
"""simple docstring"""
import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
			from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import GLPNImageProcessor




class  _snake_case	(    _lowercase							):






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												__a:    int		     =       self.config_class(**self.inputs_dict     )
												self.parent.assertTrue(hasattr(snake_case__    ,			"""hidden_sizes"""     )     )
												self.parent.assertTrue(hasattr(snake_case__    ,			"""num_attention_heads"""     )     )
												self.parent.assertTrue(hasattr(snake_case__    ,			"""num_encoder_blocks"""     )     )






class  _snake_case	:






							def __init__(       self    :      List[Any]    ,			snake_case__    :      Tuple    ,			snake_case__    :      Tuple=1_3    ,			snake_case__    :      Optional[Any]=6_4    ,			snake_case__    :      Tuple=3    ,			snake_case__    :      Tuple=4    ,			snake_case__    :      Union[str, Any]=[2, 2, 2, 2]    ,			snake_case__    :      str=[8, 4, 2, 1]    ,			snake_case__    :      str=[1_6, 3_2, 6_4, 1_2_8]    ,			snake_case__    :      str=[1, 4, 8, 1_6]    ,			snake_case__    :      Tuple=[1, 2, 4, 8]    ,			snake_case__    :      List[Any]=True    ,			snake_case__    :      Union[str, Any]=True    ,			snake_case__    :      Tuple="gelu"    ,			snake_case__    :      str=0.1    ,			snake_case__    :      List[str]=0.1    ,			snake_case__    :      Union[str, Any]=0.02    ,			snake_case__    :      Optional[Any]=1_6    ,			snake_case__    :      str=3    ,			snake_case__    :      Optional[Any]=None    ,			):






												"""simple docstring"""



												__a:    List[str]		     =       parent
												__a:    Any		     =       batch_size
												__a:    Tuple		     =       image_size
												__a:    List[str]		     =       num_channels
												__a:    str		     =       num_encoder_blocks
												__a:    Tuple		     =       sr_ratios
												__a:    int		     =       depths
												__a:    List[Any]		     =       hidden_sizes
												__a:    List[Any]		     =       downsampling_rates
												__a:    Tuple		     =       num_attention_heads
												__a:    Optional[int]		     =       is_training
												__a:    int		     =       use_labels
												__a:    str		     =       hidden_act
												__a:    Optional[Any]		     =       hidden_dropout_prob
												__a:    Dict		     =       attention_probs_dropout_prob
												__a:    Dict		     =       initializer_range
												__a:    int		     =       decoder_hidden_size
												__a:    Tuple		     =       num_labels
												__a:    int		     =       scope






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    Tuple		     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

												__a:    Union[str, Any]		     =       None
												if self.use_labels:
																	__a:    List[Any]		     =       ids_tensor([self.batch_size, self.image_size, self.image_size]    ,			self.num_labels     )

												__a:    Optional[Any]		     =       self.get_config()
												return config, pixel_values, labels






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												return GLPNConfig(
												    image_size=self.image_size    ,			num_channels=self.num_channels    ,			num_encoder_blocks=self.num_encoder_blocks    ,			depths=self.depths    ,			hidden_sizes=self.hidden_sizes    ,			num_attention_heads=self.num_attention_heads    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			initializer_range=self.initializer_range    ,			decoder_hidden_size=self.decoder_hidden_size    ,			)






							def 				__lowercase	(       self    :      Tuple    ,			snake_case__    :      List[str]    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      List[str]     ):






												"""simple docstring"""



												__a:    List[Any]		     =       GLPNModel(config=snake_case__     )
												model.to(snake_case__     )
												model.eval()
												__a:    List[str]		     =       model(snake_case__     )
												__a:    int		     =       self.image_size // (self.downsampling_rates[-1] * 2)
												self.parent.assertEqual(
												    result.last_hidden_state.shape    ,			(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)     )






							def 				__lowercase	(       self    :      Union[str, Any]    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      Optional[Any]    ,			snake_case__    :      str     ):






												"""simple docstring"""



												__a:    int		     =       self.num_labels
												__a:    str		     =       GLPNForDepthEstimation(snake_case__     )
												model.to(snake_case__     )
												model.eval()
												__a:    Any		     =       model(snake_case__     )
												self.parent.assertEqual(result.predicted_depth.shape    ,			(self.batch_size, self.image_size, self.image_size)     )
												__a:    List[Any]		     =       model(snake_case__    ,			labels=snake_case__     )
												self.parent.assertEqual(result.predicted_depth.shape    ,			(self.batch_size, self.image_size, self.image_size)     )






							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       self.prepare_config_and_inputs()
												__a     ,					__a     ,					__a:    Dict		     =       config_and_inputs
												__a:    Union[str, Any]		     =       {"""pixel_values""": pixel_values}
												return config, inputs_dict






@require_torch
class  _snake_case	(    _lowercase			,					_lowercase			,					unittest.TestCase							):
							SCREAMING_SNAKE_CASE		  =    (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
							SCREAMING_SNAKE_CASE		  =    (
							    {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
							)

							SCREAMING_SNAKE_CASE		  =    False
							SCREAMING_SNAKE_CASE		  =    False
							SCREAMING_SNAKE_CASE		  =    False






							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												__a:    List[Any]		     =       GLPNModelTester(self     )
												__a:    Optional[Any]		     =       GLPNConfigTester(self    ,			config_class=snake_case__     )






							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												self.config_tester.run_common_tests()






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






												"""simple docstring"""



												__a:    str		     =       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*snake_case__     )






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												__a:    Dict		     =       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_depth_estimation(*snake_case__     )






							@unittest.skip("""GLPN does not use inputs_embeds"""     )
							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												pass






							@unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""     )
							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												pass






							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



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

												for model_class in self.all_model_classes:
																	__a:    Any		     =       model_class(snake_case__     )
																	__a:    Union[str, Any]		     =       inspect.signature(model.forward     )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	__a:    Dict		     =       [*signature.parameters.keys()]

																	__a:    str		     =       ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]    ,			snake_case__     )






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a     ,					__a:    Union[str, Any]		     =       self.model_tester.prepare_config_and_inputs_for_common()
												__a:    List[Any]		     =       True

												for model_class in self.all_model_classes:
																	__a:    Optional[Any]		     =       True
																	__a:    int		     =       False
																	__a:    int		     =       True
																	__a:    Optional[Any]		     =       model_class(snake_case__     )
																	model.to(snake_case__     )
																	model.eval()
																	with torch.no_grad():
																						__a:    Tuple		     =       model(**self._prepare_for_class(snake_case__    ,			snake_case__     )     )
																	__a:    List[Any]		     =       outputs.attentions

																	__a:    str		     =       sum(self.model_tester.depths     )
																	self.assertEqual(len(snake_case__     )    ,			snake_case__     )

																	# check that output_attentions also work using config
																	del inputs_dict["output_attentions"]
																	__a:    List[str]		     =       True
																	__a:    Tuple		     =       model_class(snake_case__     )
																	model.to(snake_case__     )
																	model.eval()
																	with torch.no_grad():
																						__a:    List[str]		     =       model(**self._prepare_for_class(snake_case__    ,			snake_case__     )     )
																	__a:    Optional[int]		     =       outputs.attentions

																	self.assertEqual(len(snake_case__     )    ,			snake_case__     )

																	# verify the first attentions (first block, first layer)
																	__a:    Dict		     =       (self.model_tester.image_size // 4) ** 2
																	__a:    Optional[int]		     =       (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]     )    ,			[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]    ,			)

																	# verify the last attentions (last block, last layer)
																	__a:    Union[str, Any]		     =       (self.model_tester.image_size // 3_2) ** 2
																	__a:    Optional[Any]		     =       (self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
																	self.assertListEqual(
																	    list(attentions[-1].shape[-3:]     )    ,			[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]    ,			)
																	__a:    Any		     =       len(snake_case__     )

																	# Check attention is always last and order is fine
																	__a:    List[Any]		     =       True
																	__a:    Tuple		     =       True
																	__a:    str		     =       model_class(snake_case__     )
																	model.to(snake_case__     )
																	model.eval()
																	with torch.no_grad():
																						__a:    str		     =       model(**self._prepare_for_class(snake_case__    ,			snake_case__     )     )

																	self.assertEqual(out_len + 1    ,			len(snake_case__     )     )

																	__a:    str		     =       outputs.attentions

																	self.assertEqual(len(snake_case__     )    ,			snake_case__     )
																	# verify the first attentions (first block, first layer)
																	__a:    str		     =       (self.model_tester.image_size // 4) ** 2
																	__a:    Optional[int]		     =       (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																	self.assertListEqual(
																	    list(self_attentions[0].shape[-3:]     )    ,			[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]    ,			)






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												def check_hidden_states_output(snake_case__    :      Optional[Any]    ,			snake_case__    :      Optional[int]    ,			snake_case__    :      Dict     ):
																	__a:    int		     =       model_class(snake_case__     )
																	model.to(snake_case__     )
																	model.eval()

																	with torch.no_grad():
																						__a:    Dict		     =       model(**self._prepare_for_class(snake_case__    ,			snake_case__     )     )

																	__a:    Optional[int]		     =       outputs.hidden_states

																	__a:    List[Any]		     =       self.model_tester.num_encoder_blocks
																	self.assertEqual(len(snake_case__     )    ,			snake_case__     )

																	# verify the first hidden states (first block)
																	self.assertListEqual(
																	    list(hidden_states[0].shape[-3:]     )    ,			[
																	        self.model_tester.hidden_sizes[0],
																	        self.model_tester.image_size // 4,
																	        self.model_tester.image_size // 4,
																	    ]    ,			)

												__a     ,					__a:    Any		     =       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__a:    Union[str, Any]		     =       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:    Tuple		     =       True

																	check_hidden_states_output(snake_case__    ,			snake_case__    ,			snake_case__     )






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												if not self.model_tester.is_training:
																	return

												__a     ,					__a:    Optional[Any]		     =       self.model_tester.prepare_config_and_inputs_for_common()
												__a:    int		     =       True

												for model_class in self.all_model_classes:
																	if model_class in get_values(snake_case__     ):
																						continue
																	# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
																	# this can then be incorporated into _prepare_for_class in test_modeling_common.py
																	if model_class.__name__ == "GLPNForDepthEstimation":
																						__a     ,					__a     ,					__a     ,					__a:    int		     =       inputs_dict["""pixel_values"""].shape
																						__a:    str		     =       torch.zeros(
																						    [self.model_tester.batch_size, height, width]    ,			device=snake_case__     ).long()
																	__a:    List[str]		     =       model_class(snake_case__     )
																	model.to(snake_case__     )
																	model.train()
																	__a:    Optional[Any]		     =       self._prepare_for_class(snake_case__    ,			snake_case__    ,			return_labels=snake_case__     )
																	__a:    List[str]		     =       model(**snake_case__     ).loss
																	loss.backward()






							@slow
							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__a:    int		     =       GLPNModel.from_pretrained(snake_case__     )
																	self.assertIsNotNone(snake_case__     )






def      lowerCamelCase_     (						)  ->      Dict:


					'''simple docstring'''
					__a:    Optional[Any]		     =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
					return image




@require_torch
@require_vision
@slow
class  _snake_case	(    unittest.TestCase							):






							@slow
							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
												__a:    int		     =       GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(snake_case__     )

												__a:    Any		     =       prepare_img()
												__a:    Union[str, Any]		     =       image_processor(images=snake_case__    ,			return_tensors="""pt"""     ).to(snake_case__     )

												# forward pass
												with torch.no_grad():
																	__a:    Optional[int]		     =       model(**snake_case__     )

												# verify the predicted depth
												__a:    List[Any]		     =       torch.Size([1, 4_8_0, 6_4_0]     )
												self.assertEqual(outputs.predicted_depth.shape    ,			snake_case__     )

												__a:    Tuple		     =       torch.tensor(
												    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]     ).to(snake_case__     )

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


"""simple docstring"""
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


lowercase		:    Dict			=       getLogger(__name__)


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


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_ = 8						,					lowerCamelCase_ = DEFAULT_DEVICE						,					lowerCamelCase_=False						,					lowerCamelCase_="summarization"						,					lowerCamelCase_=None						,					**lowerCamelCase_						,					)  ->      Dict:


					'''simple docstring'''
					__a:    str		     =       Path(lowerCamelCase_			).open("""w"""						,					encoding="""utf-8"""			)
					__a:    Tuple		     =       str(lowerCamelCase_			)
					__a:    List[Any]		     =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_			).to(lowerCamelCase_			)
					if fpaa:
										__a:    List[str]		     =       model.half()

					__a:    Optional[int]		     =       AutoTokenizer.from_pretrained(lowerCamelCase_			)
					logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""			)  # if this is wrong, check config.model_type.

					__a:    str		     =       time.time()
					# update config with task specific params
					use_task_specific_params(lowerCamelCase_						,					lowerCamelCase_			)
					if prefix is None:
										__a:    Tuple		     =       prefix or getattr(model.config						,					"""prefix"""						,					""""""			) or """"""
					for examples_chunk in tqdm(list(chunks(lowerCamelCase_						,					lowerCamelCase_			)			)			):
										__a:    Tuple		     =       [prefix + text for text in examples_chunk]
										__a:    Optional[int]		     =       tokenizer(lowerCamelCase_						,					return_tensors="""pt"""						,					truncation=lowerCamelCase_						,					padding="""longest"""			).to(lowerCamelCase_			)
										__a:    Any		     =       model.generate(
										    input_ids=batch.input_ids						,					attention_mask=batch.attention_mask						,					**lowerCamelCase_						,					)
										__a:    Optional[int]		     =       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()
					__a:    Any		     =       int(time.time() - start_time			)  # seconds
					__a:    Optional[int]		     =       len(lowerCamelCase_			)
					return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs						,					4			)}


def      lowerCamelCase_     (						)  ->      str:


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


def      lowerCamelCase_     (						lowerCamelCase_=True			)  ->      Any:


					'''simple docstring'''
					__a:    Any		     =       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
					__a     ,					__a:    int		     =       parser.parse_known_args()
					__a:    List[Any]		     =       parse_numeric_n_bool_cl_kwargs(lowerCamelCase_			)
					if parsed_args and verbose:
										print(f"""parsed the following generate kwargs: {parsed_args}"""			)
					with open(args.input_path			) as f:
										__a:    Tuple		     =       [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
					if args.n_obs > 0:
										__a:    Any		     =       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."""			)
					__a:    Tuple		     =       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
					__a:    Optional[Any]		     =       calculate_bleu if """translation""" in args.task else calculate_rouge
					__a:    Union[str, Any]		     =       [x.rstrip() for x in open(args.save_path			).readlines()]
					__a:    List[str]		     =       [x.rstrip() for x in open(args.reference_path			).readlines()][: len(lowerCamelCase_			)]
					__a:    dict		     =       score_fn(lowerCamelCase_						,					lowerCamelCase_			)
					scores.update(lowerCamelCase_			)

					if args.dump_args:
										scores.update(lowerCamelCase_			)
					if args.info:
										__a:    Optional[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)
