






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow




@require_torch
class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						"""ylacombe/bark-small"""
            __snake_case			:Optional[Any]          =						tempfile.mkdtemp()
            __snake_case			:Any          =						"""en_speaker_1"""
            __snake_case			:Optional[Any]          =						"""This is a test string"""
            __snake_case			:List[str]          =						"""speaker_embeddings_path.json"""
            __snake_case			:Optional[Any]          =						"""speaker_embeddings"""



       def 					__lowercase						(							self			,						**a__    ) ->      List[str]:






            '''simple docstring'''





            return AutoTokenizer.from_pretrained(self.checkpoint			,						**a__    )



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            shutil.rmtree(self.tmpdirname    )



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.get_tokenizer()

            __snake_case			:str          =						BarkProcessor(tokenizer=a__    )

            processor.save_pretrained(self.tmpdirname    )
            __snake_case			:Tuple          =						BarkProcessor.from_pretrained(self.tmpdirname    )

            self.assertEqual(processor.tokenizer.get_vocab()			,						tokenizer.get_vocab()    )



       @slow
       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Any          =						BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint			,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,						)
            processor.save_pretrained(
                self.tmpdirname			,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,						speaker_embeddings_directory=self.speaker_embeddings_directory			,						)

            __snake_case			:List[str]          =						self.get_tokenizer(bos_token="""(BOS)"""			,						eos_token="""(EOS)"""    )

            __snake_case			:Optional[int]          =						BarkProcessor.from_pretrained(
                self.tmpdirname			,						self.speaker_embeddings_dict_path			,						bos_token="""(BOS)"""			,						eos_token="""(EOS)"""			,						)

            self.assertEqual(processor.tokenizer.get_vocab()			,						tokenizer_add_kwargs.get_vocab()    )



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






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint			,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,						)

            __snake_case			:Dict          =						35
            __snake_case			:str          =						2
            __snake_case			:Tuple          =						8

            __snake_case			:Dict          =						{
                """semantic_prompt""": np.ones(a__    ),
                """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)    ),
                """fine_prompt""": np.ones((nb_codebooks_total, seq_len)    ),
            }

            # test providing already loaded voice_preset
            __snake_case			:Optional[int]          =						processor(text=self.input_string			,						voice_preset=a__    )

            __snake_case			:int          =						inputs["""history_prompt"""]
            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()			,						processed_voice_preset.get(a__			,						np.array([]    )    ).tolist()    )

            # test loading voice preset from npz file
            __snake_case			:List[Any]          =						os.path.join(self.tmpdirname			,						"""file.npz"""    )
            np.savez(a__			,						**a__    )
            __snake_case			:str          =						processor(text=self.input_string			,						voice_preset=a__    )
            __snake_case			:List[Any]          =						inputs["""history_prompt"""]

            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()			,						processed_voice_preset.get(a__			,						np.array([]    )    ).tolist()    )

            # test loading voice preset from the hub
            __snake_case			:Any          =						processor(text=self.input_string			,						voice_preset=self.voice_preset    )



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self.get_tokenizer()

            __snake_case			:Any          =						BarkProcessor(tokenizer=a__    )

            __snake_case			:Union[str, Any]          =						processor(text=self.input_string    )

            __snake_case			:str          =						tokenizer(
                self.input_string			,						padding="""max_length"""			,						max_length=2_56			,						add_special_tokens=a__			,						return_attention_mask=a__			,						return_token_type_ids=a__			,						)

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]			,						encoded_processor[key].squeeze().tolist()    )










# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= "dandelin/vilt-b32-finetuned-vqa"
       lowerCamelCase							:				List[str] 				= (
           "This is a tool that answers a question about an image. It takes an input named `image` which should be the "
           "image containing the information, as well as a `question` which should be the question in English. It "
           "returns a text that is the answer to the question."
       )
       lowerCamelCase							:				Optional[Any] 				= "image_qa"
       lowerCamelCase							:				str 				= AutoProcessor
       lowerCamelCase							:				Union[str, Any] 				= AutoModelForVisualQuestionAnswering

       lowerCamelCase							:				Any 				= ["image", "text"]
       lowerCamelCase							:				Dict 				= ["text"]



       def __init__(							self			,						*a__			,						**a__    ) ->      Any:






            '''simple docstring'''





            requires_backends(self			,						["""vision"""]    )
            super().__init__(*a__			,						**a__    )



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      int:






            '''simple docstring'''





            return self.pre_processor(a__			,						a__			,						return_tensors="""pt"""    )



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






            '''simple docstring'''





            with torch.no_grad():
                 return self.model(**a__    ).logits



       def 					__lowercase						(							self			,						a__    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:str          =						outputs.argmax(-1    ).item()
            return self.model.config.idalabel[idx]


