

import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
 import torch

if is_tf_available():
 import tensorflow as tf



@require_vision
@require_torchvision
class 		a_       (							unittest.TestCase     ):



      """simple docstring"""







      def        _lowerCAmelCase     (      self     :							List[str]				):
           SCREAMING_SNAKE_CASE      						=tempfile.mkdtemp()
           SCREAMING_SNAKE_CASE      						=SamImageProcessor()
           SCREAMING_SNAKE_CASE      						=SamProcessor(a__				)
           processor.save_pretrained(self.tmpdirname				)
      def        _lowerCAmelCase     (      self     :							int			,**snake_case     :							str				):
           return AutoProcessor.from_pretrained(self.tmpdirname			,**a__				).image_processor
      def        _lowerCAmelCase     (      self     :							List[Any]				):
           shutil.rmtree(self.tmpdirname				)
      def        _lowerCAmelCase     (      self     :							str				):
           SCREAMING_SNAKE_CASE      						=[np.random.randint(255			,size=(3, 30, 400)			,dtype=np.uinta				)]

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

           return image_inputs
      def        _lowerCAmelCase     (      self     :							Tuple				):
           SCREAMING_SNAKE_CASE      						=SamProcessor(image_processor=self.get_image_processor()				)
           processor.save_pretrained(self.tmpdirname				)

           SCREAMING_SNAKE_CASE      						=self.get_image_processor(do_normalize=a__			,padding_value=1.0				)

           SCREAMING_SNAKE_CASE      						=SamProcessor.from_pretrained(self.tmpdirname			,do_normalize=a__			,padding_value=1.0				)

           self.assertEqual(processor.image_processor.to_json_string()			,image_processor_add_kwargs.to_json_string()				)
           self.assertIsInstance(processor.image_processor			,a__				)
      def        _lowerCAmelCase     (      self     :							Union[str, Any]				):
           SCREAMING_SNAKE_CASE      						=self.get_image_processor()

           SCREAMING_SNAKE_CASE      						=SamProcessor(image_processor=a__				)

           SCREAMING_SNAKE_CASE      						=self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE      						=image_processor(a__			,return_tensors='np'				)
           SCREAMING_SNAKE_CASE      						=processor(images=a__			,return_tensors='np'				)

           input_feat_extract.pop('original_sizes'				)  # pop original_sizes as it is popped in the processor
           input_feat_extract.pop('reshaped_input_sizes'				)  # pop original_sizes as it is popped in the processor

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


      @require_torch
      def        _lowerCAmelCase     (      self     :							List[str]				):
           SCREAMING_SNAKE_CASE      						=self.get_image_processor()

           SCREAMING_SNAKE_CASE      						=SamProcessor(image_processor=a__				)
           SCREAMING_SNAKE_CASE      						=[torch.ones((1, 3, 5, 5)				)]

           SCREAMING_SNAKE_CASE      						=[[1764, 2646]]

           SCREAMING_SNAKE_CASE      						=[[683, 1024]]
           SCREAMING_SNAKE_CASE      						=processor.post_process_masks(a__			,a__			,a__				)
           self.assertEqual(masks[0].shape			,(1, 3, 1764, 2646)				)

           SCREAMING_SNAKE_CASE      						=processor.post_process_masks(
               a__			,torch.tensor(a__				)			,torch.tensor(a__				)				)
           self.assertEqual(masks[0].shape			,(1, 3, 1764, 2646)				)

           # should also work with np
           SCREAMING_SNAKE_CASE      						=[np.ones((1, 3, 5, 5)				)]
           SCREAMING_SNAKE_CASE      						=processor.post_process_masks(a__			,np.array(a__				)			,np.array(a__				)				)

           self.assertEqual(masks[0].shape			,(1, 3, 1764, 2646)				)

           SCREAMING_SNAKE_CASE      						=[[1, 0], [0, 1]]
           with self.assertRaises(a__				):
                SCREAMING_SNAKE_CASE      						=processor.post_process_masks(a__			,np.array(a__				)			,np.array(a__				)				)







@require_vision
@require_tf
class 		a_       (							unittest.TestCase     ):



      """simple docstring"""







      def        _lowerCAmelCase     (      self     :							List[Any]				):
           SCREAMING_SNAKE_CASE      						=tempfile.mkdtemp()
           SCREAMING_SNAKE_CASE      						=SamImageProcessor()
           SCREAMING_SNAKE_CASE      						=SamProcessor(a__				)
           processor.save_pretrained(self.tmpdirname				)
      def        _lowerCAmelCase     (      self     :							List[str]			,**snake_case     :							List[Any]				):
           return AutoProcessor.from_pretrained(self.tmpdirname			,**a__				).image_processor
      def        _lowerCAmelCase     (      self     :							Union[str, Any]				):
           shutil.rmtree(self.tmpdirname				)
      def        _lowerCAmelCase     (      self     :							Optional[int]				):
           SCREAMING_SNAKE_CASE      						=[np.random.randint(255			,size=(3, 30, 400)			,dtype=np.uinta				)]

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

           return image_inputs
      def        _lowerCAmelCase     (      self     :							Dict				):
           SCREAMING_SNAKE_CASE      						=SamProcessor(image_processor=self.get_image_processor()				)
           processor.save_pretrained(self.tmpdirname				)

           SCREAMING_SNAKE_CASE      						=self.get_image_processor(do_normalize=a__			,padding_value=1.0				)

           SCREAMING_SNAKE_CASE      						=SamProcessor.from_pretrained(self.tmpdirname			,do_normalize=a__			,padding_value=1.0				)

           self.assertEqual(processor.image_processor.to_json_string()			,image_processor_add_kwargs.to_json_string()				)
           self.assertIsInstance(processor.image_processor			,a__				)
      def        _lowerCAmelCase     (      self     :							int				):
           SCREAMING_SNAKE_CASE      						=self.get_image_processor()

           SCREAMING_SNAKE_CASE      						=SamProcessor(image_processor=a__				)

           SCREAMING_SNAKE_CASE      						=self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE      						=image_processor(a__			,return_tensors='np'				)
           SCREAMING_SNAKE_CASE      						=processor(images=a__			,return_tensors='np'				)

           input_feat_extract.pop('original_sizes'				)  # pop original_sizes as it is popped in the processor
           input_feat_extract.pop('reshaped_input_sizes'				)  # pop reshaped_input_sizes as it is popped in the processor

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


      @require_tf
      def        _lowerCAmelCase     (      self     :							Optional[int]				):
           SCREAMING_SNAKE_CASE      						=self.get_image_processor()

           SCREAMING_SNAKE_CASE      						=SamProcessor(image_processor=a__				)
           SCREAMING_SNAKE_CASE      						=[tf.ones((1, 3, 5, 5)				)]

           SCREAMING_SNAKE_CASE      						=[[1764, 2646]]

           SCREAMING_SNAKE_CASE      						=[[683, 1024]]
           SCREAMING_SNAKE_CASE      						=processor.post_process_masks(a__			,a__			,a__			,return_tensors='tf'				)
           self.assertEqual(masks[0].shape			,(1, 3, 1764, 2646)				)

           SCREAMING_SNAKE_CASE      						=processor.post_process_masks(
               a__			,tf.convert_to_tensor(a__				)			,tf.convert_to_tensor(a__				)			,return_tensors='tf'			,)
           self.assertEqual(masks[0].shape			,(1, 3, 1764, 2646)				)

           # should also work with np
           SCREAMING_SNAKE_CASE      						=[np.ones((1, 3, 5, 5)				)]
           SCREAMING_SNAKE_CASE      						=processor.post_process_masks(
               a__			,np.array(a__				)			,np.array(a__				)			,return_tensors='tf'				)

           self.assertEqual(masks[0].shape			,(1, 3, 1764, 2646)				)

           SCREAMING_SNAKE_CASE      						=[[1, 0], [0, 1]]
           with self.assertRaises(tf.errors.InvalidArgumentError				):
                SCREAMING_SNAKE_CASE      						=processor.post_process_masks(
                    a__			,np.array(a__				)			,np.array(a__				)			,return_tensors='tf'				)







@require_vision
@require_torchvision
class 		a_       (							unittest.TestCase     ):



      """simple docstring"""







      def        _lowerCAmelCase     (      self     :							int				):
           SCREAMING_SNAKE_CASE      						=tempfile.mkdtemp()
           SCREAMING_SNAKE_CASE      						=SamImageProcessor()
           SCREAMING_SNAKE_CASE      						=SamProcessor(a__				)
           processor.save_pretrained(self.tmpdirname				)
      def        _lowerCAmelCase     (      self     :							int			,**snake_case     :							Any				):
           return AutoProcessor.from_pretrained(self.tmpdirname			,**a__				).image_processor
      def        _lowerCAmelCase     (      self     :							Union[str, Any]				):
           shutil.rmtree(self.tmpdirname				)
      def        _lowerCAmelCase     (      self     :							str				):
           SCREAMING_SNAKE_CASE      						=[np.random.randint(255			,size=(3, 30, 400)			,dtype=np.uinta				)]

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

           return image_inputs
      @is_pt_tf_cross_test
      def        _lowerCAmelCase     (      self     :							int				):
           SCREAMING_SNAKE_CASE      						=self.get_image_processor()

           SCREAMING_SNAKE_CASE      						=SamProcessor(image_processor=a__				)
           SCREAMING_SNAKE_CASE      						=np.random.randint(0			,2			,size=(1, 3, 5, 5)				).astype(np.floataa				)
           SCREAMING_SNAKE_CASE      						=[tf.convert_to_tensor(a__				)]
           SCREAMING_SNAKE_CASE      						=[torch.tensor(a__				)]

           SCREAMING_SNAKE_CASE      						=[[1764, 2646]]

           SCREAMING_SNAKE_CASE      						=[[683, 1024]]
           SCREAMING_SNAKE_CASE      						=processor.post_process_masks(
               a__			,a__			,a__			,return_tensors='tf'				)
           SCREAMING_SNAKE_CASE      						=processor.post_process_masks(
               a__			,a__			,a__			,return_tensors='pt'				)

           self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()				)				)


      @is_pt_tf_cross_test
      def        _lowerCAmelCase     (      self     :							str				):
           SCREAMING_SNAKE_CASE      						=self.get_image_processor()

           SCREAMING_SNAKE_CASE      						=SamProcessor(image_processor=a__				)

           SCREAMING_SNAKE_CASE      						=self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE      						=image_processor(a__			,return_tensors='pt'				)["""pixel_values"""].numpy()
           SCREAMING_SNAKE_CASE      						=processor(images=a__			,return_tensors='pt'				)["""pixel_values"""].numpy()

           SCREAMING_SNAKE_CASE      						=image_processor(a__			,return_tensors='tf'				)["""pixel_values"""].numpy()
           SCREAMING_SNAKE_CASE      						=processor(images=a__			,return_tensors='tf'				)["""pixel_values"""].numpy()

           self.assertTrue(np.allclose(a__			,a__				)				)
           self.assertTrue(np.allclose(a__			,a__				)				)
           self.assertTrue(np.allclose(a__			,a__				)				)


"""simple docstring"""




# 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 re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
     from PIL import Image
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[Any]							=		"naver-clova-ix/donut-base-finetuned-docvqa"
     _UpperCamelCase							:			Dict							=		(
         "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
         "should be the document containing the information, as well as a `question` that is the question about the "
         "document. It returns a text that contains the answer to the question."
     )
     _UpperCamelCase							:			Optional[int]							=		"document_qa"
     _UpperCamelCase							:			Any							=		AutoProcessor
     _UpperCamelCase							:			Union[str, Any]							=		VisionEncoderDecoderModel

     _UpperCamelCase							:			Union[str, Any]							=		["image", "text"]
     _UpperCamelCase							:			List[str]							=		["text"]






     def __init__(		self				,     *a__				,     **a__  ):
       if not is_vision_available():
         raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""  )

       super().__init__(*a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
       _lowerCAmelCase						:       Dict  			=     task_prompt.replace("""{user_input}"""				,     a__  )
       _lowerCAmelCase						:       str  			=     self.pre_processor.tokenizer(
           a__				,     add_special_tokens=a__				,     return_tensors="""pt"""  ).input_ids
       _lowerCAmelCase						:       Dict  			=     self.pre_processor(a__				,     return_tensors="""pt"""  ).pixel_values

       return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}






     def     __A  (		self				,     a__  ):
       return self.model.generate(
           inputs["""pixel_values"""].to(self.device  )				,     decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device  )				,     max_length=self.model.decoder.config.max_position_embeddings				,     early_stopping=a__				,     pad_token_id=self.pre_processor.tokenizer.pad_token_id				,     eos_token_id=self.pre_processor.tokenizer.eos_token_id				,     use_cache=a__				,     num_beams=1				,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]				,     return_dict_in_generate=a__				,     ).sequences






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Tuple  			=     self.pre_processor.batch_decode(a__  )[0]
       _lowerCAmelCase						:       int  			=     sequence.replace(self.pre_processor.tokenizer.eos_token				,     """"""  )
       _lowerCAmelCase						:       List[str]  			=     sequence.replace(self.pre_processor.tokenizer.pad_token				,     """"""  )
       _lowerCAmelCase						:       List[str]  			=     re.sub(r"""<.*?>"""				,     """"""				,     a__				,     count=1  ).strip()  # remove first task start token
       _lowerCAmelCase						:       List[str]  			=     self.pre_processor.tokenajson(a__  )

       return sequence["answer"]




