



import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor


class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






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






								"""simple docstring"""


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



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								A__       =    self.get_config()

								return config, pixel_values



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								return RegNetConfig(
								    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     _a							(     self					:       Any				,					_snake_case					:       Any				,					_snake_case					:       Tuple					):






								"""simple docstring"""


								A__       =    FlaxRegNetModel(config=snake_case_					)
								A__       =    model(snake_case_					)

								# Output shape (b, c, h, w)
								self.parent.assertEqual(
								    result.last_hidden_state.shape				,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,					)



					def     _a							(     self					:       int				,					_snake_case					:       List[str]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    self.num_labels
								A__       =    FlaxRegNetForImageClassification(config=snake_case_					)
								A__       =    model(snake_case_					)
								self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.num_labels)					)



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


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







@require_flax
class      __lowerCAmelCase     (		_a							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		List[str]			       =    (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

					A__				:		List[Any]			       =    False
					A__				:		Optional[Any]			       =    False
					A__				:		int			       =    False



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    FlaxRegNetModelTester(self					)
								A__       =    ConfigTester(self				,					config_class=snake_case_				,					has_text_modality=snake_case_					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()



					def     _a							(     self					:       int					):






								"""simple docstring"""


								return



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case_					)



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*snake_case_					)



					@unittest.skip(reason='RegNet does not use inputs_embeds'					)
					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								pass



					@unittest.skip(reason='RegNet does not support input and output embeddings'					)
					def     _a							(     self					:       Any					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

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

											A__       =    ['pixel_values']
											self.assertListEqual(arg_names[:1]				,					snake_case_					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								def check_hidden_states_output(_snake_case					:       List[str]				,					_snake_case					:       Optional[int]				,					_snake_case					:       Optional[Any]					):
											A__       =    model_class(snake_case_					)

											A__       =    model(**self._prepare_for_class(snake_case_				,					snake_case_					)					)

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

											A__       =    self.model_tester.num_stages
											self.assertEqual(len(snake_case_					)				,					expected_num_stages + 1					)

								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__       =    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"]
											A__       =    True

											check_hidden_states_output(snake_case_				,					snake_case_				,					snake_case_					)



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__					):
														A__       =    self._prepare_for_class(snake_case_				,					snake_case_					)
														A__       =    model_class(snake_case_					)

														@jax.jit
														def model_jitted(_snake_case					:       str				,					**_snake_case					:       str					):
																	return model(pixel_values=snake_case_				,					**snake_case_					)

														with self.subTest('JIT Enabled'					):
																	A__       =    model_jitted(**snake_case_					).to_tuple()

														with self.subTest('JIT Disabled'					):
																	with jax.disable_jit():
																				A__       =    model_jitted(**snake_case_					).to_tuple()

														self.assertEqual(len(snake_case_					)				,					len(snake_case_					)					)
														for jitted_output, output in zip(snake_case_				,					snake_case_					):
																	self.assertEqual(jitted_output.shape				,					output.shape					)







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






@require_flax
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@cached_property
					def     _a							(     self					:       int					):






								"""simple docstring"""


								return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'					) if is_vision_available() else None



					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'					)

								A__       =    self.default_image_processor
								A__       =    prepare_img()
								A__       =    image_processor(images=snake_case_				,					return_tensors='np'					)

								A__       =    model(**snake_case_					)

								# verify the logits
								A__       =    (1, 10_00)
								self.assertEqual(outputs.logits.shape				,					snake_case_					)

								A__       =    jnp.array([-0.4180, -1.5051, -3.4836]					)

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









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

transformers_logging.set_verbosity_info()


def A						(       __UpperCamelCase					)						->      Tuple:
			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 A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      List[Any]:
			return max(metric_fn(__UpperCamelCase ,	__UpperCamelCase					) for gt in ground_truths					)


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      List[str]:
			A__       =    [line.strip() for line in open(__UpperCamelCase ,	'r'					).readlines()]
			A__       =    []

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

			A__       =    A__       =    A__       =    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					)

			A__       =    100.0 * em / total
			A__       =    100.0 * fa / total

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Optional[int]:
			A__       =    args.k
			A__       =    [line.strip() for line in open(__UpperCamelCase ,	'r'					).readlines()]
			A__       =    [line.strip() for line in open(__UpperCamelCase ,	'r'					).readlines()]

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

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Union[str, Any]:
			def strip_title(__UpperCamelCase					):
						if title.startswith('"'					):
									A__       =    title[1:]
						if title.endswith('"'					):
									A__       =    title[:-1]
						return title

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

			A__       =    rag_model.rag.question_encoder(__UpperCamelCase					)
			A__       =    question_enc_outputs[0]

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Optional[Any]:
			with torch.no_grad():
						A__       =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    __UpperCamelCase ,	return_tensors='pt' ,	padding=__UpperCamelCase ,	truncation=__UpperCamelCase					)

						A__       =    inputs_dict.input_ids.to(args.device					)
						A__       =    inputs_dict.attention_mask.to(args.device					)
						A__       =    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]] ,	)
						A__       =    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 A						(       )						->      Any:
			A__       =    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.' ,	)
			A__       =    parser.parse_args()
			A__       =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)
			return args


def A						(       __UpperCamelCase					)						->      int:
			A__       =    {}
			if args.model_type is None:
						A__       =    infer_model_type(args.model_name_or_path					)
						assert args.model_type is not None
			if args.model_type.startswith('rag'					):
						A__       =    RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
						A__       =    args.n_docs
						if args.index_name is not None:
									A__       =    args.index_name
						if args.index_path is not None:
									A__       =    args.index_path
			else:
						A__       =    BartForConditionalGeneration

			A__       =    (
			    [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					)

			A__       =    get_scores if args.eval_mode == 'e2e' else get_precision_at_k
			A__       =    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'					):
									A__       =    RagRetriever.from_pretrained(__UpperCamelCase ,	**__UpperCamelCase					)
									A__       =    model_class.from_pretrained(__UpperCamelCase ,	retriever=__UpperCamelCase ,	**__UpperCamelCase					)
									model.retriever.init_retrieval()
						else:
									A__       =    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:
									A__       =    []
									for line in tqdm(__UpperCamelCase					):
												questions.append(line.strip()					)
												if len(__UpperCamelCase					) == args.eval_batch_size:
															A__       =    evaluate_batch_fn(__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)
															preds_file.write('\n'.join(__UpperCamelCase					) + '\n'					)
															preds_file.flush()
															A__       =    []
									if len(__UpperCamelCase					) > 0:
												A__       =    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__":
							SCREAMING_SNAKE_CASE__       =      get_args()
							main(args)




