




"""simple docstring"""




import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

 from transformers import ViTMSNForImageClassification, ViTMSNModel
 from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor






class      UpperCAmelCase_  :



      def __init__(					self					:     Dict					,     A					:     List[str]					,     A					:     Optional[int]=1_3					,     A					:     List[str]=3_0					,     A					:     int=2					,     A					:     Tuple=3					,     A					:     Optional[int]=True					,     A					:     Optional[int]=True					,     A					:     Optional[Any]=3_2					,     A					:     Tuple=5					,     A					:     Tuple=4					,     A					:     Tuple=3_7					,     A					:     Optional[int]="gelu"					,     A					:     Optional[int]=0.1					,     A					:     Optional[int]=0.1					,     A					:     Tuple=1_0					,     A					:     List[str]=0.02					,     A					:     Union[str, Any]=None					,     ):
           _UpperCAmelCase      :     str        =   parent
           _UpperCAmelCase      :     str        =   batch_size
           _UpperCAmelCase      :     Dict        =   image_size
           _UpperCAmelCase      :     int        =   patch_size
           _UpperCAmelCase      :     Any        =   num_channels
           _UpperCAmelCase      :     Optional[Any]        =   is_training
           _UpperCAmelCase      :     str        =   use_labels
           _UpperCAmelCase      :     Union[str, Any]        =   hidden_size
           _UpperCAmelCase      :     Optional[int]        =   num_hidden_layers
           _UpperCAmelCase      :     Optional[Any]        =   num_attention_heads
           _UpperCAmelCase      :     Optional[int]        =   intermediate_size
           _UpperCAmelCase      :     Optional[int]        =   hidden_act
           _UpperCAmelCase      :     Dict        =   hidden_dropout_prob
           _UpperCAmelCase      :     Dict        =   attention_probs_dropout_prob
           _UpperCAmelCase      :     Tuple        =   type_sequence_label_size
           _UpperCAmelCase      :     Optional[int]        =   initializer_range
           _UpperCAmelCase      :     List[str]        =   scope

           # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           _UpperCAmelCase      :     str        =   (image_size // patch_size) ** 2
           _UpperCAmelCase      :     Tuple        =   num_patches + 1



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     Union[str, Any]        =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

           _UpperCAmelCase      :     int        =   None
           if self.use_labels:
                _UpperCAmelCase      :     List[Any]        =   ids_tensor([self.batch_size]					,     self.type_sequence_label_size						)

           _UpperCAmelCase      :     Optional[int]        =   self.get_config()

           return config, pixel_values, labels



      def 						snake_case_ (					self					:     Optional[int]						):
           return ViTMSNConfig(
               image_size=self.image_size					,     patch_size=self.patch_size					,     num_channels=self.num_channels					,     hidden_size=self.hidden_size					,     num_hidden_layers=self.num_hidden_layers					,     num_attention_heads=self.num_attention_heads					,     intermediate_size=self.intermediate_size					,     hidden_act=self.hidden_act					,     hidden_dropout_prob=self.hidden_dropout_prob					,     attention_probs_dropout_prob=self.attention_probs_dropout_prob					,     initializer_range=self.initializer_range					,     )



      def 						snake_case_ (					self					:     int					,     A					:     int					,     A					:     Any					,     A					:     List[Any]						):
           _UpperCAmelCase      :     List[str]        =   ViTMSNModel(config=__lowerCAmelCase						)
           model.to(__lowerCAmelCase						)
           model.eval()
           _UpperCAmelCase      :     Tuple        =   model(__lowerCAmelCase						)
           self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)						)



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     str					,     A					:     str					,     A					:     List[Any]						):
           _UpperCAmelCase      :     int        =   self.type_sequence_label_size
           _UpperCAmelCase      :     Optional[int]        =   ViTMSNForImageClassification(__lowerCAmelCase						)
           model.to(__lowerCAmelCase						)
           model.eval()
           _UpperCAmelCase      :     Optional[Any]        =   model(__lowerCAmelCase					,     labels=__lowerCAmelCase						)
           print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"						)
           print("Labels: {labels}"						)
           self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)						)

           # test greyscale images
           _UpperCAmelCase      :     Union[str, Any]        =   1
           _UpperCAmelCase      :     int        =   ViTMSNForImageClassification(__lowerCAmelCase						)
           model.to(__lowerCAmelCase						)
           model.eval()

           _UpperCAmelCase      :     List[Any]        =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
           _UpperCAmelCase      :     List[Any]        =   model(__lowerCAmelCase						)
           self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.prepare_config_and_inputs()
           _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   config_and_inputs
           _UpperCAmelCase      :     List[str]        =   {"pixel_values": pixel_values}
           return config, inputs_dict







@require_torch
class      UpperCAmelCase_  (    lowerCAmelCase_	,      lowerCAmelCase_	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE						:       int								=			(
          {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
          if is_torch_available()
          else {}
      )

      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			False
      __SCREAMING_SNAKE_CASE						:       int								=			False
      __SCREAMING_SNAKE_CASE						:       List[str]								=			False
      __SCREAMING_SNAKE_CASE						:       int								=			False



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Optional[int]        =   ViTMSNModelTester(self						)
           _UpperCAmelCase      :     Any        =   ConfigTester(self					,     config_class=__lowerCAmelCase					,     has_text_modality=__lowerCAmelCase					,     hidden_size=3_7						)



      def 						snake_case_ (					self					:     Any						):
           self.config_tester.run_common_tests()



      @unittest.skip(reason="ViTMSN does not use inputs_embeds"						)
      def 						snake_case_ (					self					:     List[Any]						):
           pass



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Any        =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                _UpperCAmelCase      :     Union[str, Any]        =   model_class(__lowerCAmelCase						)
                self.assertIsInstance(model.get_input_embeddings()					,     (nn.Module)						)
                _UpperCAmelCase      :     List[Any]        =   model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(__lowerCAmelCase					,     nn.Linear						)						)



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                _UpperCAmelCase      :     Dict        =   model_class(__lowerCAmelCase						)
                _UpperCAmelCase      :     Union[str, Any]        =   inspect.signature(model.forward						)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                _UpperCAmelCase      :     Any        =   [*signature.parameters.keys()]

                _UpperCAmelCase      :     int        =   ["pixel_values"]
                self.assertListEqual(arg_names[:1]					,     __lowerCAmelCase						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     List[Any]        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCAmelCase						)



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     Any        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase						)



      @slow
      def 						snake_case_ (					self					:     Optional[Any]						):
           for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase      :     int        =   ViTMSNModel.from_pretrained(__lowerCAmelCase						)
                self.assertIsNotNone(__lowerCAmelCase						)







def 						__snake_case						(				)  ->       int:





     '''simple docstring'''

     _UpperCAmelCase      :     Any        =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
     return image






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



      @cached_property
      def 						snake_case_ (					self					:     str						):
           return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"						) if is_vision_available() else None



      @slow
      def 						snake_case_ (					self					:     str						):
           torch.manual_seed(2						)
           _UpperCAmelCase      :     Tuple        =   ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"						).to(__lowerCAmelCase						)

           _UpperCAmelCase      :     str        =   self.default_image_processor
           _UpperCAmelCase      :     str        =   prepare_img()
           _UpperCAmelCase      :     List[str]        =   image_processor(images=__lowerCAmelCase					,     return_tensors="pt"						).to(__lowerCAmelCase						)

           # forward pass
           with torch.no_grad():
                _UpperCAmelCase      :     List[Any]        =   model(**__lowerCAmelCase						)

           # verify the logits
           _UpperCAmelCase      :     int        =   torch.Size((1, 1_0_0_0)						)
           self.assertEqual(outputs.logits.shape					,     __lowerCAmelCase						)

           _UpperCAmelCase      :     List[Any]        =   torch.tensor([-0.0_803, -0.4_454, -0.2_375]						).to(__lowerCAmelCase						)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]					,     __lowerCAmelCase					,     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 ..utils import cached_file


# docstyle-ignore
_lowerCAmelCase    : Any							=							"\nHuman: <<task>>\n\nAssistant: "


_lowerCAmelCase    : str							=							"huggingface-tools/default-prompts"
_lowerCAmelCase    : Union[str, Any]							=							{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[int]						,  SCREAMING_SNAKE_CASE__     :						int="run"    )  ->       int:





     '''simple docstring'''

     if prompt_or_repo_id is None:
          _UpperCAmelCase      :     Optional[int]        =   DEFAULT_PROMPTS_REPO

     # prompt is considered a repo ID when it does not contain any kind of space
     if re.search("\\s"						,  SCREAMING_SNAKE_CASE__    ) is not None:
          return prompt_or_repo_id

     _UpperCAmelCase      :     Dict        =   cached_file(
         SCREAMING_SNAKE_CASE__						,  PROMPT_FILES[mode]						,  repo_type="dataset"						,  user_agent={"agent": agent_name}    )
     with open(SCREAMING_SNAKE_CASE__						,  "r"						,  encoding="utf-8"    ) as f:
          return f.read()
