
'''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			:

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

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
								lowerCamelCase_   : str       =						self.get_config()
								return config, pixel_values

			def     UpperCAmelCase__	(self ):
								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     UpperCAmelCase__	(self     ,				A     ,				A ):
								lowerCamelCase_   : Tuple       =						MgpstrForSceneTextRecognition(__UpperCamelCase )
								model.to(__UpperCamelCase )
								model.eval()
								with torch.no_grad():
													lowerCamelCase_   : Any       =						model(__UpperCamelCase )
								self.parent.assertEqual(
								    generated_ids[0][0].shape     ,				(self.batch_size, self.max_token_length, self.num_character_labels) )






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[Any]       =						self.prepare_config_and_inputs()
								lowerCamelCase_   : List[Any]       =						config_and_inputs
								lowerCamelCase_   : Optional[int]       =						{'pixel_values': pixel_values}
								return config, inputs_dict

@require_torch
class 		__lowercase			(					_lowerCamelCase ,       _lowerCamelCase ,       unittest.TestCase      ):
			lowerCamelCase     :						Optional[Any]		 =     (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
			lowerCamelCase     :						Optional[Any]		 =     {"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
			lowerCamelCase     :						Tuple		 =     False

			lowerCamelCase     :						List[str]		 =     False
			lowerCamelCase     :						Tuple		 =     False
			lowerCamelCase     :						Optional[Any]		 =     False
			lowerCamelCase     :						Union[str, Any]		 =     False

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						MgpstrModelTester(self )
								lowerCamelCase_   : Any       =						ConfigTester(self     ,				config_class=__UpperCamelCase     ,				has_text_modality=__UpperCamelCase )

			def     UpperCAmelCase__	(self ):
								self.config_tester.run_common_tests()

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Union[str, Any]       =						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     UpperCAmelCase__	(self ):
								pass

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[Any]       =						self.model_tester.prepare_config_and_inputs_for_common()

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

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						self.model_tester.prepare_config_and_inputs_for_common()

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

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

			@unittest.skip(reason='''MgpstrModel does not support feedforward chunking''' )
			def     UpperCAmelCase__	(self ):
								pass

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Optional[int]       =						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

													lowerCamelCase_   : int       =						True
													lowerCamelCase_   : Dict       =						model_class(__UpperCamelCase )
													self.assertTrue(model.is_gradient_checkpointing )

			def     UpperCAmelCase__	(self ):







								def check_hidden_states_output(A     ,				A     ,				A ):
													lowerCamelCase_   : Tuple       =						model_class(__UpperCamelCase )
													model.to(__UpperCamelCase )
													model.eval()

													with torch.no_grad():
																		lowerCamelCase_   : Tuple       =						model(**self._prepare_for_class(__UpperCamelCase     ,				__UpperCamelCase ) )

													lowerCamelCase_   : Any       =						outputs.hidden_states

													lowerCamelCase_   : str       =						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]     ,				)

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

								for model_class in self.all_model_classes:
													lowerCamelCase_   : Any       =						True
													check_hidden_states_output(__UpperCamelCase     ,				__UpperCamelCase     ,				__UpperCamelCase )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCamelCase_   : Optional[Any]       =						True

													check_hidden_states_output(__UpperCamelCase     ,				__UpperCamelCase     ,				__UpperCamelCase )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase_   : Any       =						_config_zero_init(__UpperCamelCase )
								for model_class in self.all_model_classes:
													lowerCamelCase_   : Tuple       =						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     UpperCAmelCase__	(self ):
								pass




def lowercase_		(				)       ->							List[Any]:
					'''simple docstring'''

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






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

			@slow
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						'alibaba-damo/mgp-str-base'
								lowerCamelCase_   : Optional[int]       =						MgpstrForSceneTextRecognition.from_pretrained(__UpperCamelCase ).to(__UpperCamelCase )
								lowerCamelCase_   : Tuple       =						MgpstrProcessor.from_pretrained(__UpperCamelCase )

								lowerCamelCase_   : int       =						prepare_img()
								lowerCamelCase_   : Dict       =						processor(images=__UpperCamelCase     ,				return_tensors='''pt''' ).pixel_values.to(__UpperCamelCase )

								# forward pass
								with torch.no_grad():
													lowerCamelCase_   : Optional[Any]       =						model(__UpperCamelCase )

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

								lowerCamelCase_   : List[Any]       =						processor.batch_decode(outputs.logits )
								lowerCamelCase_   : Union[str, Any]       =						'ticket'

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

								lowerCamelCase_   : Union[str, Any]       =						torch.tensor(
								    [[[-3_9.5_3_9_7, -4_4.4_0_2_4, -3_6.1_8_4_4], [-6_1.4_7_0_9, -6_3.8_6_3_9, -5_8.3_4_5_4], [-7_4.0_2_2_5, -6_8.5_4_9_4, -7_1.2_1_6_4]]]     ,				device=__UpperCamelCase     ,				)

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




'''simple docstring'''


import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowercase							:				Optional[Any]					=       logging.getLogger(__name__)




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Optional[int]       =						argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''			)
					parser.add_argument('''--model_name'''     ,    type=_lowercase     ,    default='''transfo-xl-wt103'''     ,    help='''pretrained model name'''			)
					parser.add_argument(
					    '''--split'''     ,    type=_lowercase     ,    default='''test'''     ,    choices=['''all''', '''valid''', '''test''']     ,    help='''which split to evaluate'''			)
					parser.add_argument('''--batch_size'''     ,    type=_lowercase     ,    default=10     ,    help='''batch size'''			)
					parser.add_argument('''--tgt_len'''     ,    type=_lowercase     ,    default=128     ,    help='''number of tokens to predict'''			)
					parser.add_argument('''--ext_len'''     ,    type=_lowercase     ,    default=0     ,    help='''length of the extended context'''			)
					parser.add_argument('''--mem_len'''     ,    type=_lowercase     ,    default=1_600     ,    help='''length of the retained previous heads'''			)
					parser.add_argument('''--clamp_len'''     ,    type=_lowercase     ,    default=1_000     ,    help='''max positional embedding index'''			)
					parser.add_argument('''--no_cuda'''     ,    action='''store_true'''     ,    help='''Do not use CUDA even though CUA is available'''			)
					parser.add_argument('''--work_dir'''     ,    type=_lowercase     ,    required=_lowercase     ,    help='''path to the work_dir'''			)
					parser.add_argument('''--no_log'''     ,    action='''store_true'''     ,    help='''do not log the eval result'''			)
					parser.add_argument('''--same_length'''     ,    action='''store_true'''     ,    help='''set same length attention with masking'''			)
					parser.add_argument('''--server_ip'''     ,    type=_lowercase     ,    default=''''''     ,    help='''Can be used for distant debugging.'''			)
					parser.add_argument('''--server_port'''     ,    type=_lowercase     ,    default=''''''     ,    help='''Can be used for distant debugging.'''			)
					lowerCamelCase_   : Dict       =						parser.parse_args()
					assert args.ext_len >= 0, "extended context length must be non-negative"

					if args.server_ip and args.server_port:
										# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
										import ptvsd

										print('''Waiting for debugger attach'''			)
										ptvsd.enable_attach(address=(args.server_ip, args.server_port)     ,    redirect_output=_lowercase			)
										ptvsd.wait_for_attach()

					lowerCamelCase_   : List[str]       =						torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
					logger.info('''device: {}'''.format(_lowercase			)			)

					# Load a pre-processed dataset
					# You can also build the corpus yourself using TransfoXLCorpus methods
					# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
					# and tokenizing the dataset
					# The pre-processed corpus is a convertion (using the conversion script )
					lowerCamelCase_   : str       =						TransfoXLCorpus.from_pretrained(args.model_name			)

					lowerCamelCase_   : Optional[int]       =						corpus.get_iterator('''valid'''     ,    args.batch_size     ,    args.tgt_len     ,    device=_lowercase     ,    ext_len=args.ext_len			)
					lowerCamelCase_   : str       =						corpus.get_iterator('''test'''     ,    args.batch_size     ,    args.tgt_len     ,    device=_lowercase     ,    ext_len=args.ext_len			)

					# Load a pre-trained model
					lowerCamelCase_   : str       =						TransfoXLLMHeadModel.from_pretrained(args.model_name			)
					model.to(_lowercase			)

					logger.info(
					    '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
					        args.batch_size     ,    args.tgt_len     ,    args.ext_len     ,    args.mem_len     ,    args.clamp_len			)			)

					model.reset_memory_length(args.mem_len			)
					if args.clamp_len > 0:
										lowerCamelCase_   : Union[str, Any]       =						args.clamp_len
					if args.same_length:
										lowerCamelCase_   : int       =						True

					###############################################################################
					# Evaluation code
					###############################################################################
					def evaluate(_lowercase			):
										# Turn on evaluation mode which disables dropout.
										model.eval()
										lowerCamelCase_, lowerCamelCase_   : Union[str, Any]       =						0, 0.0
										lowerCamelCase_   : Optional[int]       =						time.time()
										with torch.no_grad():
															lowerCamelCase_   : Dict       =						None
															for idx, (data, target, seq_len) in enumerate(_lowercase			):
																				lowerCamelCase_   : str       =						model(_lowercase     ,    lm_labels=_lowercase     ,    mems=_lowercase			)
																				lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : int       =						ret
																				lowerCamelCase_   : List[str]       =						loss.mean()
																				total_loss += seq_len * loss.item()
																				total_len += seq_len
															lowerCamelCase_   : List[str]       =						time.time() - start_time
										logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(_lowercase     ,    1_000 * total_time / (idx + 1)			)			)
										return total_loss / total_len

					# Run on test data.
					if args.split == "all":
										lowerCamelCase_   : Tuple       =						evaluate(_lowercase			)
										lowerCamelCase_   : List[Any]       =						evaluate(_lowercase			)
					elif args.split == "valid":
										lowerCamelCase_   : Union[str, Any]       =						evaluate(_lowercase			)
										lowerCamelCase_   : str       =						None
					elif args.split == "test":
										lowerCamelCase_   : Any       =						evaluate(_lowercase			)
										lowerCamelCase_   : int       =						None

					def format_log(_lowercase     ,    _lowercase			):
										lowerCamelCase_   : Optional[Any]       =						'''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(_lowercase     ,    _lowercase     ,    math.exp(_lowercase			)			)
										return log_str

					lowerCamelCase_   : List[Any]       =						''''''
					if valid_loss is not None:
										log_str += format_log(_lowercase     ,    '''valid'''			)
					if test_loss is not None:
										log_str += format_log(_lowercase     ,    '''test'''			)

					logger.info('''=''' * 100			)
					logger.info(_lowercase			)
					logger.info('''=''' * 100			)


if __name__ == "__main__":
					main()


