'''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 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=3	,   __lowerCAmelCase=32	,   __lowerCAmelCase=3	,   __lowerCAmelCase=10	,   __lowerCAmelCase=[10, 20, 30, 40]	,   __lowerCAmelCase=[1, 1, 2, 1]	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase="relu"	,   __lowerCAmelCase=3	,   __lowerCAmelCase=None	,   )  ->  Optional[int]:
        lowercase__				:    List[Any]  =					parent
        lowercase__				:    List[Any]  =					batch_size
        lowercase__				:    int  =					image_size
        lowercase__				:    List[str]  =					num_channels
        lowercase__				:    Optional[Any]  =					embeddings_size
        lowercase__				:    str  =					hidden_sizes
        lowercase__				:    str  =					depths
        lowercase__				:    Optional[int]  =					is_training
        lowercase__				:    List[Any]  =					use_labels
        lowercase__				:    str  =					hidden_act
        lowercase__				:    str  =					num_labels
        lowercase__				:    Union[str, Any]  =					scope
        lowercase__				:    List[Any]  =					len(__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    int  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

        lowercase__				:    Dict  =					self.get_config()

        return config, pixel_values, labels

       def _lowerCAmelCase(						self )  ->  List[Any]:
        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	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  str:
        lowercase__				:    List[str]  =					TFResNetModel(config=__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					model(__lowerCAmelCase )
        # 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 // 32, self.image_size // 32)	,   )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  List[str]:
        lowercase__				:    int  =					self.num_labels
        lowercase__				:    Tuple  =					TFResNetForImageClassification(__lowerCAmelCase )
        lowercase__				:    Tuple  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels) )


       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__				:    Tuple  =					self.prepare_config_and_inputs()
        lowercase__ ,							lowercase__ ,							lowercase__				:    Optional[Any]  =					config_and_inputs
        lowercase__				:    List[Any]  =					{'''pixel_values''': pixel_values}
        return config, inputs_dict



@require_tf
class 			UpperCAmelCase  (						a__    ,						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
       SCREAMING_SNAKE_CASE							=    (
           {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
           if is_tf_available()
           else {}
       )

       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    Any  =					TFResNetModelTester(self )
        lowercase__				:    Dict  =					ConfigTester(self	,   config_class=__lowerCAmelCase	,   has_text_modality=__lowerCAmelCase )

       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 )  ->  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 )  ->  Tuple:
        pass

       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__ ,							lowercase__				:    Dict  =					self.model_tester.prepare_config_and_inputs_for_common()

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

         lowercase__				:    Any  =					['''pixel_values''']
         self.assertListEqual(arg_names[:1]	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  int:
        lowercase__				:    Dict  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  List[str]:
        def check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase ):
         lowercase__				:    Optional[int]  =					model_class(__lowerCAmelCase )
         lowercase__				:    Tuple  =					model(**self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase ) )

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

         lowercase__				:    Union[str, Any]  =					self.model_tester.num_stages
         self.assertEqual(len(__lowerCAmelCase )	,   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]	,   )

        lowercase__ ,							lowercase__				:    Optional[Any]  =					self.model_tester.prepare_config_and_inputs_for_common()
        lowercase__				:    Union[str, Any]  =					['''basic''', '''bottleneck''']
        for model_class in self.all_model_classes:
         for layer_type in layers_type:
          lowercase__				:    str  =					layer_type
          lowercase__				:    Optional[int]  =					True
          check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )

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

          check_hidden_states_output(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  List[Any]:
        lowercase__				:    Optional[int]  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )


       @slow
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase__				:    str  =					TFResNetModel.from_pretrained(__lowerCAmelCase )
         self.assertIsNotNone(__lowerCAmelCase )


def 			__UpperCamelCase       (   ):
 lowercase__				:    int  =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
 return image





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





       '''simple docstring'''



       @cached_property
       def _lowerCAmelCase(						self )  ->  List[str]:
        return (
            AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
            if is_vision_available()
            else None
        )


       @slow
       def _lowerCAmelCase(						self )  ->  List[Any]:
        lowercase__				:    Union[str, Any]  =					TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

        lowercase__				:    Any  =					self.default_image_processor
        lowercase__				:    List[Any]  =					prepare_img()
        lowercase__				:    Optional[int]  =					image_processor(images=__lowerCAmelCase	,   return_tensors='''tf''' )

        # forward pass
        lowercase__				:    Optional[Any]  =					model(**__lowerCAmelCase )

        # verify the logits
        lowercase__				:    Optional[Any]  =					tf.TensorShape((1, 1000) )
        self.assertEqual(outputs.logits.shape	,   __lowerCAmelCase )

        lowercase__				:    Any  =					tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7] )

        self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()	,   __lowerCAmelCase	,   atol=1E-4 ) )


'''simple docstring'''

import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    CTRLTokenizer
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Dict:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase__				:    Optional[Any]  =					['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
        lowercase__				:    str  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )
        lowercase__				:    Tuple  =					['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
        lowercase__				:    Optional[Any]  =					{'''unk_token''': '''<unk>'''}

        lowercase__				:    Dict  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Dict  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  List[str]:
        kwargs.update(self.special_tokens_map )
        return CTRLTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  List[str]:
        lowercase__				:    List[str]  =					'''adapt react readapt apt'''
        lowercase__				:    Union[str, Any]  =					'''adapt react readapt apt'''
        return input_text, output_text


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Union[str, Any]  =					CTRLTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Optional[Any]  =					'''adapt react readapt apt'''
        lowercase__				:    Dict  =					'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    int  =					tokens + [tokenizer.unk_token]

        lowercase__				:    List[str]  =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

