






"""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
)
_UpperCamelCase						:    int		=						logging.getLogger(__name__)
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			argparse.ArgumentParser(description='PyTorch Transformer Language Model'       )
  parser.add_argument('--model_name'						,   type=__snake_case						,   default='transfo-xl-wt103'						,   help='pretrained model name'       )
  parser.add_argument(
      '--split'						,   type=__snake_case						,   default='test'						,   choices=['all', 'valid', 'test']						,   help='which split to evaluate'       )
  parser.add_argument('--batch_size'						,   type=__snake_case						,   default=10						,   help='batch size'       )
  parser.add_argument('--tgt_len'						,   type=__snake_case						,   default=1_28						,   help='number of tokens to predict'       )
  parser.add_argument('--ext_len'						,   type=__snake_case						,   default=0						,   help='length of the extended context'       )
  parser.add_argument('--mem_len'						,   type=__snake_case						,   default=16_00						,   help='length of the retained previous heads'       )
  parser.add_argument('--clamp_len'						,   type=__snake_case						,   default=10_00						,   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=__snake_case						,   required=__snake_case						,   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=__snake_case						,   default=''						,   help='Can be used for distant debugging.'       )
  parser.add_argument('--server_port'						,   type=__snake_case						,   default=''						,   help='Can be used for distant debugging.'       )
  lowercase				       =			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=__snake_case       )
    ptvsd.wait_for_attach()

  lowercase				       =			torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'       )
  logger.info('device: {}'.format(__snake_case       )       )

  # 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 )
  lowercase				       =			TransfoXLCorpus.from_pretrained(args.model_name       )

  lowercase				       =			corpus.get_iterator('valid'						,   args.batch_size						,   args.tgt_len						,   device=__snake_case						,   ext_len=args.ext_len       )
  lowercase				       =			corpus.get_iterator('test'						,   args.batch_size						,   args.tgt_len						,   device=__snake_case						,   ext_len=args.ext_len       )

  # Load a pre-trained model
  lowercase				       =			TransfoXLLMHeadModel.from_pretrained(args.model_name       )
  model.to(__snake_case       )

  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:
    lowercase				       =			args.clamp_len
  if args.same_length:
    lowercase				       =			True

  ###############################################################################
  # Evaluation code
  ###############################################################################
  def evaluate(__snake_case     :    Any       ):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    lowercase       ,	lowercase				       =			0, 0.0
    lowercase				       =			time.time()
    with torch.no_grad():
      lowercase				       =			None
      for idx, (data, target, seq_len) in enumerate(__snake_case       ):
        lowercase				       =			model(__snake_case						,   lm_labels=__snake_case						,   mems=__snake_case       )
        lowercase       ,	lowercase       ,	lowercase				       =			ret
        lowercase				       =			loss.mean()
        total_loss += seq_len * loss.item()
        total_len += seq_len
      lowercase				       =			time.time() - start_time
    logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(__snake_case						,   10_00 * total_time / (idx + 1)       )       )
    return total_loss / total_len

  # Run on test data.
  if args.split == "all":
    lowercase				       =			evaluate(__snake_case       )
    lowercase				       =			evaluate(__snake_case       )
  elif args.split == "valid":
    lowercase				       =			evaluate(__snake_case       )
    lowercase				       =			None
  elif args.split == "test":
    lowercase				       =			evaluate(__snake_case       )
    lowercase				       =			None

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

  lowercase				       =			''
  if valid_loss is not None:
    log_str += format_log(__snake_case						,   'valid'       )
  if test_loss is not None:
    log_str += format_log(__snake_case						,   'test'       )

  logger.info('=' * 1_00       )
  logger.info(__snake_case       )
  logger.info('=' * 1_00       )


if __name__ == "__main__":
    main()










"""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 			a	:







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=False				,						_lowerCamelCase=1_3				,						_lowerCamelCase=(3_2, 1_2_8)				,						_lowerCamelCase=4				,						_lowerCamelCase=3				,						_lowerCamelCase=2_7				,						_lowerCamelCase=3_8				,						_lowerCamelCase=9_9				,						_lowerCamelCase=9_9				,						_lowerCamelCase=3_2				,						_lowerCamelCase=5				,						_lowerCamelCase=4				,						_lowerCamelCase=4.0				,						_lowerCamelCase=2_5_7				,						_lowerCamelCase=None				,						):
      lowercase				       =			parent
      lowercase				       =			is_training
      lowercase				       =			batch_size
      lowercase				       =			image_size
      lowercase				       =			patch_size
      lowercase				       =			num_channels
      lowercase				       =			max_token_length
      lowercase				       =			num_character_labels
      lowercase				       =			num_bpe_labels
      lowercase				       =			num_wordpiece_labels
      lowercase				       =			hidden_size
      lowercase				       =			num_hidden_layers
      lowercase				       =			num_attention_heads
      lowercase				       =			mlp_ratio
      lowercase				       =			patch_embeds_hidden_size
      lowercase				       =			output_hidden_states







    def      UpperCamelCase_ (     self			):
      lowercase				       =			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]			)
      lowercase				       =			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				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			MgpstrForSceneTextRecognition(_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      with torch.no_grad():
        lowercase				       =			model(_lowerCamelCase			)
      self.parent.assertEqual(
          generated_ids[0][0].shape				,						(self.batch_size, self.max_token_length, self.num_character_labels)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.prepare_config_and_inputs()
      lowercase       ,	lowercase				       =			config_and_inputs
      lowercase				       =			{'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class 			a	(  a_,							a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	Optional[int]      	=(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
    UpperCAmelCase_					:	Optional[Any]      	={"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
    UpperCAmelCase_					:	Union[str, Any]      	=False

    UpperCAmelCase_					:	Optional[Any]      	=False
    UpperCAmelCase_					:	Dict      	=False
    UpperCAmelCase_					:	str      	=False
    UpperCAmelCase_					:	str      	=False







    def      UpperCamelCase_ (     self			):
      lowercase				       =			MgpstrModelTester(self			)
      lowercase				       =			ConfigTester(self				,						config_class=_lowerCamelCase				,						has_text_modality=_lowerCamelCase			)







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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_lowerCamelCase			)







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







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			model_class(_lowerCamelCase			)
        self.assertIsInstance(model.get_input_embeddings()				,						(nn.Module)			)
        lowercase				       =			model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_lowerCamelCase				,						nn.Linear			)			)







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

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

        lowercase				       =			['pixel_values']
        self.assertListEqual(arg_names[:1]				,						_lowerCamelCase			)







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







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			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

        lowercase				       =			True
        lowercase				       =			model_class(_lowerCamelCase			)
        self.assertTrue(model.is_gradient_checkpointing			)







    def      UpperCamelCase_ (     self			):
      def check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
        lowercase				       =			model_class(_lowerCamelCase			)
        model.to(_lowerCamelCase			)
        model.eval()

        with torch.no_grad():
          lowercase				       =			model(**self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)			)

        lowercase				       =			outputs.hidden_states

        lowercase				       =			getattr(
            self.model_tester				,						'expected_num_hidden_layers'				,						self.model_tester.num_hidden_layers + 1			)
        self.assertEqual(len(_lowerCamelCase			)				,						_lowerCamelCase			)

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

      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			True
        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowercase				       =			True

        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      lowercase				       =			_config_zero_init(_lowerCamelCase			)
      for model_class in self.all_model_classes:
        lowercase				       =			model_class(config=_lowerCamelCase			)
        for name, param in model.named_parameters():
          if isinstance(_lowerCamelCase				,						(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 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			'https://i.postimg.cc/ZKwLg2Gw/367-14.png'
  lowercase				       =			Image.open(requests.get(__snake_case						,   stream=__snake_case       ).raw       ).convert('RGB'       )
  return im



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







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			'alibaba-damo/mgp-str-base'
      lowercase				       =			MgpstrForSceneTextRecognition.from_pretrained(_lowerCamelCase			).to(_lowerCamelCase			)
      lowercase				       =			MgpstrProcessor.from_pretrained(_lowerCamelCase			)

      lowercase				       =			prepare_img()
      lowercase				       =			processor(images=_lowerCamelCase				,						return_tensors='pt'			).pixel_values.to(_lowerCamelCase			)

      # forward pass
      with torch.no_grad():
        lowercase				       =			model(_lowerCamelCase			)

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

      lowercase				       =			processor.batch_decode(outputs.logits			)
      lowercase				       =			'ticket'

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

      lowercase				       =			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=_lowerCamelCase				,						)

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


