




import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
						from transformers import (
						    FlaxBertModel,
						    FlaxCLIPVisionModel,
						    FlaxVisionTextDualEncoderModel,
						    FlaxViTModel,
						    VisionTextDualEncoderConfig,
						    VisionTextDualEncoderProcessor,
						)
						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)


if is_torch_available():
						import torch

						from transformers import VisionTextDualEncoderModel

if is_vision_available():
						from PIL import Image





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  )       ->  Dict:







			"""simple docstring"""





			if isinstance(lowerCamelCase_  ,							collections.abc.Iterable  ):
						return x
			return (x, x)







@require_flax
class 				lowerCAmelCase_       :






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ ):
							pass






				def 		snake_case	(			self ):
							pass






				def 		snake_case	(			self ):
							pass






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : int					  =      np.abs((a - b) ).max()
							self.assertLessEqual(snake_case__      ,snake_case__      ,F'Difference between torch and flax is {diff} (>= {tol}).' )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__=None      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      VisionTextDualEncoderConfig.from_vision_text_configs(snake_case__      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : str					  =      FlaxVisionTextDualEncoderModel(snake_case__ )

							SCREAMING_SNAKE_CASE_   : int					  =      model(input_ids=snake_case__      ,pixel_values=snake_case__      ,attention_mask=snake_case__ )

							self.assertEqual(output['text_embeds'].shape      ,(input_ids.shape[0], config.projection_dim) )
							self.assertEqual(output['image_embeds'].shape      ,(pixel_values.shape[0], config.projection_dim) )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__=None      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : int					  =      self.get_vision_text_model(snake_case__      ,snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      {'vision_model': vision_model, 'text_model': text_model}
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case__ )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      model(input_ids=snake_case__      ,pixel_values=snake_case__      ,attention_mask=snake_case__ )

							self.assertEqual(output['text_embeds'].shape      ,(input_ids.shape[0], model.config.projection_dim) )
							self.assertEqual(output['image_embeds'].shape      ,(pixel_values.shape[0], model.config.projection_dim) )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__=None      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.get_vision_text_model(snake_case__      ,snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      {'vision_model': vision_model, 'text_model': text_model}
							SCREAMING_SNAKE_CASE_   : str					  =      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case__ )

							SCREAMING_SNAKE_CASE_   : Any					  =      model(input_ids=snake_case__      ,pixel_values=snake_case__      ,attention_mask=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      output[0]

							with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(snake_case__ )
										SCREAMING_SNAKE_CASE_   : str					  =      FlaxVisionTextDualEncoderModel.from_pretrained(snake_case__ )

										SCREAMING_SNAKE_CASE_   : int					  =      model(input_ids=snake_case__      ,pixel_values=snake_case__      ,attention_mask=snake_case__ )
										SCREAMING_SNAKE_CASE_   : int					  =      after_output[0]
										SCREAMING_SNAKE_CASE_   : Tuple					  =      np.amax(np.abs(out_a - out_a ) )
										self.assertLessEqual(snake_case__      ,1E-3 )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__=None      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.get_vision_text_model(snake_case__      ,snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      {'vision_model': vision_model, 'text_model': text_model}
							SCREAMING_SNAKE_CASE_   : int					  =      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      model(
							    input_ids=snake_case__      ,pixel_values=snake_case__      ,attention_mask=snake_case__      ,output_attentions=snake_case__ )

							SCREAMING_SNAKE_CASE_   : int					  =      output.vision_model_output.attentions
							self.assertEqual(len(snake_case__ )      ,vision_config.num_hidden_layers )

							# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
							SCREAMING_SNAKE_CASE_   : Tuple					  =      to_atuple(vision_model.config.image_size )
							SCREAMING_SNAKE_CASE_   : int					  =      to_atuple(vision_model.config.patch_size )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							SCREAMING_SNAKE_CASE_   : str					  =      num_patches + 1
							self.assertEqual(vision_attentions[0].shape[-3:]      ,(vision_config.num_attention_heads, seq_len, seq_len) )

							SCREAMING_SNAKE_CASE_   : str					  =      output.text_model_output.attentions
							self.assertEqual(len(snake_case__ )      ,text_config.num_hidden_layers )

							self.assertEqual(
							    text_attentions[0].shape[-3:]      ,(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])      ,)






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ ):
							pt_model.to(snake_case__ )
							pt_model.eval()

							# prepare inputs
							SCREAMING_SNAKE_CASE_   : int					  =      inputs_dict
							SCREAMING_SNAKE_CASE_   : Tuple					  =      {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}

							with torch.no_grad():
										SCREAMING_SNAKE_CASE_   : List[str]					  =      pt_model(**snake_case__ ).to_tuple()

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      fx_model(**snake_case__ ).to_tuple()
							self.assertEqual(len(snake_case__ )      ,len(snake_case__ )      ,'Output lengths differ between Flax and PyTorch' )
							for fx_output, pt_output in zip(fx_outputs[:4]      ,pt_outputs[:4] ):
										self.assert_almost_equals(snake_case__      ,pt_output.numpy()      ,4E-2 )

							# PT -> Flax
							with tempfile.TemporaryDirectory() as tmpdirname:
										pt_model.save_pretrained(snake_case__ )
										SCREAMING_SNAKE_CASE_   : Any					  =      FlaxVisionTextDualEncoderModel.from_pretrained(snake_case__      ,from_pt=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      fx_model_loaded(**snake_case__ ).to_tuple()
							self.assertEqual(len(snake_case__ )      ,len(snake_case__ )      ,'Output lengths differ between Flax and PyTorch' )
							for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]      ,pt_outputs[:4] ):
										self.assert_almost_equals(snake_case__      ,pt_output.numpy()      ,4E-2 )

							# Flax -> PT
							with tempfile.TemporaryDirectory() as tmpdirname:
										fx_model.save_pretrained(snake_case__ )
										SCREAMING_SNAKE_CASE_   : str					  =      VisionTextDualEncoderModel.from_pretrained(snake_case__      ,from_flax=snake_case__ )

							pt_model_loaded.to(snake_case__ )
							pt_model_loaded.eval()

							with torch.no_grad():
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      pt_model_loaded(**snake_case__ ).to_tuple()

							self.assertEqual(len(snake_case__ )      ,len(snake_case__ )      ,'Output lengths differ between Flax and PyTorch' )
							for fx_output, pt_output_loaded in zip(fx_outputs[:4]      ,pt_outputs_loaded[:4] ):
										self.assert_almost_equals(snake_case__      ,pt_output_loaded.numpy()      ,4E-2 )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      VisionTextDualEncoderConfig.from_vision_text_configs(snake_case__      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : int					  =      VisionTextDualEncoderModel(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      FlaxVisionTextDualEncoderModel(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,snake_case__ )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      fx_state

							self.check_pt_flax_equivalence(snake_case__      ,snake_case__      ,snake_case__ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      VisionTextDualEncoderConfig.from_vision_text_configs(snake_case__      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      VisionTextDualEncoderModel(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      FlaxVisionTextDualEncoderModel(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      load_flax_weights_in_pytorch_model(snake_case__      ,fx_model.params )

							self.check_pt_flax_equivalence(snake_case__      ,snake_case__      ,snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.prepare_config_and_inputs()
							self.check_model_from_pretrained_configs(**snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.prepare_config_and_inputs()
							self.check_vision_text_dual_encoder_from_pretrained(**snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.prepare_config_and_inputs()
							self.check_save_load(**snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      self.prepare_config_and_inputs()
							self.check_vision_text_output_attention(**snake_case__ )






				@is_pt_flax_cross_test
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      config_inputs_dict.pop('vision_config' )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      config_inputs_dict.pop('text_config' )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      config_inputs_dict

							self.check_equivalence_pt_to_flax(snake_case__      ,snake_case__      ,snake_case__ )
							self.check_equivalence_flax_to_pt(snake_case__      ,snake_case__      ,snake_case__ )






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.get_pretrained_model_and_inputs()

							SCREAMING_SNAKE_CASE_   : Any					  =      model_a(**snake_case__ )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      outputs[0]

							with tempfile.TemporaryDirectory() as tmp_dirname:
										model_a.save_pretrained(snake_case__ )
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      FlaxVisionTextDualEncoderModel.from_pretrained(snake_case__ )

										SCREAMING_SNAKE_CASE_   : List[str]					  =      model_a(**snake_case__ )
										SCREAMING_SNAKE_CASE_   : int					  =      after_outputs[0]
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      np.amax(np.abs(out_a - out_a ) )
										self.assertLessEqual(snake_case__      ,1E-5 )







@require_flax
class 				lowerCAmelCase_       (					lowerCamelCase_						,     unittest.TestCase							):






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
							    'hf-internal-testing/tiny-random-vit'      ,'hf-internal-testing/tiny-bert'      ,vision_from_pt=snake_case__      ,text_from_pt=snake_case__      ,)
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      13
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      floats_tensor(
							    [
							        batch_size,
							        model.config.vision_config.num_channels,
							        model.config.vision_config.image_size,
							        model.config.vision_config.image_size,
							    ] )
							SCREAMING_SNAKE_CASE_   : Any					  =      ids_tensor([batch_size, 4]      ,model.config.text_config.vocab_size )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      random_attention_mask([batch_size, 4] )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

							return model, inputs






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : int					  =      FlaxViTModel(snake_case__ )
							SCREAMING_SNAKE_CASE_   : str					  =      FlaxBertModel(snake_case__ )
							return vision_model, text_model






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      FlaxViTModelTester(self )
							SCREAMING_SNAKE_CASE_   : int					  =      FlaxBertModelTester(self )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      vit_model_tester.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE_   : List[str]					  =      bert_model_tester.prepare_config_and_inputs()

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Tuple					  =      vision_config_and_inputs

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      text_config_and_inputs

							# make sure that cross attention layers are added
							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": attention_mask,
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							}







@require_torch
class 				lowerCAmelCase_       (					lowerCamelCase_						,     unittest.TestCase							):






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
							    'hf-internal-testing/tiny-random-clip'      ,'hf-internal-testing/tiny-bert'      ,vision_from_pt=snake_case__      ,text_from_pt=snake_case__      ,)
							SCREAMING_SNAKE_CASE_   : Any					  =      13
							SCREAMING_SNAKE_CASE_   : str					  =      floats_tensor(
							    [
							        batch_size,
							        model.config.vision_config.num_channels,
							        model.config.vision_config.image_size,
							        model.config.vision_config.image_size,
							    ] )
							SCREAMING_SNAKE_CASE_   : Dict					  =      ids_tensor([batch_size, 4]      ,model.config.text_config.vocab_size )
							SCREAMING_SNAKE_CASE_   : int					  =      random_attention_mask([batch_size, 4] )
							SCREAMING_SNAKE_CASE_   : Any					  =      {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

							return model, inputs






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : int					  =      FlaxCLIPVisionModel(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Dict					  =      FlaxBertModel(snake_case__ )
							return vision_model, text_model






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      FlaxCLIPVisionModelTester(self )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      FlaxBertModelTester(self )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      clip_model_tester.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE_   : Tuple					  =      bert_model_tester.prepare_config_and_inputs()

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      vision_config_and_inputs

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : str					  =      text_config_and_inputs

							# make sure that cross attention layers are added
							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": attention_mask,
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							}







@require_flax
@require_vision
class 				lowerCAmelCase_       (					unittest.TestCase							):






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian'      ,logit_scale_init_value=1.0 )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian' )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      processor(
							    text=['una foto di un gatto', 'una foto di un cane']      ,images=snake_case__      ,padding=snake_case__      ,return_tensors='np' )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      model(**snake_case__ )

							# verify the logits
							self.assertEqual(outputs.logits_per_image.shape      ,(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
							self.assertEqual(
							    outputs.logits_per_text.shape      ,(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])      ,)

							SCREAMING_SNAKE_CASE_   : Dict					  =      np.array([[1.2284727, 0.3104122]] )

							self.assertTrue(np.allclose(outputs.logits_per_image      ,snake_case__      ,atol=1E-3 ) )









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


UpperCamelCase__ :   Tuple						=							getLogger(__name__)


UpperCamelCase__ :   Dict						=							'''cuda''' if torch.cuda.is_available() else '''cpu'''





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







			"""simple docstring"""





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

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

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





def        __UpperCAmelCase						(							)       ->  int:







			"""simple docstring"""





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





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict=True  )       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      argparse.ArgumentParser()
			parser.add_argument('model_name'  ,							type=lowerCamelCase_  ,							help='like facebook/bart-large-cnn,t5-base, etc.'  )
			parser.add_argument('input_path'  ,							type=lowerCamelCase_  ,							help='like cnn_dm/test.source'  )
			parser.add_argument('save_path'  ,							type=lowerCamelCase_  ,							help='where to save summaries'  )
			parser.add_argument('--reference_path'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							help='like cnn_dm/test.target'  )
			parser.add_argument('--score_path'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							default='metrics.json'  ,							help='where to save metrics'  )
			parser.add_argument('--device'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							default=lowerCamelCase_  ,							help='cuda, cuda:1, cpu etc.'  )
			parser.add_argument(
			    '--prefix'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							default=lowerCamelCase_  ,							help='will be added to the begininng of src examples'  )
			parser.add_argument('--task'  ,							type=lowerCamelCase_  ,							default='summarization'  ,							help='used for task_specific_params + metrics'  )
			parser.add_argument('--bs'  ,							type=lowerCamelCase_  ,							default=8  ,							required=lowerCamelCase_  ,							help='batch size'  )
			parser.add_argument(
			    '--n_obs'  ,							type=lowerCamelCase_  ,							default=-1  ,							required=lowerCamelCase_  ,							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=lowerCamelCase_  ,							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
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      parser.parse_known_args()
			SCREAMING_SNAKE_CASE_   : str					  =      parse_numeric_n_bool_cl_kwargs(lowerCamelCase_  )
			if parsed_args and verbose:
						print(F'parsed the following generate kwargs: {parsed_args}'  )
			SCREAMING_SNAKE_CASE_   : int					  =      [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path  ).readlines()]
			if args.n_obs > 0:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      examples[: args.n_obs]
			Path(args.save_path  ).parent.mkdir(exist_ok=lowerCamelCase_  )

			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'  )

			SCREAMING_SNAKE_CASE_   : List[str]					  =      generate_summaries_or_translations(
			    lowerCamelCase_  ,							args.save_path  ,							args.model_name  ,							batch_size=args.bs  ,							device=args.device  ,							fpaa=args.fpaa  ,							task=args.task  ,							prefix=args.prefix  ,							**lowerCamelCase_  ,							)

			if args.reference_path is None:
						return {}

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

			if args.dump_args:
						scores.update(lowerCamelCase_  )
			if args.info:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      args.info

			if verbose:
						print(lowerCamelCase_  )

			if args.score_path is not None:
						json.dump(lowerCamelCase_  ,							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)



