

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     LevitForImageClassification,
     LevitForImageClassificationWithTeacher,
     LevitModel,
 )
 from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor



class 		a_       (							SCREAMING_SNAKE_CASE_     ):



      """simple docstring"""







      def        _lowerCAmelCase     (      self     :							List[Any]				):
           SCREAMING_SNAKE_CASE      						=self.config_class(**self.inputs_dict				)
           self.parent.assertTrue(hasattr(a__			,'hidden_sizes'				)				)
           self.parent.assertTrue(hasattr(a__			,'num_attention_heads'				)				)







class 		a_       :



      """simple docstring"""







      def __init__(      self     :							Any			,snake_case     :							Tuple			,snake_case     :							List[Any]=13			,snake_case     :							Any=64			,snake_case     :							List[Any]=3			,snake_case     :							int=3			,snake_case     :							Union[str, Any]=2			,snake_case     :							Dict=1			,snake_case     :							Union[str, Any]=16			,snake_case     :							Dict=[128, 256, 384]			,snake_case     :							int=[4, 6, 8]			,snake_case     :							Dict=[2, 3, 4]			,snake_case     :							int=[16, 16, 16]			,snake_case     :							Union[str, Any]=0			,snake_case     :							Tuple=[2, 2, 2]			,snake_case     :							Any=[2, 2, 2]			,snake_case     :							Union[str, Any]=0.02			,snake_case     :							Optional[Any]=True			,snake_case     :							List[Any]=True			,snake_case     :							Dict=2			,):
           SCREAMING_SNAKE_CASE      						=parent
           SCREAMING_SNAKE_CASE      						=batch_size
           SCREAMING_SNAKE_CASE      						=image_size
           SCREAMING_SNAKE_CASE      						=num_channels
           SCREAMING_SNAKE_CASE      						=kernel_size
           SCREAMING_SNAKE_CASE      						=stride
           SCREAMING_SNAKE_CASE      						=padding
           SCREAMING_SNAKE_CASE      						=hidden_sizes
           SCREAMING_SNAKE_CASE      						=num_attention_heads
           SCREAMING_SNAKE_CASE      						=depths
           SCREAMING_SNAKE_CASE      						=key_dim
           SCREAMING_SNAKE_CASE      						=drop_path_rate
           SCREAMING_SNAKE_CASE      						=patch_size
           SCREAMING_SNAKE_CASE      						=attention_ratio
           SCREAMING_SNAKE_CASE      						=mlp_ratio
           SCREAMING_SNAKE_CASE      						=initializer_range
           SCREAMING_SNAKE_CASE      						=[
               ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
               ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
           ]
           SCREAMING_SNAKE_CASE      						=is_training
           SCREAMING_SNAKE_CASE      						=use_labels
           SCREAMING_SNAKE_CASE      						=num_labels
           SCREAMING_SNAKE_CASE      						=initializer_range
      def        _lowerCAmelCase     (      self     :							List[Any]				):
           SCREAMING_SNAKE_CASE      						=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           SCREAMING_SNAKE_CASE      						=None
           if self.use_labels:
                SCREAMING_SNAKE_CASE      						=ids_tensor([self.batch_size]			,self.num_labels				)

           SCREAMING_SNAKE_CASE      						=self.get_config()
           return config, pixel_values, labels
      def        _lowerCAmelCase     (      self     :							Dict				):
           return LevitConfig(
               image_size=self.image_size			,num_channels=self.num_channels			,kernel_size=self.kernel_size			,stride=self.stride			,padding=self.padding			,patch_size=self.patch_size			,hidden_sizes=self.hidden_sizes			,num_attention_heads=self.num_attention_heads			,depths=self.depths			,key_dim=self.key_dim			,drop_path_rate=self.drop_path_rate			,mlp_ratio=self.mlp_ratio			,attention_ratio=self.attention_ratio			,initializer_range=self.initializer_range			,down_ops=self.down_ops			,)
      def        _lowerCAmelCase     (      self     :							int			,snake_case     :							Union[str, Any]			,snake_case     :							Optional[int]			,snake_case     :							Optional[int]				):
           SCREAMING_SNAKE_CASE      						=LevitModel(config=a__				)
           model.to(a__				)
           model.eval()
           SCREAMING_SNAKE_CASE      						=model(a__				)
           SCREAMING_SNAKE_CASE      						=(self.image_size, self.image_size)
           SCREAMING_SNAKE_CASE      						=image_size[0], image_size[1]
           for _ in range(4				):
                SCREAMING_SNAKE_CASE      						=floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
                SCREAMING_SNAKE_CASE      						=floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
           self.parent.assertEqual(
               result.last_hidden_state.shape			,(self.batch_size, ceil(height / 4				) * ceil(width / 4				), self.hidden_sizes[-1])			,)
      def        _lowerCAmelCase     (      self     :							Any			,snake_case     :							List[str]			,snake_case     :							Optional[Any]			,snake_case     :							str				):
           SCREAMING_SNAKE_CASE      						=self.num_labels
           SCREAMING_SNAKE_CASE      						=LevitForImageClassification(a__				)
           model.to(a__				)
           model.eval()
           SCREAMING_SNAKE_CASE      						=model(a__			,labels=a__				)
           self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)				)


      def        _lowerCAmelCase     (      self     :							Tuple				):
           SCREAMING_SNAKE_CASE      						=self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE      						=config_and_inputs
           SCREAMING_SNAKE_CASE      						={"""pixel_values""": pixel_values}
           return config, inputs_dict







@require_torch
class 		a_       (							SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			unittest.TestCase     ):



      """simple docstring"""







      __UpperCAmelCase					=      (
          (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
          if is_torch_available()
          else ()
      )
      __UpperCAmelCase					=      (
          {
              "feature-extraction": LevitModel,
              "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
          }
          if is_torch_available()
          else {}
      )

      __UpperCAmelCase					=      False
      __UpperCAmelCase					=      False
      __UpperCAmelCase					=      False
      __UpperCAmelCase					=      False
      __UpperCAmelCase					=      False
      def        _lowerCAmelCase     (      self     :							Optional[Any]				):
           SCREAMING_SNAKE_CASE      						=LevitModelTester(self				)
           SCREAMING_SNAKE_CASE      						=ConfigTester(self			,config_class=a__			,has_text_modality=a__			,hidden_size=37				)
      def        _lowerCAmelCase     (      self     :							List[Any]				):
           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()
      def        _lowerCAmelCase     (      self     :							Optional[int]				):
           return
      @unittest.skip(reason='Levit does not use inputs_embeds'				)
      def        _lowerCAmelCase     (      self     :							Dict				):
           pass
      @unittest.skip(reason='Levit does not support input and output embeddings'				)
      def        _lowerCAmelCase     (      self     :							Optional[Any]				):
           pass
      @unittest.skip(reason='Levit does not output attentions'				)
      def        _lowerCAmelCase     (      self     :							Dict				):
           pass
      def        _lowerCAmelCase     (      self     :							List[Any]				):
           SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE      						=model_class(a__				)
                SCREAMING_SNAKE_CASE      						=inspect.signature(model.forward				)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                SCREAMING_SNAKE_CASE      						=[*signature.parameters.keys()]

                SCREAMING_SNAKE_CASE      						=["""pixel_values"""]
                self.assertListEqual(arg_names[:1]			,a__				)
      def        _lowerCAmelCase     (      self     :							Union[str, Any]				):
           def check_hidden_states_output(snake_case     :							Any			,snake_case     :							Optional[Any]			,snake_case     :							List[str]				):
                SCREAMING_SNAKE_CASE      						=model_class(a__				)
                model.to(a__				)
                model.eval()

                with torch.no_grad():
                     SCREAMING_SNAKE_CASE      						=model(**self._prepare_for_class(a__			,a__				)				)

                SCREAMING_SNAKE_CASE      						=outputs.hidden_states

                SCREAMING_SNAKE_CASE      						=len(self.model_tester.depths				) + 1
                self.assertEqual(len(a__				)			,a__				)

                SCREAMING_SNAKE_CASE      						=(self.model_tester.image_size, self.model_tester.image_size)
                SCREAMING_SNAKE_CASE      						=image_size[0], image_size[1]
                for _ in range(4				):
                     SCREAMING_SNAKE_CASE      						=floor(
                         (
                             (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                             / self.model_tester.stride
                         )
                         + 1				)
                     SCREAMING_SNAKE_CASE      						=floor(
                         (
                             (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                             / self.model_tester.stride
                         )
                         + 1				)
                # verify the first hidden states (first block)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]				)			,[
                        height * width,
                        self.model_tester.hidden_sizes[0],
                    ]			,)

           SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE      						=True
                check_hidden_states_output(a__			,a__			,a__				)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                SCREAMING_SNAKE_CASE      						=True

                check_hidden_states_output(a__			,a__			,a__				)
      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
      def        _lowerCAmelCase     (      self     :							Optional[Any]				):
           pass
      def        _lowerCAmelCase     (      self     :							Any			,snake_case     :							Tuple			,snake_case     :							Union[str, Any]			,snake_case     :							Any=False				):
           SCREAMING_SNAKE_CASE      						=super()._prepare_for_class(a__			,a__			,return_labels=a__				)

           if return_labels:
                if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                     del inputs_dict["labels"]

           return inputs_dict
      def        _lowerCAmelCase     (      self     :							Any				):
           SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*a__				)
      def        _lowerCAmelCase     (      self     :							Any				):
           SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*a__				)
      def        _lowerCAmelCase     (      self     :							List[Any]				):
           if not self.model_tester.is_training:
                return

           SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE      						=True

           for model_class in self.all_model_classes:
                # LevitForImageClassificationWithTeacher supports inference-only
                if (
                    model_class in get_values(a__				)
                    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                ):
                     continue
                SCREAMING_SNAKE_CASE      						=model_class(a__				)
                model.to(a__				)
                model.train()
                SCREAMING_SNAKE_CASE      						=self._prepare_for_class(a__			,a__			,return_labels=a__				)
                SCREAMING_SNAKE_CASE      						=model(**a__				).loss
                loss.backward()
      def        _lowerCAmelCase     (      self     :							Optional[Any]				):
           SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
                return

           SCREAMING_SNAKE_CASE      						=False
           SCREAMING_SNAKE_CASE      						=True

           for model_class in self.all_model_classes:
                if model_class in get_values(a__				) or not model_class.supports_gradient_checkpointing:
                     continue
                # LevitForImageClassificationWithTeacher supports inference-only
                if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                     continue
                SCREAMING_SNAKE_CASE      						=model_class(a__				)
                model.gradient_checkpointing_enable()
                model.to(a__				)
                model.train()
                SCREAMING_SNAKE_CASE      						=self._prepare_for_class(a__			,a__			,return_labels=a__				)
                SCREAMING_SNAKE_CASE      						=model(**a__				).loss
                loss.backward()
      def        _lowerCAmelCase     (      self     :							Optional[Any]				):
           SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs_for_common()

           SCREAMING_SNAKE_CASE      						=[
               {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
               {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
               {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
           ]

           for model_class in self.all_model_classes:
                if (
                    model_class
                    not in [
                        *get_values(a__				),
                    ]
                    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                ):
                     continue

                for problem_type in problem_types:
                     with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}'				):
                          SCREAMING_SNAKE_CASE      						=problem_type["""title"""]
                          SCREAMING_SNAKE_CASE      						=problem_type["""num_labels"""]

                          SCREAMING_SNAKE_CASE      						=model_class(a__				)
                          model.to(a__				)
                          model.train()

                          SCREAMING_SNAKE_CASE      						=self._prepare_for_class(a__			,a__			,return_labels=a__				)

                          if problem_type["num_labels"] > 1:
                               SCREAMING_SNAKE_CASE      						=inputs["""labels"""].unsqueeze(1				).repeat(1			,problem_type['num_labels']				)

                          SCREAMING_SNAKE_CASE      						=inputs["""labels"""].to(problem_type['dtype']				)

                          # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                          # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                          # they have the same size." which is a symptom something in wrong for the regression problem.
                          # See https://github.com/huggingface/transformers/issues/11780
                          with warnings.catch_warnings(record=a__				) as warning_list:
                               SCREAMING_SNAKE_CASE      						=model(**a__				).loss
                          for w in warning_list:
                               if "Using a target size that is different to the input size" in str(w.message				):
                                    raise ValueError(
                                        f'Something is going wrong in the regression problem: intercepted {w.message}'				)

                          loss.backward()


      @slow
      def        _lowerCAmelCase     (      self     :							int				):
           for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                SCREAMING_SNAKE_CASE      						=LevitModel.from_pretrained(a__				)
                self.assertIsNotNone(a__				)







def 			snake_case__				(   ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE      						=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
     return image



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



      """simple docstring"""







      @cached_property
      def        _lowerCAmelCase     (      self     :							int				):
           return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)


      @slow
      def        _lowerCAmelCase     (      self     :							Dict				):
           SCREAMING_SNAKE_CASE      						=LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(
               a__				)

           SCREAMING_SNAKE_CASE      						=self.default_image_processor
           SCREAMING_SNAKE_CASE      						=prepare_img()
           SCREAMING_SNAKE_CASE      						=image_processor(images=a__			,return_tensors='pt'				).to(a__				)

           # forward pass
           with torch.no_grad():
                SCREAMING_SNAKE_CASE      						=model(**a__				)

           # verify the logits
           SCREAMING_SNAKE_CASE      						=torch.Size((1, 1000)				)
           self.assertEqual(outputs.logits.shape			,a__				)

           SCREAMING_SNAKE_CASE      						=torch.tensor([1.0_448, -0.3_745, -1.8_317]				).to(a__				)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]			,a__			,atol=1e-4				)				)


"""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"]




