import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							def        A__  ( self							):
												__lowerCAmelCase					=							tempfile.mkdtemp()

												__lowerCAmelCase					=							BlipImageProcessor()
												__lowerCAmelCase					=							GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'							)

												__lowerCAmelCase					=							BlipaProcessor(A_					,						A_							)

												processor.save_pretrained(self.tmpdirname							)




							def        A__  ( self					,						**_A							):
												return AutoProcessor.from_pretrained(self.tmpdirname					,						**A_							).tokenizer




							def        A__  ( self					,						**_A							):
												return AutoProcessor.from_pretrained(self.tmpdirname					,						**A_							).image_processor




							def        A__  ( self							):
												shutil.rmtree(self.tmpdirname							)




							def        A__  ( self							):
												__lowerCAmelCase					=							[np.random.randint(2_5_5					,						size=(3, 3_0, 4_0_0)					,						dtype=np.uinta							)]

												__lowerCAmelCase					=							[Image.fromarray(np.moveaxis(A_					,						0					,						-1							)							) for x in image_inputs]

												return image_inputs




							def        A__  ( self							):
												__lowerCAmelCase					=							BlipaProcessor(tokenizer=self.get_tokenizer()					,						image_processor=self.get_image_processor()							)
												processor.save_pretrained(self.tmpdirname							)

												__lowerCAmelCase					=							self.get_tokenizer(bos_token='(BOS)'					,						eos_token='(EOS)'							)
												__lowerCAmelCase					=							self.get_image_processor(do_normalize=A_					,						padding_value=1.0							)

												__lowerCAmelCase					=							BlipaProcessor.from_pretrained(
												    self.tmpdirname					,						bos_token='(BOS)'					,						eos_token='(EOS)'					,						do_normalize=A_					,						padding_value=1.0							)

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

												self.assertEqual(processor.image_processor.to_json_string()					,						image_processor_add_kwargs.to_json_string()							)
												self.assertIsInstance(processor.image_processor					,						A_							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.get_image_processor()
												__lowerCAmelCase					=							self.get_tokenizer()

												__lowerCAmelCase					=							BlipaProcessor(tokenizer=A_					,						image_processor=A_							)

												__lowerCAmelCase					=							self.prepare_image_inputs()

												__lowerCAmelCase					=							image_processor(A_					,						return_tensors='np'							)
												__lowerCAmelCase					=							processor(images=A_					,						return_tensors='np'							)

												for key in input_feat_extract.keys():
																	self.assertAlmostEqual(input_feat_extract[key].sum()					,						input_processor[key].sum()					,						delta=1e-2							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.get_image_processor()
												__lowerCAmelCase					=							self.get_tokenizer()

												__lowerCAmelCase					=							BlipaProcessor(tokenizer=A_					,						image_processor=A_							)

												__lowerCAmelCase					=							'lower newer'

												__lowerCAmelCase					=							processor(text=A_							)

												__lowerCAmelCase					=							tokenizer(A_					,						return_token_type_ids=A_							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							self.get_image_processor()
												__lowerCAmelCase					=							self.get_tokenizer()

												__lowerCAmelCase					=							BlipaProcessor(tokenizer=A_					,						image_processor=A_							)

												__lowerCAmelCase					=							'lower newer'
												__lowerCAmelCase					=							self.prepare_image_inputs()

												__lowerCAmelCase					=							processor(text=A_					,						images=A_							)

												self.assertListEqual(list(inputs.keys()							)					,						['pixel_values', 'input_ids', 'attention_mask']							)

												# test if it raises when no input is passed
												with pytest.raises(A_							):
																	processor()




							def        A__  ( self							):
												__lowerCAmelCase					=							self.get_image_processor()
												__lowerCAmelCase					=							self.get_tokenizer()

												__lowerCAmelCase					=							BlipaProcessor(tokenizer=A_					,						image_processor=A_							)

												__lowerCAmelCase					=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												__lowerCAmelCase					=							processor.batch_decode(A_							)
												__lowerCAmelCase					=							tokenizer.batch_decode(A_							)

												self.assertListEqual(A_					,						A_							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.get_image_processor()
												__lowerCAmelCase					=							self.get_tokenizer()

												__lowerCAmelCase					=							BlipaProcessor(tokenizer=A_					,						image_processor=A_							)

												__lowerCAmelCase					=							'lower newer'
												__lowerCAmelCase					=							self.prepare_image_inputs()

												__lowerCAmelCase					=							processor(text=A_					,						images=A_							)

												# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
												self.assertListEqual(list(inputs.keys()							)					,						['pixel_values', 'input_ids', 'attention_mask']							)









# 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.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       ="""facebook/bart-large-mnli"""
							__UpperCAmelCase       =(
							    """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
							    """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
							    """It returns the most likely label in the list of provided `labels` for the input text."""
							)
							__UpperCAmelCase       ="""text_classifier"""
							__UpperCAmelCase       =AutoTokenizer
							__UpperCAmelCase       =AutoModelForSequenceClassification

							__UpperCAmelCase       =["""text""", ["""text"""]]
							__UpperCAmelCase       =["""text"""]




							def        A__  ( self							):
												super().setup()
												__lowerCAmelCase					=							self.model.config
												__lowerCAmelCase					=							-1
												for idx, label in config.idalabel.items():
																	if label.lower().startswith('entail'							):
																						__lowerCAmelCase					=							int(_A							)
												if self.entailment_id == -1:
																	raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.'							)




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							labels
												return self.pre_processor(
												    [text] * len(_A							)					,						[F"""This example is {label}""" for label in labels]					,						return_tensors='pt'					,						padding='max_length'					,						)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							outputs.logits
												__lowerCAmelCase					=							torch.argmax(logits[:, 2]							).item()
												return self._labels[label_id]






