





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


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

transformers_logging.set_verbosity_info()




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		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_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Dict:
       return max(metric_fn(_UpperCAmelCase       ,		_UpperCAmelCase	) for gt in ground_truths	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		List[str]:
       UpperCamelCase_ 							=	[line.strip() for line in open(_UpperCAmelCase       ,		"r"	).readlines()]
       UpperCamelCase_ 							=	[]

       if args.gold_data_mode == "qa":
              UpperCamelCase_ 							=	pd.read_csv(_UpperCAmelCase       ,		sep="\t"       ,		header=_UpperCAmelCase	)
              for answer_list in data[1]:
                     UpperCamelCase_ 							=	ast.literal_eval(_UpperCAmelCase	)
                     answers.append(_UpperCAmelCase	)
       else:
              UpperCamelCase_ 							=	[line.strip() for line in open(_UpperCAmelCase       ,		"r"	).readlines()]
              UpperCamelCase_ 							=	[[reference] for reference in references]

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

       UpperCamelCase_ 							=	100.0 * em / total
       UpperCamelCase_ 							=	100.0 * fa / total

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




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Dict:
       UpperCamelCase_ 							=	args.k
       UpperCamelCase_ 							=	[line.strip() for line in open(_UpperCAmelCase       ,		"r"	).readlines()]
       UpperCamelCase_ 							=	[line.strip() for line in open(_UpperCAmelCase       ,		"r"	).readlines()]

       UpperCamelCase_ 							=	0
       for hypo, reference in zip(_UpperCAmelCase       ,		_UpperCAmelCase	):
              UpperCamelCase_ 							=	set(hypo.split("\t"	)[:k]	)
              UpperCamelCase_ 							=	set(reference.split("\t"	)	)
              total += 1
              em += len(hypo_provenance & ref_provenance	) / k

       UpperCamelCase_ 							=	100.0 * em / total
       logger.info(F'''Precision@{k}: {em: .2f}'''	)




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


       def strip_title(UpperCamelCase_	):
              if title.startswith("\""	):
                     UpperCamelCase_ 							=	title[1:]
              if title.endswith("\""	):
                     UpperCamelCase_ 							=	title[:-1]
              return title

       UpperCamelCase_ 							=	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           _UpperCAmelCase       ,		return_tensors="pt"       ,		padding=_UpperCAmelCase       ,		truncation=_UpperCAmelCase       ,		)["input_ids"].to(args.device	)

       UpperCamelCase_ 							=	rag_model.rag.question_encoder(_UpperCAmelCase	)
       UpperCamelCase_ 							=	question_enc_outputs[0]

       UpperCamelCase_ 							=	rag_model.retriever(
           _UpperCAmelCase       ,		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"       ,		)
       UpperCamelCase_ 							=	rag_model.retriever.index.get_doc_dicts(result.doc_ids	)
       UpperCamelCase_ 							=	[]
       for docs in all_docs:
              UpperCamelCase_ 							=	[strip_title(_UpperCAmelCase	) for title in docs["title"]]
              provenance_strings.append("\t".join(_UpperCAmelCase	)	)
       return provenance_strings




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Dict:


       with torch.no_grad():
              UpperCamelCase_ 							=	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  _UpperCAmelCase       ,		return_tensors="pt"       ,		padding=_UpperCAmelCase       ,		truncation=_UpperCAmelCase	)

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

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

              return answers




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




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Any:
       UpperCamelCase_ 							=	{}
       if args.model_type is None:
              UpperCamelCase_ 							=	infer_model_type(args.model_name_or_path	)
              assert args.model_type is not None
       if args.model_type.startswith("rag"	):
              UpperCamelCase_ 							=	RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
              UpperCamelCase_ 							=	args.n_docs
              if args.index_name is not None:
                     UpperCamelCase_ 							=	args.index_name
              if args.index_path is not None:
                     UpperCamelCase_ 							=	args.index_path
       else:
              UpperCamelCase_ 							=	BartForConditionalGeneration

       UpperCamelCase_ 							=	(
           [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"       ,		_UpperCAmelCase	)

       UpperCamelCase_ 							=	get_scores if args.eval_mode == "e2e" else get_precision_at_k
       UpperCamelCase_ 							=	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(_UpperCAmelCase       ,		args.predictions_path       ,		args.gold_data_path	)
                     continue

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

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

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


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





"""simple docstring"""






from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, 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 numpy as np
					import tensorflow as tf

					from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
					from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      self.config_class(**self.inputs_dict)
								self.parent.assertTrue(hasattr(lowerCamelCase__	,					"hidden_sizes"))
								self.parent.assertTrue(hasattr(lowerCamelCase__	,					"neck_hidden_sizes"))
								self.parent.assertTrue(hasattr(lowerCamelCase__	,					"num_attention_heads"))


class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=13	,					lowerCamelCase__=32	,					lowerCamelCase__=2	,					lowerCamelCase__=3	,					lowerCamelCase__=640	,					lowerCamelCase__=4	,					lowerCamelCase__="silu"	,					lowerCamelCase__=3	,					lowerCamelCase__=32	,					lowerCamelCase__=0.1	,					lowerCamelCase__=0.1	,					lowerCamelCase__=0.1	,					lowerCamelCase__=0.02	,					lowerCamelCase__=True	,					lowerCamelCase__=True	,					lowerCamelCase__=10	,					lowerCamelCase__=None	,					)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      parent
								snake_case__  :						List[Any]		=      batch_size
								snake_case__  :						Any		=      image_size
								snake_case__  :						Tuple		=      patch_size
								snake_case__  :						int		=      num_channels
								snake_case__  :						Optional[Any]		=      last_hidden_size
								snake_case__  :						Union[str, Any]		=      num_attention_heads
								snake_case__  :						Tuple		=      hidden_act
								snake_case__  :						Optional[int]		=      conv_kernel_size
								snake_case__  :						int		=      output_stride
								snake_case__  :						Optional[Any]		=      hidden_dropout_prob
								snake_case__  :						str		=      attention_probs_dropout_prob
								snake_case__  :						Any		=      classifier_dropout_prob
								snake_case__  :						Tuple		=      use_labels
								snake_case__  :						Optional[Any]		=      is_training
								snake_case__  :						Dict		=      num_labels
								snake_case__  :						Dict		=      initializer_range
								snake_case__  :						List[str]		=      scope




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







								'''simple docstring'''




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

								snake_case__  :						Optional[Any]		=      None
								snake_case__  :						int		=      None
								if self.use_labels:
									snake_case__  :						List[str]		=      ids_tensor([self.batch_size]	,					self.num_labels)
									snake_case__  :						Any		=      ids_tensor([self.batch_size, self.image_size, self.image_size]	,					self.num_labels)

								snake_case__  :						Dict		=      self.get_config()

								return config, pixel_values, labels, pixel_labels




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								return MobileViTConfig(
								    image_size=self.image_size	,					patch_size=self.patch_size	,					num_channels=self.num_channels	,					num_attention_heads=self.num_attention_heads	,					hidden_act=self.hidden_act	,					conv_kernel_size=self.conv_kernel_size	,					output_stride=self.output_stride	,					hidden_dropout_prob=self.hidden_dropout_prob	,					attention_probs_dropout_prob=self.attention_probs_dropout_prob	,					classifier_dropout_prob=self.classifier_dropout_prob	,					initializer_range=self.initializer_range	,					)




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







								'''simple docstring'''




								snake_case__  :						Tuple		=      TFMobileViTModel(config=lowerCamelCase__)
								snake_case__  :						int		=      model(lowerCamelCase__	,					training=lowerCamelCase__)
								snake_case__  :						Tuple		=      self.image_size // self.output_stride
								self.parent.assertEqual(
								    result.last_hidden_state.shape	,					(self.batch_size, self.last_hidden_size, expected_height, expected_width))




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







								'''simple docstring'''




								snake_case__  :						Any		=      self.num_labels
								snake_case__  :						Dict		=      TFMobileViTForImageClassification(lowerCamelCase__)
								snake_case__  :						List[Any]		=      model(lowerCamelCase__	,					labels=lowerCamelCase__	,					training=lowerCamelCase__)
								self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.num_labels))




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.num_labels
								snake_case__  :						Any		=      TFMobileViTForSemanticSegmentation(lowerCamelCase__)
								snake_case__  :						List[Any]		=      self.image_size // self.output_stride

								snake_case__  :						Tuple		=      model(lowerCamelCase__	,					training=lowerCamelCase__)
								self.parent.assertEqual(
								    result.logits.shape	,					(self.batch_size, self.num_labels, expected_height, expected_width))

								snake_case__  :						str		=      model(lowerCamelCase__	,					labels=lowerCamelCase__	,					training=lowerCamelCase__)
								self.parent.assertEqual(
								    result.logits.shape	,					(self.batch_size, self.num_labels, expected_height, expected_width))







							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      self.prepare_config_and_inputs()
								snake_case__,					snake_case__,					snake_case__,					snake_case__  :						List[Any]		=      config_and_inputs
								snake_case__  :						Tuple		=      {"pixel_values": pixel_values}
								return config, inputs_dict


@require_tf
class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Optional[int]				  =  (
							    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
							    if is_tf_available()
							    else ()
							)
							__magic_name__					:     str				  =  (
							    {'''feature-extraction''': TFMobileViTModel, '''image-classification''': TFMobileViTForImageClassification}
							    if is_tf_available()
							    else {}
							)

							__magic_name__					:     List[str]				  =  False
							__magic_name__					:     int				  =  False
							__magic_name__					:     Any				  =  False
							__magic_name__					:     Optional[int]				  =  False
							__magic_name__					:     Union[str, Any]				  =  False




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







								'''simple docstring'''




								snake_case__  :						Any		=      TFMobileViTModelTester(self)
								snake_case__  :						Dict		=      TFMobileViTConfigTester(self	,					config_class=lowerCamelCase__	,					has_text_modality=lowerCamelCase__)




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







								'''simple docstring'''




								self.config_tester.run_common_tests()




							@unittest.skip(reason="MobileViT does not use inputs_embeds")
							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								pass




							@unittest.skip(reason="MobileViT does not support input and output embeddings")
							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								pass




							@unittest.skip(reason="MobileViT does not output attentions")
							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								pass




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__,					snake_case__  :						int		=      self.model_tester.prepare_config_and_inputs_for_common()

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

									snake_case__  :						Dict		=      ["pixel_values"]
									self.assertListEqual(arg_names[:1]	,					lowerCamelCase__)




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







								'''simple docstring'''




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




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







								'''simple docstring'''




								def check_hidden_states_output(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__):
									snake_case__  :						List[Any]		=      model_class(lowerCamelCase__)

									snake_case__  :						Optional[int]		=      model(**self._prepare_for_class(lowerCamelCase__	,					lowerCamelCase__))

									snake_case__  :						List[Any]		=      outputs.hidden_states

									snake_case__  :						Union[str, Any]		=      5
									self.assertEqual(len(lowerCamelCase__)	,					lowerCamelCase__)

									# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
									# with the width and height being successively divided by 2.
									snake_case__  :						str		=      2
									for i in range(len(lowerCamelCase__)):
										self.assertListEqual(
										    list(hidden_states[i].shape[-2:])	,					[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]	,					)
										divisor *= 2

									self.assertEqual(self.model_tester.output_stride	,					divisor // 2)

								snake_case__,					snake_case__  :						Union[str, Any]		=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									snake_case__  :						List[Any]		=      True
									check_hidden_states_output(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)

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

									check_hidden_states_output(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)




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







								'''simple docstring'''




								snake_case__  :						Dict		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__)




							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0	,					reason="TF does not support backprop for grouped convolutions on CPU."	,					)
							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								super().test_dataset_conversion()




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__=2E-1	,					lowerCamelCase__=2E-1)   ->	Optional[Any]:







								'''simple docstring'''




								self.assertTrue(np.allclose(lowerCamelCase__	,					lowerCamelCase__	,					atol=lowerCamelCase__	,					rtol=lowerCamelCase__))




							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0	,					reason="TF does not support backprop for grouped convolutions on CPU."	,					)
							@slow
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__,					snake_case__  :						str		=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
									if model_class.__name__ != "TFMobileViTModel":
										snake_case__  :						Dict		=      model_class(lowerCamelCase__)
										if getattr(lowerCamelCase__	,					"hf_compute_loss"	,					lowerCamelCase__):
											super().test_keras_fit()




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__,					snake_case__  :						List[str]		=      self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
									# set an ignore index to correctly test the masked loss used in
									# `TFMobileViTForSemanticSegmentation`.
									if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
										snake_case__  :						Any		=      5

									snake_case__  :						Any		=      model_class(lowerCamelCase__)
									if getattr(lowerCamelCase__	,					"hf_compute_loss"	,					lowerCamelCase__):
										# The number of elements in the loss should be the same as the number of elements in the label
										snake_case__  :						Tuple		=      self._prepare_for_class(inputs_dict.copy()	,					lowerCamelCase__	,					return_labels=lowerCamelCase__)
										snake_case__  :						Optional[Any]		=      prepared_for_class[
										    sorted(prepared_for_class.keys() - inputs_dict.keys()	,					reverse=lowerCamelCase__)[0]
										]
										snake_case__  :						Optional[int]		=      added_label.shape.as_list()[:1]

										# Test that model correctly compute the loss with kwargs
										snake_case__  :						Dict		=      self._prepare_for_class(inputs_dict.copy()	,					lowerCamelCase__	,					return_labels=lowerCamelCase__)
										snake_case__  :						Dict		=      {"input_ids", "pixel_values", "input_features"}
										snake_case__  :						Any		=      possible_input_names.intersection(set(lowerCamelCase__)).pop()
										snake_case__  :						List[str]		=      prepared_for_class.pop(lowerCamelCase__)

										snake_case__  :						Optional[Any]		=      model(lowerCamelCase__	,					**lowerCamelCase__)[0]
										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

										# Test that model correctly compute the loss when we mask some positions
										snake_case__  :						Dict		=      self._prepare_for_class(inputs_dict.copy()	,					lowerCamelCase__	,					return_labels=lowerCamelCase__)
										snake_case__  :						Dict		=      {"input_ids", "pixel_values", "input_features"}
										snake_case__  :						List[Any]		=      possible_input_names.intersection(set(lowerCamelCase__)).pop()
										snake_case__  :						List[Any]		=      prepared_for_class.pop(lowerCamelCase__)
										if "labels" in prepared_for_class:
											snake_case__  :						int		=      prepared_for_class["labels"].numpy()
											if len(labels.shape) > 1 and labels.shape[1] != 1:
												# labels[0] = -100
												snake_case__  :						Union[str, Any]		=      tf.convert_to_tensor(lowerCamelCase__)
												snake_case__  :						Tuple		=      model(lowerCamelCase__	,					**lowerCamelCase__)[0]
												self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
												self.assertTrue(not np.any(np.isnan(loss.numpy())))

                # Test that model correctly compute the loss with a dict
										snake_case__  :						int		=      self._prepare_for_class(inputs_dict.copy()	,					lowerCamelCase__	,					return_labels=lowerCamelCase__)
										snake_case__  :						Union[str, Any]		=      model(lowerCamelCase__)[0]
										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

										# Test that model correctly compute the loss with a tuple
										snake_case__  :						str		=      self._prepare_for_class(inputs_dict.copy()	,					lowerCamelCase__	,					return_labels=lowerCamelCase__)

										# Get keys that were added with the _prepare_for_class function
										snake_case__  :						List[Any]		=      prepared_for_class.keys() - inputs_dict.keys()
										snake_case__  :						str		=      inspect.signature(model.call).parameters
										snake_case__  :						Tuple		=      list(signature.keys())

										# Create a dictionary holding the location of the tensors in the tuple
										snake_case__  :						int		=      {0: input_name}
										for label_key in label_keys:
											snake_case__  :						Tuple		=      signature_names.index(lowerCamelCase__)
											snake_case__  :						Union[str, Any]		=      label_key
										snake_case__  :						Union[str, Any]		=      sorted(tuple_index_mapping.items())
										# Initialize a list with their default values, update the values and convert to a tuple
										snake_case__  :						List[Any]		=      []

										for name in signature_names:
											if name != "kwargs":
												list_input.append(signature[name].default)

										for index, value in sorted_tuple_index_mapping:
											snake_case__  :						Any		=      prepared_for_class[value]

										snake_case__  :						List[str]		=      tuple(lowerCamelCase__)

										# Send to model
										snake_case__  :						int		=      model(tuple_input[:-1])[0]

										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])







							@slow
							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									snake_case__  :						Optional[Any]		=      TFMobileViTModel.from_pretrained(lowerCamelCase__)
									self.assertIsNotNone(lowerCamelCase__)



def 			A__		(					)							->	Optional[Any]:

	'''simple docstring'''


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



@require_tf
class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							@slow
							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small")

								snake_case__  :						List[str]		=      MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small")
								snake_case__  :						Dict		=      prepare_img()
								snake_case__  :						List[str]		=      image_processor(images=lowerCamelCase__	,					return_tensors="tf")

								# forward pass
								snake_case__  :						Union[str, Any]		=      model(**lowerCamelCase__	,					training=lowerCamelCase__)

								# verify the logits
								snake_case__  :						str		=      tf.TensorShape((1, 1_000))
								self.assertEqual(outputs.logits.shape	,					lowerCamelCase__)

								snake_case__  :						List[Any]		=      tf.constant([-1.93_64, -1.23_27, -0.46_53])

								tf.debugging.assert_near(outputs.logits[0, :3]	,					lowerCamelCase__	,					atol=1E-4	,					rtol=1E-04)







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







								'''simple docstring'''




								snake_case__  :						Dict		=      TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")

								snake_case__  :						Any		=      MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")

								snake_case__  :						List[str]		=      prepare_img()
								snake_case__  :						Dict		=      image_processor(images=lowerCamelCase__	,					return_tensors="tf")

								# forward pass
								snake_case__  :						str		=      model(inputs.pixel_values	,					training=lowerCamelCase__)
								snake_case__  :						Dict		=      outputs.logits

								# verify the logits
								snake_case__  :						Union[str, Any]		=      tf.TensorShape((1, 21, 32, 32))
								self.assertEqual(logits.shape	,					lowerCamelCase__)

								snake_case__  :						List[Any]		=      tf.constant(
								    [
								        [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
								        [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
								        [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
								    ])

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





