



"""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


__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def        lowerCAmelCase_(   lowercase_     :							List[str]							)	->       Union[str, Any]:
				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        lowerCAmelCase_(   lowercase_     :							List[Any]						,   lowercase_     :							Any						,   lowercase_     :							List[str]							)	->       List[str]:
				return max(metric_fn(lowercase_						,   lowercase_							) for gt in ground_truths							)



def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							str						,   lowercase_     :							Union[str, Any]							)	->       Union[str, Any]:
				_lowerCamelCase        =				[line.strip() for line in open(lowercase_						,   '''r'''							).readlines()]
				_lowerCamelCase        =				[]

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

				_lowerCamelCase        =				0
				for prediction, ground_truths in zip(lowercase_						,   lowercase_							):
								total += 1
								em += metric_max_over_ground_truths(lowercase_						,   lowercase_						,   lowercase_							)
								fa += metric_max_over_ground_truths(lowercase_						,   lowercase_						,   lowercase_							)

				_lowerCamelCase        =				1_0_0.0 * em / total
				_lowerCamelCase        =				1_0_0.0 * fa / total

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



def        lowerCAmelCase_(   lowercase_     :							List[Any]						,   lowercase_     :							List[Any]						,   lowercase_     :							Optional[int]							)	->       List[str]:
				_lowerCamelCase        =				args.k
				_lowerCamelCase        =				[line.strip() for line in open(lowercase_						,   '''r'''							).readlines()]
				_lowerCamelCase        =				[line.strip() for line in open(lowercase_						,   '''r'''							).readlines()]

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

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



def        lowerCAmelCase_(   lowercase_     :							List[str]						,   lowercase_     :							Optional[Any]						,   lowercase_     :							Optional[Any]							)	->       Any:
				def strip_title(lowercase_     :							Optional[int]							):
								if title.startswith('''"'''							):
												_lowerCamelCase        =				title[1:]
								if title.endswith('''"'''							):
												_lowerCamelCase        =				title[:-1]
								return title

				_lowerCamelCase        =				rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    lowercase_						,   return_tensors='''pt'''						,   padding=lowercase_						,   truncation=lowercase_						,   )['input_ids'].to(args.device							)

				_lowerCamelCase        =				rag_model.rag.question_encoder(lowercase_							)
				_lowerCamelCase        =				question_enc_outputs[0]

				_lowerCamelCase        =				rag_model.retriever(
				    lowercase_						,   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'''						,   )
				_lowerCamelCase        =				rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
				_lowerCamelCase        =				[]
				for docs in all_docs:
								_lowerCamelCase        =				[strip_title(lowercase_							) for title in docs['title']]
								provenance_strings.append('''\t'''.join(lowercase_							)							)
				return provenance_strings



def        lowerCAmelCase_(   lowercase_     :							List[Any]						,   lowercase_     :							List[Any]						,   lowercase_     :							List[Any]							)	->       Tuple:
				with torch.no_grad():
								_lowerCamelCase        =				rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    lowercase_						,   return_tensors='''pt'''						,   padding=lowercase_						,   truncation=lowercase_							)

								_lowerCamelCase        =				inputs_dict.input_ids.to(args.device							)
								_lowerCamelCase        =				inputs_dict.attention_mask.to(args.device							)
								_lowerCamelCase        =				rag_model.generate(  # rag_model overwrites generate
								    lowercase_						,   attention_mask=lowercase_						,   num_beams=args.num_beams						,   min_length=args.min_length						,   max_length=args.max_length						,   early_stopping=lowercase_						,   num_return_sequences=1						,   bad_words_ids=[[0, 0]]						,   )
								_lowerCamelCase        =				rag_model.retriever.generator_tokenizer.batch_decode(lowercase_						,   skip_special_tokens=lowercase_							)

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

								return answers



def        lowerCAmelCase_(   )	->       Tuple:
				_lowerCamelCase        =				argparse.ArgumentParser()
				parser.add_argument(
				    '''--model_type'''						,   choices=['''rag_sequence''', '''rag_token''', '''bart''']						,   type=lowercase_						,   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=lowercase_						,   choices=['''exact''', '''compressed''', '''legacy''']						,   type=lowercase_						,   help='''RAG model retriever type'''						,   )
				parser.add_argument(
				    '''--index_path'''						,   default=lowercase_						,   type=lowercase_						,   help='''Path to the retrieval index'''						,   )
				parser.add_argument('''--n_docs'''						,   default=5						,   type=lowercase_						,   help='''Number of retrieved docs'''							)
				parser.add_argument(
				    '''--model_name_or_path'''						,   default=lowercase_						,   type=lowercase_						,   required=lowercase_						,   help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''						,   )
				parser.add_argument(
				    '''--eval_mode'''						,   choices=['''e2e''', '''retrieval''']						,   default='''e2e'''						,   type=lowercase_						,   help=(
				        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
				        ''' precision@k.'''
				    )						,   )
				parser.add_argument('''--k'''						,   default=1						,   type=lowercase_						,   help='''k for the precision@k calculation'''							)
				parser.add_argument(
				    '''--evaluation_set'''						,   default=lowercase_						,   type=lowercase_						,   required=lowercase_						,   help='''Path to a file containing evaluation samples'''						,   )
				parser.add_argument(
				    '''--gold_data_path'''						,   default=lowercase_						,   type=lowercase_						,   required=lowercase_						,   help='''Path to a tab-separated file with gold samples'''						,   )
				parser.add_argument(
				    '''--gold_data_mode'''						,   default='''qa'''						,   type=lowercase_						,   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=lowercase_						,   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=lowercase_						,   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=lowercase_						,   help='''Number of beams to be used when generating answers'''						,   )
				parser.add_argument('''--min_length'''						,   default=1						,   type=lowercase_						,   help='''Min length of the generated answers'''							)
				parser.add_argument('''--max_length'''						,   default=50						,   type=lowercase_						,   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.'''						,   )
				_lowerCamelCase        =				parser.parse_args()
				_lowerCamelCase        =				torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''							)
				return args



def        lowerCAmelCase_(   lowercase_     :							Optional[int]							)	->       List[Any]:
				_lowerCamelCase        =				{}
				if args.model_type is None:
								_lowerCamelCase        =				infer_model_type(args.model_name_or_path							)
								assert args.model_type is not None
				if args.model_type.startswith('''rag'''							):
								_lowerCamelCase        =				RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
								_lowerCamelCase        =				args.n_docs
								if args.index_name is not None:
												_lowerCamelCase        =				args.index_name
								if args.index_path is not None:
												_lowerCamelCase        =				args.index_path
				else:
								_lowerCamelCase        =				BartForConditionalGeneration

				_lowerCamelCase        =				(
				    [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'''						,   lowercase_							)

				_lowerCamelCase        =				get_scores if args.eval_mode == 'e2e' else get_precision_at_k
				_lowerCamelCase        =				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(lowercase_						,   args.predictions_path						,   args.gold_data_path							)
												continue

								logger.info('''***** Running evaluation for {} *****'''.format(lowercase_							)							)
								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'''							):
												_lowerCamelCase        =				RagRetriever.from_pretrained(lowercase_						,   **lowercase_							)
												_lowerCamelCase        =				model_class.from_pretrained(lowercase_						,   retriever=lowercase_						,   **lowercase_							)
												model.retriever.init_retrieval()
								else:
												_lowerCamelCase        =				model_class.from_pretrained(lowercase_						,   **lowercase_							)
								model.to(args.device							)

								with open(args.evaluation_set						,   '''r'''							) as eval_file, open(args.predictions_path						,   '''w'''							) as preds_file:
												_lowerCamelCase        =				[]
												for line in tqdm(lowercase_							):
																questions.append(line.strip()							)
																if len(lowercase_							) == args.eval_batch_size:
																				_lowerCamelCase        =				evaluate_batch_fn(lowercase_						,   lowercase_						,   lowercase_							)
																				preds_file.write('''\n'''.join(lowercase_							) + '''\n'''							)
																				preds_file.flush()
																				_lowerCamelCase        =				[]
												if len(lowercase_							) > 0:
																_lowerCamelCase        =				evaluate_batch_fn(lowercase_						,   lowercase_						,   lowercase_							)
																preds_file.write('''\n'''.join(lowercase_							)							)
																preds_file.flush()

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


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						str    							=      get_args()
			main(args)





"""simple docstring"""
from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=1_3				,       lowerCamelCase__=3_0				,       lowerCamelCase__=2				,       lowerCamelCase__=3				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=3_2				,       lowerCamelCase__=2				,       lowerCamelCase__=4				,       lowerCamelCase__=3_7				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=1_0				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=3				,       lowerCamelCase__=0.6				,       lowerCamelCase__=None				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				image_size
									_lowerCamelCase        =				patch_size
									_lowerCamelCase        =				num_channels
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				use_labels
									_lowerCamelCase        =				hidden_size
									_lowerCamelCase        =				num_hidden_layers
									_lowerCamelCase        =				num_attention_heads
									_lowerCamelCase        =				intermediate_size
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				hidden_dropout_prob
									_lowerCamelCase        =				attention_probs_dropout_prob
									_lowerCamelCase        =				type_sequence_label_size
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				mask_ratio
									_lowerCamelCase        =				scope

									# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
									# (we add 1 for the [CLS] token)
									_lowerCamelCase        =				(image_size // patch_size) ** 2
									_lowerCamelCase        =				int(math.ceil((1 - mask_ratio) * (num_patches + 1)   )   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

									_lowerCamelCase        =				None
									if self.use_labels:
													_lowerCamelCase        =				ids_tensor([self.batch_size]				,       self.type_sequence_label_size   )

									_lowerCamelCase        =				self.get_config()

									return config, pixel_values, labels

					def 			snake_case__      (    self   ):
									return ViTMAEConfig(
									    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				,       decoder_hidden_size=self.hidden_size				,       decoder_num_hidden_layers=self.num_hidden_layers				,       decoder_num_attention_heads=self.num_attention_heads				,       decoder_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				,       is_decoder=lowerCamelCase__				,       initializer_range=self.initializer_range				,       mask_ratio=self.mask_ratio				,       )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				TFViTMAEModel(config=lowerCamelCase__   )
									_lowerCamelCase        =				model(lowerCamelCase__				,       training=lowerCamelCase__   )
									self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				TFViTMAEForPreTraining(lowerCamelCase__   )
									_lowerCamelCase        =				model(lowerCamelCase__				,       training=lowerCamelCase__   )
									# expected sequence length = num_patches
									_lowerCamelCase        =				(self.image_size // self.patch_size) ** 2
									_lowerCamelCase        =				self.patch_size**2 * self.num_channels
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, num_patches, expected_num_channels)   )

									# test greyscale images
									_lowerCamelCase        =				1
									_lowerCamelCase        =				TFViTMAEForPreTraining(lowerCamelCase__   )

									_lowerCamelCase        =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
									_lowerCamelCase        =				model(lowerCamelCase__				,       training=lowerCamelCase__   )
									_lowerCamelCase        =				self.patch_size**2
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, num_patches, expected_num_channels)   )





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									((_lowerCamelCase)  ,     (_lowerCamelCase)  ,     (_lowerCamelCase))        =				config_and_inputs
									_lowerCamelCase        =				{'''pixel_values''': pixel_values}
									return config, inputs_dict


@require_tf
class 							lowerCamelCase_(   A__,				A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Optional[Any]										= (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
					lowercase__   :       Dict										= {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

					lowercase__   :       Optional[Any]										= False
					lowercase__   :       Union[str, Any]										= False
					lowercase__   :       str										= False
					lowercase__   :       List[str]										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TFViTMAEModelTester(self   )
									_lowerCamelCase        =				ConfigTester(self				,       config_class=lowerCamelCase__				,       has_text_modality=lowerCamelCase__				,       hidden_size=3_7   )

					def 			snake_case__      (    self   ):
									self.config_tester.run_common_tests()

					@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''   )
					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													self.assertIsInstance(model.get_input_embeddings()				,       (tf.keras.layers.Layer)   )
													_lowerCamelCase        =				model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCamelCase__				,       tf.keras.layers.Layer   )   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

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

													_lowerCamelCase        =				['''pixel_values''']
													self.assertListEqual(arg_names[:1]				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									# make the mask reproducible
									np.random.seed(2   )

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCamelCase        =				int((config.image_size // config.patch_size) ** 2   )
									_lowerCamelCase        =				np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )

													_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )

													_lowerCamelCase        =				copy.deepcopy(self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )   )
													_lowerCamelCase        =				model(**lowerCamelCase__				,       noise=lowerCamelCase__   )
													_lowerCamelCase        =				outputs_dict[0].numpy()
													_lowerCamelCase        =				outputs_keywords[0].numpy()

													self.assertLess(np.sum(np.abs(output_dict - output_keywords   )   )				,       1e-6   )

					def 			snake_case__      (    self   ):
									# make the mask reproducible
									np.random.seed(2   )

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCamelCase        =				int((config.image_size // config.patch_size) ** 2   )
									_lowerCamelCase        =				np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

									def prepare_numpy_arrays(lowerCamelCase__   ):
													_lowerCamelCase        =				{}
													for k, v in inputs_dict.items():
																	if tf.is_tensor(lowerCamelCase__   ):
																					_lowerCamelCase        =				v.numpy()
																	else:
																					_lowerCamelCase        =				np.array(lowerCamelCase__   )

													return inputs_np_dict

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )

													_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )
													_lowerCamelCase        =				prepare_numpy_arrays(lowerCamelCase__   )

													_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )
													_lowerCamelCase        =				model(**lowerCamelCase__				,       noise=lowerCamelCase__   )
													self.assert_outputs_same(lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									# make masks reproducible
									np.random.seed(2   )

									_lowerCamelCase        =				int((tf_model.config.image_size // tf_model.config.patch_size) ** 2   )
									_lowerCamelCase        =				np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
									_lowerCamelCase        =				tf.constant(lowerCamelCase__   )

									# Add `noise` argument.
									# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
									_lowerCamelCase        =				tf_noise

									super().check_pt_tf_models(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									# make mask reproducible
									np.random.seed(2   )

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCamelCase        =				{
									    module_member
									    for model_class in self.all_model_classes
									    for module in (import_module(model_class.__module__   ),)
									    for module_member_name in dir(lowerCamelCase__   )
									    if module_member_name.endswith('''MainLayer'''   )
									    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
									    and module_member_name[: -len('''MainLayer'''   )] == model_class.__name__[: -len('''Model'''   )]
									    for module_member in (getattr(lowerCamelCase__				,       lowerCamelCase__   ),)
									    if isinstance(lowerCamelCase__				,       lowerCamelCase__   )
									    and tf.keras.layers.Layer in module_member.__bases__
									    and getattr(lowerCamelCase__				,       '''_keras_serializable'''				,       lowerCamelCase__   )
									}

									_lowerCamelCase        =				int((config.image_size // config.patch_size) ** 2   )
									_lowerCamelCase        =				np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
									_lowerCamelCase        =				tf.convert_to_tensor(lowerCamelCase__   )
									inputs_dict.update({'''noise''': noise}   )

									for main_layer_class in tf_main_layer_classes:
													_lowerCamelCase        =				main_layer_class(lowerCamelCase__   )

													_lowerCamelCase        =				{
													    name: tf.keras.Input(tensor.shape[1:]				,       dtype=tensor.dtype   ) for name, tensor in inputs_dict.items()
													}

													_lowerCamelCase        =				tf.keras.Model(lowerCamelCase__				,       outputs=main_layer(lowerCamelCase__   )   )
													_lowerCamelCase        =				model(lowerCamelCase__   )

													with tempfile.TemporaryDirectory() as tmpdirname:
																	_lowerCamelCase        =				os.path.join(lowerCamelCase__				,       '''keras_model.h5'''   )
																	model.save(lowerCamelCase__   )
																	_lowerCamelCase        =				tf.keras.models.load_model(
																	    lowerCamelCase__				,       custom_objects={main_layer_class.__name__: main_layer_class}   )
																	assert isinstance(lowerCamelCase__				,       tf.keras.Model   )
																	_lowerCamelCase        =				model(lowerCamelCase__   )
																	self.assert_outputs_same(lowerCamelCase__				,       lowerCamelCase__   )

					@slow
					def 			snake_case__      (    self   ):
									# make mask reproducible
									np.random.seed(2   )

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCamelCase        =				int((config.image_size // config.patch_size) ** 2   )
									_lowerCamelCase        =				np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )
													_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )

													if model_class.__name__ == "TFViTMAEModel":
																	_lowerCamelCase        =				outputs.last_hidden_state.numpy()
																	_lowerCamelCase        =				0
													else:
																	_lowerCamelCase        =				outputs.logits.numpy()
																	_lowerCamelCase        =				0

													with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_pretrained(lowerCamelCase__				,       saved_model=lowerCamelCase__   )
																	_lowerCamelCase        =				model_class.from_pretrained(lowerCamelCase__   )
																	_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )

																	if model_class.__name__ == "TFViTMAEModel":
																					_lowerCamelCase        =				after_outputs['''last_hidden_state'''].numpy()
																					_lowerCamelCase        =				0
																	else:
																					_lowerCamelCase        =				after_outputs['''logits'''].numpy()
																					_lowerCamelCase        =				0

																	_lowerCamelCase        =				np.amax(np.abs(out_a - out_a   )   )
																	self.assertLessEqual(lowerCamelCase__				,       1e-5   )

					def 			snake_case__      (    self   ):
									# make mask reproducible
									np.random.seed(2   )

									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCamelCase        =				int((config.image_size // config.patch_size) ** 2   )
									_lowerCamelCase        =				np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

									for model_class in self.all_model_classes:
													_lowerCamelCase        =				model_class(lowerCamelCase__   )
													_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )

													_lowerCamelCase        =				model(lowerCamelCase__				,       noise=lowerCamelCase__   )
													_lowerCamelCase        =				model.get_config()
													# make sure that returned config is jsonifiable, which is required by keras
													json.dumps(lowerCamelCase__   )
													_lowerCamelCase        =				model_class.from_config(model.get_config()   )
													# make sure it also accepts a normal config
													_lowerCamelCase        =				model_class.from_config(model.config   )
													_lowerCamelCase        =				new_model(lowerCamelCase__   )  # Build model
													new_model.set_weights(model.get_weights()   )
													_lowerCamelCase        =				new_model(lowerCamelCase__				,       noise=lowerCamelCase__   )

													self.assert_outputs_same(lowerCamelCase__				,       lowerCamelCase__   )

					@unittest.skip(
					    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''   )
					def 			snake_case__      (    self   ):
									pass

					@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''   )
					def 			snake_case__      (    self   ):
									pass





					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''   )
									self.assertIsNotNone(lowerCamelCase__   )



def        lowerCAmelCase_(   )	->       List[Any]:
				_lowerCamelCase        =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				return image




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





					'''simple docstring'''



					@cached_property
					def 			snake_case__      (    self   ):
									return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''   ) if is_vision_available() else None





					@slow
					def 			snake_case__      (    self   ):
									# make random mask reproducible across the PT and TF model
									np.random.seed(2   )

									_lowerCamelCase        =				TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''   )

									_lowerCamelCase        =				self.default_image_processor
									_lowerCamelCase        =				prepare_img()
									_lowerCamelCase        =				image_processor(images=lowerCamelCase__				,       return_tensors='''tf'''   )

									# prepare a noise vector that will be also used for testing the TF model
									# (this way we can ensure that the PT and TF models operate on the same inputs)
									_lowerCamelCase        =				ViTMAEConfig()
									_lowerCamelCase        =				int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2   )
									_lowerCamelCase        =				np.random.uniform(size=(1, num_patches)   )

									# forward pass
									_lowerCamelCase        =				model(**lowerCamelCase__				,       noise=lowerCamelCase__   )

									# verify the logits
									_lowerCamelCase        =				tf.convert_to_tensor([1, 1_9_6, 7_6_8]   )
									self.assertEqual(outputs.logits.shape				,       lowerCamelCase__   )

									_lowerCamelCase        =				tf.convert_to_tensor(
									    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]   )

									tf.debugging.assert_near(outputs.logits[0, :3, :3]				,       lowerCamelCase__				,       atol=1e-4   )
