


"""simple docstring"""





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor



class 		snake_case_:






     def __init__(	self  :					Tuple					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					str=3					,				UpperCamelCase_  :					Any=3_2					,				UpperCamelCase_  :					List[Any]=3					,				UpperCamelCase_  :					Tuple=1_0					,				UpperCamelCase_  :					Any=[1_0, 2_0, 3_0, 4_0]					,				UpperCamelCase_  :					List[Any]=[1, 1, 2, 1]					,				UpperCamelCase_  :					Optional[Any]=True					,				UpperCamelCase_  :					Dict=True					,				UpperCamelCase_  :					Union[str, Any]="relu"					,				UpperCamelCase_  :					Optional[Any]=3					,				UpperCamelCase_  :					Union[str, Any]=None					,				):
           lowerCAmelCase					:	Dict       =   parent
           lowerCAmelCase					:	Dict       =   batch_size
           lowerCAmelCase					:	Tuple       =   image_size
           lowerCAmelCase					:	List[Any]       =   num_channels
           lowerCAmelCase					:	int       =   embeddings_size
           lowerCAmelCase					:	int       =   hidden_sizes
           lowerCAmelCase					:	Tuple       =   depths
           lowerCAmelCase					:	List[str]       =   is_training
           lowerCAmelCase					:	Dict       =   use_labels
           lowerCAmelCase					:	List[Any]       =   hidden_act
           lowerCAmelCase					:	List[str]       =   num_labels
           lowerCAmelCase					:	int       =   scope
           lowerCAmelCase					:	Tuple       =   len(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	List[str]       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           lowerCAmelCase					:	int       =   None
           if self.use_labels:
                 lowerCAmelCase					:	Optional[Any]       =   ids_tensor([self.batch_size]					,				self.num_labels				)

           lowerCAmelCase					:	Union[str, Any]       =   self.get_config()

           return config, pixel_values, labels






     def      lowerCamelCase__     (	self  :					int				):
           return ResNetConfig(
               num_channels=self.num_channels					,				embeddings_size=self.embeddings_size					,				hidden_sizes=self.hidden_sizes					,				depths=self.depths					,				hidden_act=self.hidden_act					,				num_labels=self.num_labels					,				image_size=self.image_size					,				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	int       =   TFResNetModel(config=UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   model(UpperCamelCase_				)
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape					,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)					,				)






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	Any       =   self.num_labels
           lowerCAmelCase					:	Optional[int]       =   TFResNetForImageClassification(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[int]       =   model(UpperCamelCase_					,				labels=UpperCamelCase_				)
           self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	int       =   self.prepare_config_and_inputs()
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	List[Any]       =   config_and_inputs
           lowerCAmelCase					:	List[str]       =   {'''pixel_values''': pixel_values}
           return config, inputs_dict



@require_tf
class 		snake_case_(       a__							,					a__							,					unittest.TestCase			):
     __UpperCamelCase  =					(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     __UpperCamelCase  =					(
         {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     __UpperCamelCase  =					False
     __UpperCamelCase  =					False
     __UpperCamelCase  =					False
     __UpperCamelCase  =					False
     __UpperCamelCase  =					False






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Tuple       =   TFResNetModelTester(self				)
           lowerCAmelCase					:	str       =   ConfigTester(self					,				config_class=UpperCamelCase_					,				has_text_modality=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Dict				):
           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  :					List[Any]				):
           return






     @unittest.skip(reason='''ResNet does not use inputs_embeds'''				)
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           pass






     @unittest.skip(reason='''ResNet does not support input and output embeddings'''				)
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           pass






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCAmelCase					:	Any       =   model_class(UpperCamelCase_				)
                 lowerCAmelCase					:	Any       =   inspect.signature(model.call				)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCAmelCase					:	Any       =   [*signature.parameters.keys()]

                 lowerCAmelCase					:	Union[str, Any]       =   ['''pixel_values''']
                 self.assertListEqual(arg_names[:1]					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase					:	Dict       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					int				):
           def check_hidden_states_output(UpperCamelCase_  :					int					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					str				):
                 lowerCAmelCase					:	Optional[Any]       =   model_class(UpperCamelCase_				)
                 lowerCAmelCase					:	Any       =   model(**self._prepare_for_class(UpperCamelCase_					,				UpperCamelCase_				)				)

                 lowerCAmelCase					:	Union[str, Any]       =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 lowerCAmelCase					:	int       =   self.model_tester.num_stages
                 self.assertEqual(len(UpperCamelCase_				)					,				expected_num_stages + 1				)

                 # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:]				)					,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,				)

           lowerCAmelCase,      lowerCAmelCase					:	Any       =   self.model_tester.prepare_config_and_inputs_for_common()
           lowerCAmelCase					:	Any       =   ['''basic''', '''bottleneck''']
           for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                       lowerCAmelCase					:	Dict       =   layer_type
                       lowerCAmelCase					:	Optional[int]       =   True
                       check_hidden_states_output(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)

                       # check that output_hidden_states also work using config
                       del inputs_dict["output_hidden_states"]
                       lowerCAmelCase					:	int       =   True

                       check_hidden_states_output(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	List[str]       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_				)






     @slow
     def      lowerCamelCase__     (	self  :					List[Any]				):
           for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCAmelCase					:	Tuple       =   TFResNetModel.from_pretrained(UpperCamelCase_				)
                 self.assertIsNotNone(UpperCamelCase_				)



def      _snake_case						(						):
      lowerCAmelCase					:	Union[str, Any]       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
      return image






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






     @cached_property
     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           return (
               AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
               if is_vision_available()
               else None
           )






     @slow
     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	List[Any]       =   TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

           lowerCAmelCase					:	Optional[Any]       =   self.default_image_processor
           lowerCAmelCase					:	Any       =   prepare_img()
           lowerCAmelCase					:	Any       =   image_processor(images=UpperCamelCase_					,				return_tensors='''tf'''				)

           # forward pass
           lowerCAmelCase					:	Dict       =   model(**UpperCamelCase_				)

           # verify the logits
           lowerCAmelCase					:	str       =   tf.TensorShape((1, 1_0_0_0)				)
           self.assertEqual(outputs.logits.shape					,				UpperCamelCase_				)

           lowerCAmelCase					:	str       =   tf.constant([-11.1_069, -9.7_877, -8.3_777]				)

           self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()					,				UpperCamelCase_					,				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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class 		snake_case_(       a__			):
     __UpperCamelCase  =					'''philschmid/bart-large-cnn-samsum'''
     __UpperCamelCase  =					(
         '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
         '''and returns a summary of the text.'''
     )
     __UpperCamelCase  =					'''summarizer'''
     __UpperCamelCase  =					AutoTokenizer
     __UpperCamelCase  =					AutoModelForSeqaSeqLM

     __UpperCamelCase  =					['''text''']
     __UpperCamelCase  =					['''text''']






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					int				):
           return self.pre_processor(UpperCamelCase_					,				return_tensors='''pt'''					,				truncation=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str				):
           return self.model.generate(**UpperCamelCase_				)[0]






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Tuple				):
           return self.pre_processor.decode(UpperCamelCase_					,				skip_special_tokens=UpperCamelCase_					,				clean_up_tokenization_spaces=UpperCamelCase_				)


