

"""simple docstring"""





import inspect
import unittest

import requests

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor

class       __lowercase       :


				'''simple docstring'''


				def __init__(   self   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     int=False      ,SCREAMING_SNAKE_CASE_   :     Any=1_3      ,SCREAMING_SNAKE_CASE_   :     Tuple=(3_2, 1_2_8)      ,SCREAMING_SNAKE_CASE_   :     Any=4      ,SCREAMING_SNAKE_CASE_   :     Optional[int]=3      ,SCREAMING_SNAKE_CASE_   :     Dict=2_7      ,SCREAMING_SNAKE_CASE_   :     List[Any]=3_8      ,SCREAMING_SNAKE_CASE_   :     Tuple=9_9      ,SCREAMING_SNAKE_CASE_   :     Any=9_9      ,SCREAMING_SNAKE_CASE_   :     int=3_2      ,SCREAMING_SNAKE_CASE_   :     int=5      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]=4      ,SCREAMING_SNAKE_CASE_   :     Dict=4.0      ,SCREAMING_SNAKE_CASE_   :     List[str]=2_5_7      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]=None      ,):
										snake_case__       :					Any										=					parent
										snake_case__       :					Optional[int]										=					is_training
										snake_case__       :					Dict										=					batch_size
										snake_case__       :					Any										=					image_size
										snake_case__       :					str										=					patch_size
										snake_case__       :					Optional[Any]										=					num_channels
										snake_case__       :					Optional[int]										=					max_token_length
										snake_case__       :					str										=					num_character_labels
										snake_case__       :					Optional[Any]										=					num_bpe_labels
										snake_case__       :					Optional[int]										=					num_wordpiece_labels
										snake_case__       :					Optional[Any]										=					hidden_size
										snake_case__       :					int										=					num_hidden_layers
										snake_case__       :					Tuple										=					num_attention_heads
										snake_case__       :					Union[str, Any]										=					mlp_ratio
										snake_case__       :					List[Any]										=					patch_embeds_hidden_size
										snake_case__       :					Tuple										=					output_hidden_states

				def       _lowerCamelCase					(   self   :     str      ):
										snake_case__       :					Optional[Any]										=					floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]      )
										snake_case__       :					Union[str, Any]										=					self.get_config()
										return config, pixel_values

				def       _lowerCamelCase					(   self   :     str      ):
										return MgpstrConfig(
										    image_size=self.image_size      ,patch_size=self.patch_size      ,num_channels=self.num_channels      ,max_token_length=self.max_token_length      ,num_character_labels=self.num_character_labels      ,num_bpe_labels=self.num_bpe_labels      ,num_wordpiece_labels=self.num_wordpiece_labels      ,hidden_size=self.hidden_size      ,num_hidden_layers=self.num_hidden_layers      ,num_attention_heads=self.num_attention_heads      ,mlp_ratio=self.mlp_ratio      ,output_hidden_states=self.output_hidden_states      ,)

				def       _lowerCamelCase					(   self   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     int      ):
										snake_case__       :					Any										=					MgpstrForSceneTextRecognition(__UpperCamelCase      )
										model.to(__UpperCamelCase      )
										model.eval()
										with torch.no_grad():
																snake_case__       :					List[Any]										=					model(__UpperCamelCase      )
										self.parent.assertEqual(
										    generated_ids[0][0].shape      ,(self.batch_size, self.max_token_length, self.num_character_labels)      )





				def       _lowerCamelCase					(   self   :     Any      ):
										snake_case__       :					List[Any]										=					self.prepare_config_and_inputs()
										snake_case__       :					Any										=					config_and_inputs
										snake_case__       :					Any										=					{'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_torch
class       __lowercase       (			UpperCamelCase_						,   UpperCamelCase_						,   unittest.TestCase	):


				'''simple docstring'''


				_lowerCAmelCase				=(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
				_lowerCAmelCase				={"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
				_lowerCAmelCase				=False

				_lowerCAmelCase				=False
				_lowerCAmelCase				=False
				_lowerCAmelCase				=False
				_lowerCAmelCase				=False

				def       _lowerCamelCase					(   self   :     List[str]      ):
										snake_case__       :					Any										=					MgpstrModelTester(self      )
										snake_case__       :					Union[str, Any]										=					ConfigTester(self      ,config_class=__UpperCamelCase      ,has_text_modality=__UpperCamelCase      )

				def       _lowerCamelCase					(   self   :     Optional[int]      ):
										self.config_tester.run_common_tests()

				def       _lowerCamelCase					(   self   :     int      ):
										snake_case__       :					Tuple										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__UpperCamelCase      )

				@unittest.skip(reason='''MgpstrModel does not use inputs_embeds'''      )
				def       _lowerCamelCase					(   self   :     List[Any]      ):
										pass

				def       _lowerCamelCase					(   self   :     Tuple      ):
										snake_case__       :					Optional[Any]										=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case__       :					Optional[Any]										=					model_class(__UpperCamelCase      )
																self.assertIsInstance(model.get_input_embeddings()      ,(nn.Module)      )
																snake_case__       :					Any										=					model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(__UpperCamelCase      ,nn.Linear      )      )

				def       _lowerCamelCase					(   self   :     Dict      ):
										snake_case__       :					Any										=					self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case__       :					List[str]										=					['''pixel_values''']
																self.assertListEqual(arg_names[:1]      ,__UpperCamelCase      )

				@unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''      )
				def       _lowerCamelCase					(   self   :     Optional[Any]      ):
										pass

				def       _lowerCamelCase					(   self   :     int      ):
										snake_case__       :					List[Any]										=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																if not model_class.supports_gradient_checkpointing:
																						continue

																snake_case__       :					Any										=					True
																snake_case__       :					Union[str, Any]										=					model_class(__UpperCamelCase      )
																self.assertTrue(model.is_gradient_checkpointing      )

				def       _lowerCamelCase					(   self   :     List[Any]      ):


										def check_hidden_states_output(SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     str      ):
																snake_case__       :					List[Any]										=					model_class(__UpperCamelCase      )
																model.to(__UpperCamelCase      )
																model.eval()

																with torch.no_grad():
																						snake_case__       :					Any										=					model(**self._prepare_for_class(__UpperCamelCase      ,__UpperCamelCase      )      )

																snake_case__       :					Optional[int]										=					outputs.hidden_states

																snake_case__       :					int										=					getattr(
																    self.model_tester      ,'''expected_num_hidden_layers'''      ,self.model_tester.num_hidden_layers + 1      )
																self.assertEqual(len(__UpperCamelCase      )      ,__UpperCamelCase      )

																self.assertListEqual(
																    list(hidden_states[0].shape[-2:]      )      ,[self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]      ,)

										snake_case__       :					Optional[Any]										=					self.model_tester.prepare_config_and_inputs_for_common()

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

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

																check_hidden_states_output(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      )

				def       _lowerCamelCase					(   self   :     int      ):
										snake_case__       :					List[str]										=					self.model_tester.prepare_config_and_inputs_for_common()

										snake_case__       :					Tuple										=					_config_zero_init(__UpperCamelCase      )
										for model_class in self.all_model_classes:
																snake_case__       :					Optional[int]										=					model_class(config=__UpperCamelCase      )
																for name, param in model.named_parameters():
																						if isinstance(__UpperCamelCase      ,(nn.Linear, nn.Convad, nn.LayerNorm)      ):
																												if param.requires_grad:
																																		self.assertIn(
																																		    ((param.data.mean() * 1E9).round() / 1E9).item()      ,[0.0, 1.0]      ,msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''      ,)





				@unittest.skip(reason='''Retain_grad is tested in individual model tests'''      )
				def       _lowerCamelCase					(   self   :     Optional[Any]      ):
										pass







def 		lowerCAmelCase							( ):
						'''simple docstring'''


						snake_case__       :					List[Any]										=					'''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
						snake_case__       :					List[str]										=					Image.open(requests.get(snake_case_      ,			stream=snake_case_		).raw		).convert('''RGB'''		)
						return im

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


				'''simple docstring'''


				@slow
				def       _lowerCamelCase					(   self   :     Any      ):
										snake_case__       :					int										=					'''alibaba-damo/mgp-str-base'''
										snake_case__       :					Any										=					MgpstrForSceneTextRecognition.from_pretrained(__UpperCamelCase      ).to(__UpperCamelCase      )
										snake_case__       :					int										=					MgpstrProcessor.from_pretrained(__UpperCamelCase      )

										snake_case__       :					Tuple										=					prepare_img()
										snake_case__       :					str										=					processor(images=__UpperCamelCase      ,return_tensors='''pt'''      ).pixel_values.to(__UpperCamelCase      )

										# forward pass
										with torch.no_grad():
																snake_case__       :					Any										=					model(__UpperCamelCase      )

										# verify the logits
										self.assertEqual(outputs.logits[0].shape      ,torch.Size((1, 2_7, 3_8)      )      )

										snake_case__       :					Dict										=					processor.batch_decode(outputs.logits      )
										snake_case__       :					int										=					'''ticket'''

										self.assertEqual(out_strs['''generated_text'''][0]      ,__UpperCamelCase      )

										snake_case__       :					Tuple										=					torch.tensor(
										    [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]      ,device=__UpperCamelCase      ,)

										self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]      ,__UpperCamelCase      ,atol=1E-4      )      )








"""simple docstring"""





import argparse
import os
import sys
import urllib.request
import zipfile


_lowercase	      =    ["""CoLA""", """SST""", """MRPC""", """QQP""", """STS""", """MNLI""", """SNLI""", """QNLI""", """RTE""", """WNLI""", """diagnostic"""]
_lowercase	      =    {
    """CoLA""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4""",
    """SST""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8""",
    """MRPC""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc""",
    """QQP""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5""",
    """STS""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5""",
    """MNLI""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce""",
    """SNLI""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df""",
    """QNLI""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601""",
    """RTE""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb""",
    """WNLI""": """https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf""",
    """diagnostic""": """https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D""",
}

_lowercase	      =    """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"""
_lowercase	      =    """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"""

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						print(f'''Downloading and extracting {task}...'''		)
						snake_case__       :					Optional[Any]										=					f'''{task}.zip'''
						urllib.request.urlretrieve(TASKaPATH[task]      ,			lowerCamelCase		)
						with zipfile.ZipFile(lowerCamelCase		) as zip_ref:
												zip_ref.extractall(lowerCamelCase		)
						os.remove(lowerCamelCase		)
						print('''\tCompleted!'''		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						print('''Processing MRPC...'''		)
						snake_case__       :					Union[str, Any]										=					os.path.join(lowerCamelCase      ,			'''MRPC'''		)
						if not os.path.isdir(lowerCamelCase		):
												os.mkdir(lowerCamelCase		)
						if path_to_data:
												snake_case__       :					List[Any]										=					os.path.join(lowerCamelCase      ,			'''msr_paraphrase_train.txt'''		)
												snake_case__       :					Any										=					os.path.join(lowerCamelCase      ,			'''msr_paraphrase_test.txt'''		)
						else:
												print('''Local MRPC data not specified, downloading data from %s''' % MRPC_TRAIN		)
												snake_case__       :					Tuple										=					os.path.join(lowerCamelCase      ,			'''msr_paraphrase_train.txt'''		)
												snake_case__       :					Optional[int]										=					os.path.join(lowerCamelCase      ,			'''msr_paraphrase_test.txt'''		)
												urllib.request.urlretrieve(lowerCamelCase      ,			lowerCamelCase		)
												urllib.request.urlretrieve(lowerCamelCase      ,			lowerCamelCase		)
						if not os.path.isfile(lowerCamelCase		):
												raise ValueError(f'''Train data not found at {mrpc_train_file}'''		)
						if not os.path.isfile(lowerCamelCase		):
												raise ValueError(f'''Test data not found at {mrpc_test_file}'''		)
						urllib.request.urlretrieve(TASKaPATH['''MRPC''']      ,			os.path.join(lowerCamelCase      ,			'''dev_ids.tsv'''		)		)

						snake_case__       :					Optional[Any]										=					[]
						with open(os.path.join(lowerCamelCase      ,			'''dev_ids.tsv'''		)      ,			encoding='''utf8'''		) as ids_fh:
												for row in ids_fh:
																		dev_ids.append(row.strip().split('''\t'''		)		)

						with open(lowerCamelCase      ,			encoding='''utf8'''		) as data_fh, open(
						    os.path.join(lowerCamelCase      ,			'''train.tsv'''		)      ,			'''w'''      ,			encoding='''utf8'''		) as train_fh, open(os.path.join(lowerCamelCase      ,			'''dev.tsv'''		)      ,			'''w'''      ,			encoding='''utf8'''		) as dev_fh:
												snake_case__       :					Any										=					data_fh.readline()
												train_fh.write(lowerCamelCase		)
												dev_fh.write(lowerCamelCase		)
												for row in data_fh:
																		snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__       :					Dict										=					row.strip().split('''\t'''		)
																		if [ida, ida] in dev_ids:
																								dev_fh.write('''%s\t%s\t%s\t%s\t%s\n''' % (label, ida, ida, sa, sa)		)
																		else:
																								train_fh.write('''%s\t%s\t%s\t%s\t%s\n''' % (label, ida, ida, sa, sa)		)

						with open(lowerCamelCase      ,			encoding='''utf8'''		) as data_fh, open(
						    os.path.join(lowerCamelCase      ,			'''test.tsv'''		)      ,			'''w'''      ,			encoding='''utf8'''		) as test_fh:
												snake_case__       :					Tuple										=					data_fh.readline()
												test_fh.write('''index\t#1 ID\t#2 ID\t#1 String\t#2 String\n'''		)
												for idx, row in enumerate(lowerCamelCase		):
																		snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__       :					Union[str, Any]										=					row.strip().split('''\t'''		)
																		test_fh.write('''%d\t%s\t%s\t%s\t%s\n''' % (idx, ida, ida, sa, sa)		)
						print('''\tCompleted!'''		)

def 		lowerCAmelCase							( lowerCamelCase		):
						'''simple docstring'''


						print('''Downloading and extracting diagnostic...'''		)
						if not os.path.isdir(os.path.join(lowerCamelCase      ,			'''diagnostic'''		)		):
												os.mkdir(os.path.join(lowerCamelCase      ,			'''diagnostic'''		)		)
						snake_case__       :					Any										=					os.path.join(lowerCamelCase      ,			'''diagnostic'''      ,			'''diagnostic.tsv'''		)
						urllib.request.urlretrieve(TASKaPATH['''diagnostic''']      ,			lowerCamelCase		)
						print('''\tCompleted!'''		)
						return

def 		lowerCAmelCase							( lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Any										=					task_names.split(''','''		)
						if "all" in task_names:
												snake_case__       :					str										=					TASKS
						else:
												snake_case__       :					Union[str, Any]										=					[]
												for task_name in task_names:
																		if task_name not in TASKS:
																								raise ValueError(f'''Task {task_name} not found!'''		)
																		tasks.append(lowerCamelCase		)
						return tasks

def 		lowerCAmelCase							( lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					int										=					argparse.ArgumentParser()
						parser.add_argument('''--data_dir'''      ,			help='''directory to save data to'''      ,			type=lowerCamelCase      ,			default='''glue_data'''		)
						parser.add_argument(
						    '''--tasks'''      ,			help='''tasks to download data for as a comma separated string'''      ,			type=lowerCamelCase      ,			default='''all'''		)
						parser.add_argument(
						    '''--path_to_mrpc'''      ,			help='''path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt'''      ,			type=lowerCamelCase      ,			default=''''''      ,			)
						snake_case__       :					Dict										=					parser.parse_args(lowerCamelCase		)

						if not os.path.isdir(args.data_dir		):
												os.mkdir(args.data_dir		)
						snake_case__       :					Union[str, Any]										=					get_tasks(args.tasks		)

						for task in tasks:
												if task == "MRPC":
																		format_mrpc(args.data_dir      ,			args.path_to_mrpc		)
												elif task == "diagnostic":
																		download_diagnostic(args.data_dir		)
												else:
																		download_and_extract(lowerCamelCase      ,			args.data_dir		)


if __name__ == "__main__":
							sys.exit(main(sys.argv[1:]))


