

import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCAmelCase__								=		getLogger(__name__)


lowerCAmelCase__								=		"""cuda""" if torch.cuda.is_available() else """cpu"""






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     int = 8							,       UpperCAmelCase_							:     str = DEFAULT_DEVICE							,       UpperCAmelCase_							:     str=False							,       UpperCAmelCase_							:     Union[str, Any]="summarization"							,       UpperCAmelCase_							:     Tuple=None							,       **UpperCAmelCase_							:     Dict							,       ):




	'''simple docstring'''




	_UpperCamelCase   :		List[str]			=						Path(__A	).open('w'							,       encoding='utf-8'	)
	_UpperCamelCase   :		int			=						str(__A	)
	_UpperCamelCase   :		Optional[Any]			=						AutoModelForSeqaSeqLM.from_pretrained(__A	).to(__A	)
	if fpaa:
		_UpperCamelCase   :		List[str]			=						model.half()

	_UpperCamelCase   :		str			=						AutoTokenizer.from_pretrained(__A	)
	logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''	)  # if this is wrong, check config.model_type.

	_UpperCamelCase   :		Optional[int]			=						time.time()
	# update config with task specific params
	use_task_specific_params(__A							,       __A	)
	if prefix is None:
		_UpperCamelCase   :		str			=						prefix or getattr(model.config							,       'prefix'							,       ''	) or ''''''
	for examples_chunk in tqdm(list(chunks(__A							,       __A	)	)	):
		_UpperCamelCase   :		Any			=						[prefix + text for text in examples_chunk]
		_UpperCamelCase   :		Optional[int]			=						tokenizer(__A							,       return_tensors='pt'							,       truncation=__A							,       padding='longest'	).to(__A	)
		_UpperCamelCase   :		List[str]			=						model.generate(
		    input_ids=batch.input_ids							,       attention_mask=batch.attention_mask							,       **__A							,       )
		_UpperCamelCase   :		Dict			=						tokenizer.batch_decode(__A							,       skip_special_tokens=__A							,       clean_up_tokenization_spaces=__A	)
		for hypothesis in dec:
			fout.write(hypothesis + '\n'	)
			fout.flush()
	fout.close()
	_UpperCamelCase   :		Optional[int]			=						int(time.time() - start_time	)  # seconds
	_UpperCamelCase   :		List[Any]			=						len(__A	)
	return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,       4	)}






def 					lowerCamelCase_     (							):




	'''simple docstring'''




	return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple=True	):




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						argparse.ArgumentParser()
	parser.add_argument('model_name'							,       type=__A							,       help='like facebook/bart-large-cnn,t5-base, etc.'	)
	parser.add_argument('input_path'							,       type=__A							,       help='like cnn_dm/test.source'	)
	parser.add_argument('save_path'							,       type=__A							,       help='where to save summaries'	)
	parser.add_argument('--reference_path'							,       type=__A							,       required=__A							,       help='like cnn_dm/test.target'	)
	parser.add_argument('--score_path'							,       type=__A							,       required=__A							,       default='metrics.json'							,       help='where to save metrics'	)
	parser.add_argument('--device'							,       type=__A							,       required=__A							,       default=__A							,       help='cuda, cuda:1, cpu etc.'	)
	parser.add_argument(
	    '--prefix'							,       type=__A							,       required=__A							,       default=__A							,       help='will be added to the begininng of src examples'	)
	parser.add_argument('--task'							,       type=__A							,       default='summarization'							,       help='used for task_specific_params + metrics'	)
	parser.add_argument('--bs'							,       type=__A							,       default=8							,       required=__A							,       help='batch size'	)
	parser.add_argument(
	    '--n_obs'							,       type=__A							,       default=-1							,       required=__A							,       help='How many observations. Defaults to all.'	)
	parser.add_argument('--fp16'							,       action='store_true'	)
	parser.add_argument('--dump-args'							,       action='store_true'							,       help='print the custom hparams with the results'	)
	parser.add_argument(
	    '--info'							,       nargs='?'							,       type=__A							,       const=datetime_now()							,       help=(
	        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
	        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
	    )							,       )
	# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
	_UpperCamelCase   :		Dict			=						parser.parse_known_args()
	_UpperCamelCase   :		Any			=						parse_numeric_n_bool_cl_kwargs(__A	)
	if parsed_args and verbose:
		print(F'''parsed the following generate kwargs: {parsed_args}'''	)
	_UpperCamelCase   :		Tuple			=						[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path	).readlines()]
	if args.n_obs > 0:
		_UpperCamelCase   :		List[str]			=						examples[: args.n_obs]
	Path(args.save_path	).parent.mkdir(exist_ok=__A	)

	if args.reference_path is None and Path(args.score_path	).exists():
		warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''	)

	if args.device == "cpu" and args.fpaa:
		# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
		raise ValueError('Can\'t mix --fp16 and --device cpu'	)

	_UpperCamelCase   :		Union[str, Any]			=						generate_summaries_or_translations(
	    __A							,       args.save_path							,       args.model_name							,       batch_size=args.bs							,       device=args.device							,       fpaa=args.fpaa							,       task=args.task							,       prefix=args.prefix							,       **__A							,       )

	if args.reference_path is None:
		return {}

	# Compute scores
	_UpperCamelCase   :		int			=						calculate_bleu if '''translation''' in args.task else calculate_rouge
	_UpperCamelCase   :		List[str]			=						[x.rstrip() for x in open(args.save_path	).readlines()]
	_UpperCamelCase   :		List[Any]			=						[x.rstrip() for x in open(args.reference_path	).readlines()][: len(__A	)]
	_UpperCamelCase   :		dict			=						score_fn(__A							,       __A	)
	scores.update(__A	)

	if args.dump_args:
		scores.update(__A	)
	if args.info:
		_UpperCamelCase   :		Tuple			=						args.info

	if verbose:
		print(__A	)

	if args.score_path is not None:
		json.dump(__A							,       open(args.score_path							,       'w'	)	)

	return scores


if __name__ == "__main__":
							# Usage for MT:
							# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
							run_generate(verbose=True)







import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
							from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image







class 	lowercase    :


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case=3	,       __snake_case=32	,       __snake_case=3	,       __snake_case=10	,       __snake_case=[8, 16, 32, 64]	,       __snake_case=[1, 1, 2, 1]	,       __snake_case=True	,       __snake_case=True	,       __snake_case="relu"	,       __snake_case=3	,       __snake_case=None	,       __snake_case=["stage2", "stage3", "stage4"]	,       __snake_case=[2, 3, 4]	,       __snake_case=1	,       ):
			_UpperCamelCase   :		List[Any]			=						parent
			_UpperCamelCase   :		Dict			=						batch_size
			_UpperCamelCase   :		Optional[int]			=						image_size
			_UpperCamelCase   :		str			=						num_channels
			_UpperCamelCase   :		Optional[Any]			=						embeddings_size
			_UpperCamelCase   :		Tuple			=						hidden_sizes
			_UpperCamelCase   :		Dict			=						depths
			_UpperCamelCase   :		str			=						is_training
			_UpperCamelCase   :		Optional[int]			=						use_labels
			_UpperCamelCase   :		str			=						hidden_act
			_UpperCamelCase   :		Optional[int]			=						num_labels
			_UpperCamelCase   :		Optional[int]			=						scope
			_UpperCamelCase   :		Tuple			=						len(__snake_case)
			_UpperCamelCase   :		Dict			=						out_features
			_UpperCamelCase   :		Union[str, Any]			=						out_indices
			_UpperCamelCase   :		int			=						num_groups


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			_UpperCamelCase   :		str			=						None
			if self.use_labels:
				_UpperCamelCase   :		Optional[int]			=						ids_tensor([self.batch_size]	,       self.num_labels)

			_UpperCamelCase   :		str			=						self.get_config()

			return config, pixel_values, labels


		def 		A__					(				self):
			return BitConfig(
			    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	,       out_features=self.out_features	,       out_indices=self.out_indices	,       num_groups=self.num_groups	,       )


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		str			=						BitModel(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Optional[Any]			=						model(__snake_case)
			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	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Dict			=						self.num_labels
			_UpperCamelCase   :		Dict			=						BitForImageClassification(__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Dict			=						model(__snake_case	,       labels=__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.num_labels))


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Optional[Any]			=						BitBackbone(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		List[Any]			=						model(__snake_case)

			# verify feature maps
			self.parent.assertEqual(len(result.feature_maps)	,       len(config.out_features))
			self.parent.assertListEqual(list(result.feature_maps[0].shape)	,       [self.batch_size, self.hidden_sizes[1], 4, 4])

			# verify channels
			self.parent.assertEqual(len(model.channels)	,       len(config.out_features))
			self.parent.assertListEqual(model.channels	,       config.hidden_sizes[1:])

			# verify backbone works with out_features=None
			_UpperCamelCase   :		Any			=						None
			_UpperCamelCase   :		str			=						BitBackbone(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Any			=						model(__snake_case)

			# verify feature maps
			self.parent.assertEqual(len(result.feature_maps)	,       1)
			self.parent.assertListEqual(list(result.feature_maps[0].shape)	,       [self.batch_size, self.hidden_sizes[-1], 1, 1])

			# verify channels
			self.parent.assertEqual(len(model.channels)	,       1)
			self.parent.assertListEqual(model.channels	,       [config.hidden_sizes[-1]])






		def 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						self.prepare_config_and_inputs()
			_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		int			=						config_and_inputs
			_UpperCamelCase   :		int			=						{'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class 	lowercase    (				_lowercase    ,							_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
		a__					       =  (
		    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
		    if is_torch_available()
		    else {}
		)

		a__					       =  False
		a__					       =  False
		a__					       =  False
		a__					       =  False
		a__					       =  False


		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						BitModelTester(self)
			_UpperCamelCase   :		Optional[Any]			=						ConfigTester(self	,       config_class=__snake_case	,       has_text_modality=__snake_case)


		def 		A__					(				self):
			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):
			return


		@unittest.skip(reason='Bit does not output attentions')
		def 		A__					(				self):
			pass


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


		@unittest.skip(reason='Bit does not support input and output embeddings')
		def 		A__					(				self):
			pass


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

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

				_UpperCamelCase   :		List[Any]			=						['pixel_values']
				self.assertListEqual(arg_names[:1]	,       __snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_backbone(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=						self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase   :		Union[str, Any]			=						model_class(config=__snake_case)
				for name, module in model.named_modules():
					if isinstance(__snake_case	,       (nn.BatchNormad, nn.GroupNorm)):
						self.assertTrue(
						    torch.all(module.weight == 1)	,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''	,       )
						self.assertTrue(
						    torch.all(module.bias == 0)	,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''	,       )


		def 		A__					(				self):
			def check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case):
				_UpperCamelCase   :		str			=						model_class(__snake_case)
				model.to(__snake_case)
				model.eval()

				with torch.no_grad():
					_UpperCamelCase   :		Union[str, Any]			=						model(**self._prepare_for_class(__snake_case	,       __snake_case))

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

				_UpperCamelCase   :		str			=						self.model_tester.num_stages
				self.assertEqual(len(__snake_case)	,       expected_num_stages + 1)

				# Bit's feature maps are of shape (batch_size, num_channels, height, width)
				self.assertListEqual(
				    list(hidden_states[0].shape[-2:])	,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,       )

			_UpperCamelCase	,		_UpperCamelCase   :		int			=						self.model_tester.prepare_config_and_inputs_for_common()
			_UpperCamelCase   :		List[str]			=						['preactivation', 'bottleneck']
			for model_class in self.all_model_classes:
				for layer_type in layers_type:
					_UpperCamelCase   :		Any			=						layer_type
					_UpperCamelCase   :		Tuple			=						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"]
					_UpperCamelCase   :		List[str]			=						True

					check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case)


		@unittest.skip(reason='Bit does not use feedforward chunking')
		def 		A__					(				self):
			pass


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__snake_case)






		@slow
		def 		A__					(				self):
			for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				_UpperCamelCase   :		Optional[Any]			=						BitModel.from_pretrained(__snake_case)
				self.assertIsNotNone(__snake_case)






def 					lowerCamelCase_     (							)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		List[Any]			=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image







@require_torch
@require_vision
class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		@cached_property
		def 		A__					(				self):
			return (
			    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
			)






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(__snake_case)

			_UpperCamelCase   :		str			=						self.default_image_processor
			_UpperCamelCase   :		List[str]			=						prepare_img()
			_UpperCamelCase   :		int			=						image_processor(images=__snake_case	,       return_tensors='pt').to(__snake_case)

			# forward pass
			with torch.no_grad():
				_UpperCamelCase   :		Any			=						model(**__snake_case)

			# verify the logits
			_UpperCamelCase   :		Dict			=						torch.Size((1, 10_00))
			self.assertEqual(outputs.logits.shape	,       __snake_case)

			_UpperCamelCase   :		Optional[int]			=						torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]).to(__snake_case)

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







@require_torch
class 	lowercase    (				_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  (BitBackbone,) if is_torch_available() else ()
		a__					       =  BitConfig

		a__					       =  False


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						BitModelTester(self)




