





import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 	UpperCamelCase__				(   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Tuple				=    RobertaTokenizer
     __snake_case					:			Dict				=    RobertaTokenizerFast
     __snake_case					:			Dict				=    True
     __snake_case					:			Optional[int]				=    {"cls_token": "<s>"}






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[Any]:




           '''simple docstring'''


           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           SCREAMING_SNAKE_CASE	=		[
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """<unk>""",
           ]
           SCREAMING_SNAKE_CASE	=		dict(zip(lowerCamelCase__		,range(len(lowerCamelCase__ ) ) ) )
           SCREAMING_SNAKE_CASE	=		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           SCREAMING_SNAKE_CASE	=		{"""unk_token""": """<unk>"""}

           SCREAMING_SNAKE_CASE	=		os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES["""vocab_file"""] )
           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,**lowerCamelCase__		:					int )  ->	str:




           '''simple docstring'''


           kwargs.update(self.special_tokens_map )
           return self.tokenizer_class.from_pretrained(self.tmpdirname		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,**lowerCamelCase__		:					Optional[Any] )  ->	Dict:




           '''simple docstring'''


           kwargs.update(self.special_tokens_map )
           return RobertaTokenizerFast.from_pretrained(self.tmpdirname		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					Any )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""lower newer"""
           SCREAMING_SNAKE_CASE	=		"""lower newer"""
           return input_text, output_text






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.tokenizer_class(self.vocab_file		,self.merges_file		,**self.special_tokens_map )
           SCREAMING_SNAKE_CASE	=		"""lower newer"""
           SCREAMING_SNAKE_CASE	=		["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
           SCREAMING_SNAKE_CASE	=		tokenizer.tokenize(lowerCamelCase__ )  # , add_prefix_space=True)
           self.assertListEqual(lowerCamelCase__		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tokens + [tokenizer.unk_token]
           SCREAMING_SNAKE_CASE	=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ )		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_tokenizer()

           self.assertListEqual(tokenizer.encode("""Hello world!"""		,add_special_tokens=lowerCamelCase__ )		,[0, 31414, 232, 328, 2] )
           self.assertListEqual(
               tokenizer.encode("""Hello world! cécé herlolip 418"""		,add_special_tokens=lowerCamelCase__ )		,[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]		,)






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.tokenizer_class.from_pretrained("""roberta-base""" )

           SCREAMING_SNAKE_CASE	=		tokenizer.encode("""sequence builders"""		,add_special_tokens=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		tokenizer.encode("""multi-sequence build"""		,add_special_tokens=lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tokenizer.encode(
               """sequence builders"""		,add_special_tokens=lowerCamelCase__		,add_prefix_space=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		tokenizer.encode(
               """sequence builders"""		,"""multi-sequence build"""		,add_special_tokens=lowerCamelCase__		,add_prefix_space=lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		tokenizer.build_inputs_with_special_tokens(lowerCamelCase__		,lowerCamelCase__ )

           assert encoded_sentence == encoded_text_from_decode
           assert encoded_pair == encoded_pair_from_decode






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.get_tokenizer()

           SCREAMING_SNAKE_CASE	=		"""Encode this sequence."""
           SCREAMING_SNAKE_CASE	=		tokenizer.byte_encoder[""" """.encode("""utf-8""" )[0]]

           # Testing encoder arguments
           SCREAMING_SNAKE_CASE	=		tokenizer.encode(lowerCamelCase__		,add_special_tokens=lowerCamelCase__		,add_prefix_space=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		tokenizer.convert_ids_to_tokens(encoded[0] )[0]
           self.assertNotEqual(lowerCamelCase__		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tokenizer.encode(lowerCamelCase__		,add_special_tokens=lowerCamelCase__		,add_prefix_space=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		tokenizer.convert_ids_to_tokens(encoded[0] )[0]
           self.assertEqual(lowerCamelCase__		,lowerCamelCase__ )

           tokenizer.add_special_tokens({"""bos_token""": """<s>"""} )
           SCREAMING_SNAKE_CASE	=		tokenizer.encode(lowerCamelCase__		,add_special_tokens=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		tokenizer.convert_ids_to_tokens(encoded[1] )[0]
           self.assertNotEqual(lowerCamelCase__		,lowerCamelCase__ )

           # Testing spaces after special tokens
           SCREAMING_SNAKE_CASE	=		"""<mask>"""
           tokenizer.add_special_tokens(
               {"""mask_token""": AddedToken(lowerCamelCase__		,lstrip=lowerCamelCase__		,rstrip=lowerCamelCase__ )} )  # mask token has a left space
           SCREAMING_SNAKE_CASE	=		tokenizer.convert_tokens_to_ids(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		"""Encode <mask> sequence"""
           SCREAMING_SNAKE_CASE	=		"""Encode <mask>sequence"""

           SCREAMING_SNAKE_CASE	=		tokenizer.encode(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		encoded.index(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
           self.assertEqual(lowerCamelCase__		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tokenizer.encode(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		encoded.index(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
           self.assertNotEqual(lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Optional[int]:




           '''simple docstring'''


           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                       SCREAMING_SNAKE_CASE	=		self.rust_tokenizer_class.from_pretrained(lowerCamelCase__		,**lowerCamelCase__ )
                       SCREAMING_SNAKE_CASE	=		self.tokenizer_class.from_pretrained(lowerCamelCase__		,**lowerCamelCase__ )
                       SCREAMING_SNAKE_CASE	=		"""A, <mask> AllenNLP sentence."""
                       SCREAMING_SNAKE_CASE	=		tokenizer_r.encode_plus(lowerCamelCase__		,add_special_tokens=lowerCamelCase__		,return_token_type_ids=lowerCamelCase__ )
                       SCREAMING_SNAKE_CASE	=		tokenizer_p.encode_plus(lowerCamelCase__		,add_special_tokens=lowerCamelCase__		,return_token_type_ids=lowerCamelCase__ )

                       # token_type_ids should put 0 everywhere
                       self.assertEqual(sum(tokens_r["""token_type_ids"""] )		,sum(tokens_p["""token_type_ids"""] ) )

                       # attention_mask should put 1 everywhere, so sum over length should be 1
                       self.assertEqual(
                           sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] )		,sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] )		,)

                       SCREAMING_SNAKE_CASE	=		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
                       SCREAMING_SNAKE_CASE	=		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )

                       # Rust correctly handles the space before the mask while python doesnt
                       self.assertSequenceEqual(tokens_p["""input_ids"""]		,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
                       self.assertSequenceEqual(tokens_r["""input_ids"""]		,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )

                       self.assertSequenceEqual(
                           lowerCamelCase__		,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
                       self.assertSequenceEqual(
                           lowerCamelCase__		,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[Any]:




           '''simple docstring'''


           for trim_offsets, add_prefix_space in itertools.product([True, False]		,repeat=2 ):
                 SCREAMING_SNAKE_CASE	=		self.rust_tokenizer_class.from_pretrained(
                     self.tmpdirname		,use_fast=lowerCamelCase__		,add_prefix_space=lowerCamelCase__		,trim_offsets=lowerCamelCase__ )

                 SCREAMING_SNAKE_CASE	=		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
                 SCREAMING_SNAKE_CASE	=		json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

                 self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]		,lowerCamelCase__ )

                 self.assertEqual(post_processor_state["""add_prefix_space"""]		,lowerCamelCase__ )
                 self.assertEqual(post_processor_state["""trim_offsets"""]		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[str]:




           '''simple docstring'''


           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                       SCREAMING_SNAKE_CASE	=		"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                       SCREAMING_SNAKE_CASE	=		F"""{text_of_1_token} {text_of_1_token}"""

                       SCREAMING_SNAKE_CASE	=		self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase__		,use_fast=lowerCamelCase__		,add_prefix_space=lowerCamelCase__		,trim_offsets=lowerCamelCase__ )
                       SCREAMING_SNAKE_CASE	=		tokenizer_r(lowerCamelCase__		,return_offsets_mapping=lowerCamelCase__		,add_special_tokens=lowerCamelCase__ )
                       self.assertEqual(encoding.offset_mapping[0]		,(0, len(lowerCamelCase__ )) )
                       self.assertEqual(
                           encoding.offset_mapping[1]		,(len(lowerCamelCase__ ) + 1, len(lowerCamelCase__ ) + 1 + len(lowerCamelCase__ ))		,)

                       SCREAMING_SNAKE_CASE	=		self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase__		,use_fast=lowerCamelCase__		,add_prefix_space=lowerCamelCase__		,trim_offsets=lowerCamelCase__ )
                       SCREAMING_SNAKE_CASE	=		tokenizer_r(lowerCamelCase__		,return_offsets_mapping=lowerCamelCase__		,add_special_tokens=lowerCamelCase__ )
                       self.assertEqual(encoding.offset_mapping[0]		,(0, len(lowerCamelCase__ )) )
                       self.assertEqual(
                           encoding.offset_mapping[1]		,(len(lowerCamelCase__ ) + 1, len(lowerCamelCase__ ) + 1 + len(lowerCamelCase__ ))		,)

                       SCREAMING_SNAKE_CASE	=		self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase__		,use_fast=lowerCamelCase__		,add_prefix_space=lowerCamelCase__		,trim_offsets=lowerCamelCase__ )
                       SCREAMING_SNAKE_CASE	=		tokenizer_r(lowerCamelCase__		,return_offsets_mapping=lowerCamelCase__		,add_special_tokens=lowerCamelCase__ )
                       self.assertEqual(encoding.offset_mapping[0]		,(0, len(lowerCamelCase__ )) )
                       self.assertEqual(
                           encoding.offset_mapping[1]		,(len(lowerCamelCase__ ), len(lowerCamelCase__ ) + 1 + len(lowerCamelCase__ ))		,)

                       SCREAMING_SNAKE_CASE	=		self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase__		,use_fast=lowerCamelCase__		,add_prefix_space=lowerCamelCase__		,trim_offsets=lowerCamelCase__ )
                       SCREAMING_SNAKE_CASE	=		tokenizer_r(lowerCamelCase__		,return_offsets_mapping=lowerCamelCase__		,add_special_tokens=lowerCamelCase__ )
                       self.assertEqual(encoding.offset_mapping[0]		,(0, len(lowerCamelCase__ )) )
                       self.assertEqual(
                           encoding.offset_mapping[1]		,(len(lowerCamelCase__ ), len(lowerCamelCase__ ) + 1 + len(lowerCamelCase__ ))		,)

                       SCREAMING_SNAKE_CASE	=		F""" {text}"""

                       # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                       #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                       # )
                       # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                       # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                       # self.assertEqual(
                       #     encoding.offset_mapping[1],
                       #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                       # )

                       SCREAMING_SNAKE_CASE	=		self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase__		,use_fast=lowerCamelCase__		,add_prefix_space=lowerCamelCase__		,trim_offsets=lowerCamelCase__ )
                       SCREAMING_SNAKE_CASE	=		tokenizer_r(lowerCamelCase__		,return_offsets_mapping=lowerCamelCase__		,add_special_tokens=lowerCamelCase__ )
                       self.assertEqual(encoding.offset_mapping[0]		,(1, 1 + len(lowerCamelCase__ )) )
                       self.assertEqual(
                           encoding.offset_mapping[1]		,(1 + len(lowerCamelCase__ ) + 1, 1 + len(lowerCamelCase__ ) + 1 + len(lowerCamelCase__ ))		,)

                       SCREAMING_SNAKE_CASE	=		self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase__		,use_fast=lowerCamelCase__		,add_prefix_space=lowerCamelCase__		,trim_offsets=lowerCamelCase__ )
                       SCREAMING_SNAKE_CASE	=		tokenizer_r(lowerCamelCase__		,return_offsets_mapping=lowerCamelCase__		,add_special_tokens=lowerCamelCase__ )
                       self.assertEqual(encoding.offset_mapping[0]		,(0, 1 + len(lowerCamelCase__ )) )
                       self.assertEqual(
                           encoding.offset_mapping[1]		,(1 + len(lowerCamelCase__ ), 1 + len(lowerCamelCase__ ) + 1 + len(lowerCamelCase__ ))		,)

                       SCREAMING_SNAKE_CASE	=		self.rust_tokenizer_class.from_pretrained(
                           lowerCamelCase__		,use_fast=lowerCamelCase__		,add_prefix_space=lowerCamelCase__		,trim_offsets=lowerCamelCase__ )
                       SCREAMING_SNAKE_CASE	=		tokenizer_r(lowerCamelCase__		,return_offsets_mapping=lowerCamelCase__		,add_special_tokens=lowerCamelCase__ )
                       self.assertEqual(encoding.offset_mapping[0]		,(0, 1 + len(lowerCamelCase__ )) )
                       self.assertEqual(
                           encoding.offset_mapping[1]		,(1 + len(lowerCamelCase__ ), 1 + len(lowerCamelCase__ ) + 1 + len(lowerCamelCase__ ))		,)









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		:					str		,lowerCamelCase__		:					int		,lowerCamelCase__		:					List[str]=3		,lowerCamelCase__		:					Optional[Any]=32		,lowerCamelCase__		:					Any=3		,lowerCamelCase__		:					Any=10		,lowerCamelCase__		:					Any=[10, 20, 30, 40]		,lowerCamelCase__		:					Any=[1, 1, 2, 1]		,lowerCamelCase__		:					Union[str, Any]=True		,lowerCamelCase__		:					List[str]=True		,lowerCamelCase__		:					Union[str, Any]="relu"		,lowerCamelCase__		:					Dict=3		,lowerCamelCase__		:					Any=None		,)  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		parent
           SCREAMING_SNAKE_CASE	=		batch_size
           SCREAMING_SNAKE_CASE	=		image_size
           SCREAMING_SNAKE_CASE	=		num_channels
           SCREAMING_SNAKE_CASE	=		embeddings_size
           SCREAMING_SNAKE_CASE	=		hidden_sizes
           SCREAMING_SNAKE_CASE	=		depths
           SCREAMING_SNAKE_CASE	=		is_training
           SCREAMING_SNAKE_CASE	=		use_labels
           SCREAMING_SNAKE_CASE	=		hidden_act
           SCREAMING_SNAKE_CASE	=		num_labels
           SCREAMING_SNAKE_CASE	=		scope
           SCREAMING_SNAKE_CASE	=		len(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Optional[int]:




           '''simple docstring'''


           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        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Union[str, Any]:




           '''simple docstring'''


           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        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					List[Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetModel(config=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Dict )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.num_labels
           SCREAMING_SNAKE_CASE	=		TFResNetForImageClassification(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__		,labels=lowerCamelCase__ )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[Any]:




           '''simple docstring'''


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

@require_tf
class 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Union[str, Any]				=    (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     __snake_case					:			int				=    (
         {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     __snake_case					:			Optional[int]				=    False
     __snake_case					:			int				=    False
     __snake_case					:			Optional[Any]				=    False
     __snake_case					:			Union[str, Any]				=    False
     __snake_case					:			str				=    False






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetModelTester(self )
           SCREAMING_SNAKE_CASE	=		ConfigTester(self		,config_class=lowerCamelCase__		,has_text_modality=lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           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        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           return






     @unittest.skip(reason="""ResNet does not use inputs_embeds""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""ResNet does not support input and output embeddings""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      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(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		inspect.signature(model.call )
                 # 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]		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__		:					str		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Dict ):
                 SCREAMING_SNAKE_CASE	=		model_class(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		model(**self._prepare_for_class(lowerCamelCase__		,lowerCamelCase__ ) )

                 SCREAMING_SNAKE_CASE	=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 SCREAMING_SNAKE_CASE	=		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]		,)

           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE	=		["""basic""", """bottleneck"""]
           for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                       SCREAMING_SNAKE_CASE	=		layer_type
                       SCREAMING_SNAKE_CASE	=		True
                       check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )

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

                       check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[Any]:




           '''simple docstring'''


           for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE	=		TFResNetModel.from_pretrained(lowerCamelCase__ )
                 self.assertIsNotNone(lowerCamelCase__ )



def      __lowercase     (   )				->     Union[str, Any]:


      '''simple docstring'''


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



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



     '''simple docstring'''

     @cached_property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Dict:




           '''simple docstring'''


           return (
               AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
               if is_vision_available()
               else None
           )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

           SCREAMING_SNAKE_CASE	=		self.default_image_processor
           SCREAMING_SNAKE_CASE	=		prepare_img()
           SCREAMING_SNAKE_CASE	=		image_processor(images=lowerCamelCase__		,return_tensors="""tf""" )

           # forward pass
           SCREAMING_SNAKE_CASE	=		model(**lowerCamelCase__ )

           # verify the logits
           SCREAMING_SNAKE_CASE	=		tf.TensorShape((1, 1000) )
           self.assertEqual(outputs.logits.shape		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tf.constant([-11.1069, -9.7877, -8.3777] )

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


