,				UpperCAmelCase__   :			Any="</s>"      ,				UpperCAmelCase__   :			List[Any]="<s>"      ,				UpperCAmelCase__   :			Any="<unk>"      ,				UpperCAmelCase__   :			Optional[Any]="<pad>"      ,				UpperCAmelCase__   :			Any="<mask>"      ,				UpperCAmelCase__   :			Any=False      ,				UpperCAmelCase__   :			str=True      ,				**UpperCAmelCase__   :			Optional[int]      ,				)		->Optional[Any]:





											'''simple docstring'''

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

											A__   				=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
											if pre_tok_state.get('''add_prefix_space'''      ,				UpperCAmelCase__) != add_prefix_space:
																		A__   				=					getattr(UpperCAmelCase__      ,				pre_tok_state.pop('''type'''))
																		A__   				=					add_prefix_space
																		A__   				=					pre_tok_class(**UpperCAmelCase__)

											A__   				=					add_prefix_space

											A__   				=					'''post_processor'''
											A__   				=					getattr(self.backend_tokenizer      ,				UpperCAmelCase__      ,				UpperCAmelCase__)
											if tokenizer_component_instance:
																		A__   				=					json.loads(tokenizer_component_instance.__getstate__())

																		# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																		if "sep" in state:
																									A__   				=					tuple(state['''sep'''])
																		if "cls" in state:
																									A__   				=					tuple(state['''cls'''])

																		A__   				=					False

																		if state.get('''add_prefix_space'''      ,				UpperCAmelCase__) != add_prefix_space:
																									A__   				=					add_prefix_space
																									A__   				=					True

																		if state.get('''trim_offsets'''      ,				UpperCAmelCase__) != trim_offsets:
																									A__   				=					trim_offsets
																									A__   				=					True

																		if changes_to_apply:
																									A__   				=					getattr(UpperCAmelCase__      ,				state.pop('''type'''))
																									A__   				=					component_class(**UpperCAmelCase__)
																									setattr(self.backend_tokenizer      ,				UpperCAmelCase__      ,				UpperCAmelCase__)


				@property
				# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
				def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->str:





											'''simple docstring'''

											if self._mask_token is None:
																		if self.verbose:
																									logger.error('''Using mask_token, but it is not set yet.''')
																		return None
											return str(self._mask_token)


				@mask_token.setter
				def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			List[Any])		->Any:





											'''simple docstring'''

											A__   				=					AddedToken(UpperCAmelCase__      ,				lstrip=UpperCAmelCase__      ,				rstrip=UpperCAmelCase__) if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__) else value
											A__   				=					value


				def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				*UpperCAmelCase__   :			List[Any]      ,				**UpperCAmelCase__   :			Any)		->BatchEncoding:





											'''simple docstring'''

											A__   				=					kwargs.get('''is_split_into_words'''      ,				UpperCAmelCase__)
											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(*UpperCAmelCase__      ,				**UpperCAmelCase__)


				def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				*UpperCAmelCase__   :			Optional[Any]      ,				**UpperCAmelCase__   :			Optional[int])		->BatchEncoding:





											'''simple docstring'''

											A__   				=					kwargs.get('''is_split_into_words'''      ,				UpperCAmelCase__)

											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(*UpperCAmelCase__      ,				**UpperCAmelCase__)


				def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[str] = None)		->Tuple[str]:





											'''simple docstring'''

											A__   				=					self._tokenizer.model.save(UpperCAmelCase__      ,				name=UpperCAmelCase__)
											return tuple(UpperCAmelCase__)


				def  SCREAMING_SNAKE_CASE							(    self   :			Any      ,				UpperCAmelCase__   :			List[int]      ,				UpperCAmelCase__   :			Optional[List[int]] = None)		->List[int]:





											'''simple docstring'''

											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  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			List[int]      ,				UpperCAmelCase__   :			Optional[List[int]] = None)		->int:





											'''simple docstring'''

											return token_ids_a + [self.eos_token_id]





				def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			"Conversation")		->List[int]:





											'''simple docstring'''

											A__   				=					[]
											for is_user, text in conversation.iter_texts():
																		if is_user:
																									# We need to space prefix as it's being done within blenderbot
																									inputs.append(''' ''' + text)
																		else:
																									# Generated responses should contain them already.
																									inputs.append(UpperCAmelCase__)

											A__   				=					'''  '''.join(UpperCAmelCase__)
											A__   				=					self.encode(UpperCAmelCase__)
											if len(UpperCAmelCase__) > self.model_max_length:
																		A__   				=					input_ids[-self.model_max_length :]
																		logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""")
											return input_ids








import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   IFInpaintingPipeline
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    UpperCAmelCase__													=   PipelineTesterMixin.required_optional_params - {'''latents'''}


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Dict:





           '''simple docstring'''

           return self._get_dummy_components()


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			int=0)		->int:





           '''simple docstring'''

           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__   				=					torch.manual_seed(UpperCAmelCase__)
           else:
                  A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)

           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)

           A__   				=					{
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''image''': image,
               '''mask_image''': mask_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  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->List[Any]:





           '''simple docstring'''

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->int:





           '''simple docstring'''

           self._test_save_load_optional_components()


    @unittest.skipIf(torch_device != '''cuda'''      ,				reason='''float16 requires CUDA''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[str]:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->List[Any]:





           '''simple docstring'''

           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)


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





           '''simple docstring'''

           self._test_save_load_local()





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Union[str, Any]:





           '''simple docstring'''

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


