




# 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__   ( snake_case__				):
      _a							:   List[Any] =   """naver-clova-ix/donut-base-finetuned-docvqa"""
      _a							:   Tuple =   (
          """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."""
      )
      _a							:   Union[str, Any] =   """document_qa"""
      _a							:   str =   AutoProcessor
      _a							:   Union[str, Any] =   VisionEncoderDecoderModel

      _a							:   str =   ["""image""", """text"""]
      _a							:   Optional[Any] =   ["""text"""]






      def __init__(       self		,      *_A		,      **_A      ):




           """simple docstring"""



           if not is_vision_available():
                raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool."      )

           super().__init__(*lowercase_		,      **lowercase_      )






      def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




           """simple docstring"""



           __lowerCAmelCase           =	"<s_docvqa><s_question>{user_input}</s_question><s_answer>"
           __lowerCAmelCase           =	task_prompt.replace("{user_input}"		,      lowercase_      )
           __lowerCAmelCase           =	self.pre_processor.tokenizer(
               lowercase_		,      add_special_tokens=lowercase_		,      return_tensors="pt"      ).input_ids
           __lowerCAmelCase           =	self.pre_processor(lowercase_		,      return_tensors="pt"      ).pixel_values

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






      def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




           """simple docstring"""



           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=lowercase_		,      pad_token_id=self.pre_processor.tokenizer.pad_token_id		,      eos_token_id=self.pre_processor.tokenizer.eos_token_id		,      use_cache=lowercase_		,      num_beams=1		,      bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]		,      return_dict_in_generate=lowercase_		,      ).sequences






      def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




           """simple docstring"""



           __lowerCAmelCase           =	self.pre_processor.batch_decode(lowercase_      )[0]
           __lowerCAmelCase           =	sequence.replace(self.pre_processor.tokenizer.eos_token		,      ""      )
           __lowerCAmelCase           =	sequence.replace(self.pre_processor.tokenizer.pad_token		,      ""      )
           __lowerCAmelCase           =	re.sub(R"<.*?>"		,      ""		,      lowercase_		,      count=1      ).strip()  # remove first task start token
           __lowerCAmelCase           =	self.pre_processor.tokenajson(lowercase_      )

           return sequence["answer"]










import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image
else:
					class       a__   :






											@staticmethod
											def     __SCREAMING_SNAKE_CASE(       *_A		,      **_A      ):




																"""simple docstring"""



																pass







def      _a				(						SCREAMING_SNAKE_CASE_							:    Image					):
					__lowerCAmelCase           =	hashlib.mda(image.tobytes()					)
					return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       a__   ( unittest.TestCase				):
						_a							:   Tuple =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	DepthEstimationPipeline(model=_A		,      image_processor=_A      )
											return depth_estimator, [
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											]






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
											self.assertEqual({"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )}		,      _A      )
											import datasets

											__lowerCAmelCase           =	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"		,      "image"		,      split="test"      )
											__lowerCAmelCase           =	depth_estimator(
											    [
											        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      ),
											        "http://images.cocodataset.org/val2017/000000039769.jpg",
											        # RGBA
											        dataset[0]["file"],
											        # LA
											        dataset[1]["file"],
											        # L
											        dataset[2]["file"],
											    ]      )
											self.assertEqual(
											    [
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											    ]		,      _A		,      )






						@require_tf
						@unittest.skip("Depth estimation is not implemented in TF"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@slow
						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"Intel/dpt-large"
											__lowerCAmelCase           =	pipeline("depth-estimation"		,      model=_A      )
											__lowerCAmelCase           =	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"      )
											__lowerCAmelCase           =	hashimage(outputs["depth"]      )

											# This seems flaky.
											# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
											self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()      )		,      29.3_04      )
											self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()      )		,      2.6_62      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"      )




