



"""simple docstring"""







import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCAmelCase_   						=   logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		if "token" in model_name_or_path:
				return "rag_token"
		if "sequence" in model_name_or_path:
				return "rag_sequence"
		if "bart" in model_name_or_path:
				return "bart"
		return None



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      List[str]:
		return max(metric_fn(__lowerCamelCase						,     __lowerCamelCase	) for gt in ground_truths	)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Any:
		lowercase__   :    List[Any]				=			[line.strip() for line in open(__lowerCamelCase						,     '''r'''	).readlines()]
		lowercase__   :    Union[str, Any]				=			[]

		if args.gold_data_mode == "qa":
				lowercase__   :    List[Any]				=			pd.read_csv(__lowerCamelCase						,     sep='''\t'''						,     header=__lowerCamelCase	)
				for answer_list in data[1]:
						lowercase__   :    List[str]				=			ast.literal_eval(__lowerCamelCase	)
						answers.append(__lowerCamelCase	)
		else:
				lowercase__   :    int				=			[line.strip() for line in open(__lowerCamelCase						,     '''r'''	).readlines()]
				lowercase__   :    str				=			[[reference] for reference in references]

		lowercase__   :    str				=			0
		for prediction, ground_truths in zip(__lowerCamelCase						,     __lowerCamelCase	):
				total += 1
				em += metric_max_over_ground_truths(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
				fa += metric_max_over_ground_truths(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)

		lowercase__   :    str				=			1_0_0.0 * em / total
		lowercase__   :    Tuple				=			1_0_0.0 * fa / total

		logger.info(f"""F1: {fa:.2f}"""	)
		logger.info(f"""EM: {em:.2f}"""	)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      List[str]:
		lowercase__   :    Union[str, Any]				=			args.k
		lowercase__   :    Tuple				=			[line.strip() for line in open(__lowerCamelCase						,     '''r'''	).readlines()]
		lowercase__   :    Any				=			[line.strip() for line in open(__lowerCamelCase						,     '''r'''	).readlines()]

		lowercase__   :    Dict				=			0
		for hypo, reference in zip(__lowerCamelCase						,     __lowerCamelCase	):
				lowercase__   :    Tuple				=			set(hypo.split('''\t'''	)[:k]	)
				lowercase__   :    int				=			set(reference.split('''\t'''	)	)
				total += 1
				em += len(hypo_provenance & ref_provenance	) / k

		lowercase__   :    int				=			1_0_0.0 * em / total
		logger.info(f"""Precision@{k}: {em: .2f}"""	)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      List[str]:
		def strip_title(__lowerCamelCase	):
				if title.startswith('''"'''	):
						lowercase__   :    List[str]				=			title[1:]
				if title.endswith('''"'''	):
						lowercase__   :    List[str]				=			title[:-1]
				return title

		lowercase__   :    Optional[int]				=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    __lowerCamelCase						,     return_tensors='''pt'''						,     padding=__lowerCamelCase						,     truncation=__lowerCamelCase						,     )['''input_ids'''].to(args.device	)

		lowercase__   :    Tuple				=			rag_model.rag.question_encoder(__lowerCamelCase	)
		lowercase__   :    Union[str, Any]				=			question_enc_outputs[0]

		lowercase__   :    Dict				=			rag_model.retriever(
		    __lowerCamelCase						,     question_enc_pool_output.cpu().detach().to(torch.floataa	).numpy()						,     prefix=rag_model.rag.generator.config.prefix						,     n_docs=rag_model.config.n_docs						,     return_tensors='''pt'''						,     )
		lowercase__   :    List[Any]				=			rag_model.retriever.index.get_doc_dicts(result.doc_ids	)
		lowercase__   :    Union[str, Any]				=			[]
		for docs in all_docs:
				lowercase__   :    List[Any]				=			[strip_title(__lowerCamelCase	) for title in docs['''title''']]
				provenance_strings.append('''\t'''.join(__lowerCamelCase	)	)
		return provenance_strings



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      str:
		with torch.no_grad():
				lowercase__   :    Union[str, Any]				=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    __lowerCamelCase						,     return_tensors='''pt'''						,     padding=__lowerCamelCase						,     truncation=__lowerCamelCase	)

				lowercase__   :    Dict				=			inputs_dict.input_ids.to(args.device	)
				lowercase__   :    int				=			inputs_dict.attention_mask.to(args.device	)
				lowercase__   :    Optional[Any]				=			rag_model.generate(  # rag_model overwrites generate
				    __lowerCamelCase						,     attention_mask=__lowerCamelCase						,     num_beams=args.num_beams						,     min_length=args.min_length						,     max_length=args.max_length						,     early_stopping=__lowerCamelCase						,     num_return_sequences=1						,     bad_words_ids=[[0, 0]]						,     )
				lowercase__   :    Any				=			rag_model.retriever.generator_tokenizer.batch_decode(__lowerCamelCase						,     skip_special_tokens=__lowerCamelCase	)

				if args.print_predictions:
						for q, a in zip(__lowerCamelCase						,     __lowerCamelCase	):
								logger.info('''Q: {} - A: {}'''.format(__lowerCamelCase						,     __lowerCamelCase	)	)

				return answers



def      __UpperCAmelCase      (    )							->      str:
		lowercase__   :    Any				=			argparse.ArgumentParser()
		parser.add_argument(
		    '''--model_type'''						,     choices=['''rag_sequence''', '''rag_token''', '''bart''']						,     type=__lowerCamelCase						,     help=(
		        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
		        ''' model_name_or_path'''
		    )						,     )
		parser.add_argument(
		    '''--index_name'''						,     default=__lowerCamelCase						,     choices=['''exact''', '''compressed''', '''legacy''']						,     type=__lowerCamelCase						,     help='''RAG model retriever type'''						,     )
		parser.add_argument(
		    '''--index_path'''						,     default=__lowerCamelCase						,     type=__lowerCamelCase						,     help='''Path to the retrieval index'''						,     )
		parser.add_argument('''--n_docs'''						,     default=5						,     type=__lowerCamelCase						,     help='''Number of retrieved docs'''	)
		parser.add_argument(
		    '''--model_name_or_path'''						,     default=__lowerCamelCase						,     type=__lowerCamelCase						,     required=__lowerCamelCase						,     help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''						,     )
		parser.add_argument(
		    '''--eval_mode'''						,     choices=['''e2e''', '''retrieval''']						,     default='''e2e'''						,     type=__lowerCamelCase						,     help=(
		        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
		        ''' precision@k.'''
		    )						,     )
		parser.add_argument('''--k'''						,     default=1						,     type=__lowerCamelCase						,     help='''k for the precision@k calculation'''	)
		parser.add_argument(
		    '''--evaluation_set'''						,     default=__lowerCamelCase						,     type=__lowerCamelCase						,     required=__lowerCamelCase						,     help='''Path to a file containing evaluation samples'''						,     )
		parser.add_argument(
		    '''--gold_data_path'''						,     default=__lowerCamelCase						,     type=__lowerCamelCase						,     required=__lowerCamelCase						,     help='''Path to a tab-separated file with gold samples'''						,     )
		parser.add_argument(
		    '''--gold_data_mode'''						,     default='''qa'''						,     type=__lowerCamelCase						,     choices=['''qa''', '''ans''']						,     help=(
		        '''Format of the gold data file'''
		        '''qa - a single line in the following format: question [tab] answer_list'''
		        '''ans - a single line of the gold file contains the expected answer string'''
		    )						,     )
		parser.add_argument(
		    '''--predictions_path'''						,     type=__lowerCamelCase						,     default='''predictions.txt'''						,     help='''Name of the predictions file, to be stored in the checkpoints directory'''						,     )
		parser.add_argument(
		    '''--eval_all_checkpoints'''						,     action='''store_true'''						,     help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''						,     )
		parser.add_argument(
		    '''--eval_batch_size'''						,     default=8						,     type=__lowerCamelCase						,     help='''Batch size per GPU/CPU for evaluation.'''						,     )
		parser.add_argument(
		    '''--recalculate'''						,     help='''Recalculate predictions even if the prediction file exists'''						,     action='''store_true'''						,     )
		parser.add_argument(
		    '''--num_beams'''						,     default=4						,     type=__lowerCamelCase						,     help='''Number of beams to be used when generating answers'''						,     )
		parser.add_argument('''--min_length'''						,     default=1						,     type=__lowerCamelCase						,     help='''Min length of the generated answers'''	)
		parser.add_argument('''--max_length'''						,     default=50						,     type=__lowerCamelCase						,     help='''Max length of the generated answers'''	)

		parser.add_argument(
		    '''--print_predictions'''						,     action='''store_true'''						,     help='''If True, prints predictions while evaluating.'''						,     )
		parser.add_argument(
		    '''--print_docs'''						,     action='''store_true'''						,     help='''If True, prints docs retried while generating.'''						,     )
		lowercase__   :    List[Any]				=			parser.parse_args()
		lowercase__   :    Dict				=			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''	)
		return args



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      str:
		lowercase__   :    str				=			{}
		if args.model_type is None:
				lowercase__   :    int				=			infer_model_type(args.model_name_or_path	)
				assert args.model_type is not None
		if args.model_type.startswith('''rag'''	):
				lowercase__   :    str				=			RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
				lowercase__   :    Union[str, Any]				=			args.n_docs
				if args.index_name is not None:
						lowercase__   :    Any				=			args.index_name
				if args.index_path is not None:
						lowercase__   :    List[str]				=			args.index_path
		else:
				lowercase__   :    Union[str, Any]				=			BartForConditionalGeneration

		lowercase__   :    Tuple				=			(
		    [f.path for f in os.scandir(args.model_name_or_path	) if f.is_dir()]
		    if args.eval_all_checkpoints
		    else [args.model_name_or_path]
		)

		logger.info('''Evaluate the following checkpoints: %s'''						,     __lowerCamelCase	)

		lowercase__   :    Optional[int]				=			get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
		lowercase__   :    Union[str, Any]				=			evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

		for checkpoint in checkpoints:
				if os.path.exists(args.predictions_path	) and (not args.recalculate):
						logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path	)	)
						score_fn(__lowerCamelCase						,     args.predictions_path						,     args.gold_data_path	)
						continue

				logger.info('''***** Running evaluation for {} *****'''.format(__lowerCamelCase	)	)
				logger.info('''  Batch size = %d'''						,     args.eval_batch_size	)
				logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path	)	)

				if args.model_type.startswith('''rag'''	):
						lowercase__   :    Any				=			RagRetriever.from_pretrained(__lowerCamelCase						,     **__lowerCamelCase	)
						lowercase__   :    Optional[int]				=			model_class.from_pretrained(__lowerCamelCase						,     retriever=__lowerCamelCase						,     **__lowerCamelCase	)
						model.retriever.init_retrieval()
				else:
						lowercase__   :    str				=			model_class.from_pretrained(__lowerCamelCase						,     **__lowerCamelCase	)
				model.to(args.device	)

				with open(args.evaluation_set						,     '''r'''	) as eval_file, open(args.predictions_path						,     '''w'''	) as preds_file:
						lowercase__   :    Dict				=			[]
						for line in tqdm(__lowerCamelCase	):
								questions.append(line.strip()	)
								if len(__lowerCamelCase	) == args.eval_batch_size:
										lowercase__   :    Optional[int]				=			evaluate_batch_fn(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
										preds_file.write('''\n'''.join(__lowerCamelCase	) + '''\n'''	)
										preds_file.flush()
										lowercase__   :    List[Any]				=			[]
						if len(__lowerCamelCase	) > 0:
								lowercase__   :    Union[str, Any]				=			evaluate_batch_fn(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
								preds_file.write('''\n'''.join(__lowerCamelCase	)	)
								preds_file.flush()

						score_fn(__lowerCamelCase						,     args.predictions_path						,     args.gold_data_path	)


if __name__ == "__main__":
							lowerCAmelCase_   						=   get_args()
							main(args)





"""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        __A       :







		'''simple docstring'''





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






				"""simple docstring"""



				lowercase__   :    int				=			parent
				lowercase__   :    Optional[Any]				=			batch_size
				lowercase__   :    Optional[Any]				=			image_size
				lowercase__   :    Optional[Any]				=			num_channels
				lowercase__   :    Optional[Any]				=			embeddings_size
				lowercase__   :    Optional[Any]				=			hidden_sizes
				lowercase__   :    str				=			depths
				lowercase__   :    Tuple				=			is_training
				lowercase__   :    List[Any]				=			use_labels
				lowercase__   :    Union[str, Any]				=			hidden_act
				lowercase__   :    Union[str, Any]				=			num_labels
				lowercase__   :    Tuple				=			scope
				lowercase__   :    Optional[Any]				=			len(_snake_case						)







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

				lowercase__   :    int				=			self.get_config()

				return config, pixel_values, labels







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[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 					UpperCAmelCase    (	self						:  List[str]			,_snake_case						:  Optional[int]			,_snake_case						:  int			,_snake_case						:  Tuple						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			TFResNetModel(config=_snake_case						)
				lowercase__   :    List[str]				=			model(_snake_case						)
				# 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 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  Optional[Any]			,_snake_case						:  int			,_snake_case						:  Any						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    Tuple				=			self.num_labels
				lowercase__   :    Union[str, Any]				=			TFResNetForImageClassification(_snake_case						)
				lowercase__   :    List[str]				=			model(_snake_case			,labels=_snake_case						)
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)						)







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		str:






				"""simple docstring"""



				lowercase__   :    Dict				=			self.prepare_config_and_inputs()
				lowercase__		,			lowercase__		,			lowercase__   :    Union[str, Any]				=			config_and_inputs
				lowercase__   :    Dict				=			{'''pixel_values''': pixel_values}
				return config, inputs_dict





@require_tf
class        __A       (	A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





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

		lowerCAmelCase			:					List[Any]   	=				False
		lowerCAmelCase			:					List[Any]   	=				False
		lowerCAmelCase			:					int   	=				False
		lowerCAmelCase			:					Union[str, Any]   	=				False
		lowerCAmelCase			:					List[str]   	=				False







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






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			TFResNetModelTester(self						)
				lowercase__   :    int				=			ConfigTester(self			,config_class=_snake_case			,has_text_modality=_snake_case						)







		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		str:






				"""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 					UpperCAmelCase    (	self						:  List[Any]						)					->		List[str]:






				"""simple docstring"""



				return







		@unittest.skip(reason='''ResNet does not use inputs_embeds'''						)
		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Dict:






				"""simple docstring"""



				pass







		@unittest.skip(reason='''ResNet does not support input and output embeddings'''						)
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""simple docstring"""



				pass







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






				"""simple docstring"""



				lowercase__		,			lowercase__   :    Dict				=			self.model_tester.prepare_config_and_inputs_for_common()

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

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







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		List[str]:






				"""simple docstring"""



				def check_hidden_states_output(_snake_case						:  Optional[int]			,_snake_case						:  List[str]			,_snake_case						:  Optional[Any]						):
						lowercase__   :    str				=			model_class(_snake_case						)
						lowercase__   :    Union[str, Any]				=			model(**self._prepare_for_class(_snake_case			,_snake_case						)						)

						lowercase__   :    List[Any]				=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						lowercase__   :    Tuple				=			self.model_tester.num_stages
						self.assertEqual(len(_snake_case						)			,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__   :    int				=			self.model_tester.prepare_config_and_inputs_for_common()
				lowercase__   :    List[Any]				=			['''basic''', '''bottleneck''']
				for model_class in self.all_model_classes:
						for layer_type in layers_type:
								lowercase__   :    List[Any]				=			layer_type
								lowercase__   :    Dict				=			True
								check_hidden_states_output(_snake_case			,_snake_case			,_snake_case						)

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

								check_hidden_states_output(_snake_case			,_snake_case			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_snake_case						)







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






				"""simple docstring"""



				for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Optional[Any]				=			TFResNetModel.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)





def      __UpperCAmelCase      (    )							->      Dict:
		lowercase__   :    List[str]				=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image





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







		'''simple docstring'''





		@cached_property
		def 					UpperCAmelCase    (	self						:  str						)					->		Any:






				"""simple docstring"""



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







		@slow
		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Tuple				=			TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

				lowercase__   :    Any				=			self.default_image_processor
				lowercase__   :    int				=			prepare_img()
				lowercase__   :    Tuple				=			image_processor(images=_snake_case			,return_tensors='''tf'''						)

				# forward pass
				lowercase__   :    Dict				=			model(**_snake_case						)

				# verify the logits
				lowercase__   :    List[str]				=			tf.TensorShape((1, 1_000)						)
				self.assertEqual(outputs.logits.shape			,_snake_case						)

				lowercase__   :    Any				=			tf.constant([-11.1069, -9.7877, -8.3777]						)

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