



import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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 torch import nn

			from transformers import RegNetForImageClassification, RegNetModel
			from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class    UpperCAmelCase_  :

						'''simple docstring'''




						def __init__(    self      ,							_A      ,							_A=3      ,							_A=32      ,							_A=3      ,							_A=10      ,							_A=[10, 20, 30, 40]      ,							_A=[1, 1, 2, 1]      ,							_A=True      ,							_A=True      ,							_A="relu"      ,							_A=3      ,							_A=None      ,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					parent
													__SCREAMING_SNAKE_CASE									=					batch_size
													__SCREAMING_SNAKE_CASE									=					image_size
													__SCREAMING_SNAKE_CASE									=					num_channels
													__SCREAMING_SNAKE_CASE									=					embeddings_size
													__SCREAMING_SNAKE_CASE									=					hidden_sizes
													__SCREAMING_SNAKE_CASE									=					depths
													__SCREAMING_SNAKE_CASE									=					is_training
													__SCREAMING_SNAKE_CASE									=					use_labels
													__SCREAMING_SNAKE_CASE									=					hidden_act
													__SCREAMING_SNAKE_CASE									=					num_labels
													__SCREAMING_SNAKE_CASE									=					scope
													__SCREAMING_SNAKE_CASE									=					len(__UpperCAmelCase		)






						def        _A	(    self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

													__SCREAMING_SNAKE_CASE									=					None
													if self.use_labels:
																				__SCREAMING_SNAKE_CASE									=					ids_tensor([self.batch_size]      ,							self.num_labels		)

													__SCREAMING_SNAKE_CASE									=					self.get_config()

													return config, pixel_values, labels






						def        _A	(    self		):







													'''simple docstring'''

													return RegNetConfig(
													    num_channels=self.num_channels      ,							embeddings_size=self.embeddings_size      ,							hidden_sizes=self.hidden_sizes      ,							depths=self.depths      ,							hidden_act=self.hidden_act      ,							num_labels=self.num_labels      ,							)






						def        _A	(    self      ,							_A      ,							_A      ,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					RegNetModel(config=__UpperCAmelCase		)
													model.to(__UpperCAmelCase		)
													model.eval()
													__SCREAMING_SNAKE_CASE									=					model(__UpperCAmelCase		)
													# expected last hidden states: B, C, H // 32, W // 32
													self.parent.assertEqual(
													    result.last_hidden_state.shape      ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,							)






						def        _A	(    self      ,							_A      ,							_A      ,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					self.num_labels
													__SCREAMING_SNAKE_CASE									=					RegNetForImageClassification(__UpperCAmelCase		)
													model.to(__UpperCAmelCase		)
													model.eval()
													__SCREAMING_SNAKE_CASE									=					model(__UpperCAmelCase      ,							labels=__UpperCAmelCase		)
													self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)		)






						def        _A	(    self		):







													'''simple docstring'''

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







@require_torch
class    UpperCAmelCase_  (   UpperCAmelCase_    ,    UpperCAmelCase_    ,    unittest.TestCase       ):

						'''simple docstring'''




						UpperCamelCase__			: str				  =  (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
						UpperCamelCase__			: Dict				  =  (
						    {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase__			: Union[str, Any]				  =  False
						UpperCamelCase__			: Dict				  =  False
						UpperCamelCase__			: str				  =  False
						UpperCamelCase__			: Any				  =  False






						def        _A	(    self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					RegNetModelTester(self		)
													__SCREAMING_SNAKE_CASE									=					ConfigTester(self      ,							config_class=__UpperCAmelCase      ,							has_text_modality=__UpperCAmelCase		)






						def        _A	(    self		):







													'''simple docstring'''

													self.create_and_test_config_common_properties()
													self.config_tester.create_and_test_config_to_json_string()
													self.config_tester.create_and_test_config_to_json_file()
													self.config_tester.create_and_test_config_from_and_save_pretrained()
													self.config_tester.create_and_test_config_with_num_labels()
													self.config_tester.check_config_can_be_init_without_params()
													self.config_tester.check_config_arguments_init()






						def        _A	(    self		):







													'''simple docstring'''

													return






						@unittest.skip(reason='RegNet does not use inputs_embeds'		)
						def        _A	(    self		):







													'''simple docstring'''

													pass






						@unittest.skip(reason='RegNet does not support input and output embeddings'		)
						def        _A	(    self		):







													'''simple docstring'''

													pass






						def        _A	(    self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE									=					self.model_tester.prepare_config_and_inputs_for_common()

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

																				__SCREAMING_SNAKE_CASE									=					['pixel_values']
																				self.assertListEqual(arg_names[:1]      ,							__UpperCAmelCase		)






						def        _A	(    self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCAmelCase		)






						def        _A	(    self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE									=					self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__SCREAMING_SNAKE_CASE									=					model_class(config=__UpperCAmelCase		)
																				for name, module in model.named_modules():
																											if isinstance(__UpperCAmelCase      ,							(nn.BatchNormad, nn.GroupNorm)		):
																																		self.assertTrue(
																																		    torch.all(module.weight == 1		)      ,							msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""      ,							)
																																		self.assertTrue(
																																		    torch.all(module.bias == 0		)      ,							msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""      ,							)






						def        _A	(    self		):







													'''simple docstring'''

													def check_hidden_states_output(_A      ,							_A      ,							_A		):
																				__SCREAMING_SNAKE_CASE									=					model_class(__UpperCAmelCase		)
																				model.to(__UpperCAmelCase		)
																				model.eval()

																				with torch.no_grad():
																											__SCREAMING_SNAKE_CASE									=					model(**self._prepare_for_class(__UpperCAmelCase      ,							__UpperCAmelCase		)		)

																				__SCREAMING_SNAKE_CASE									=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																				__SCREAMING_SNAKE_CASE									=					self.model_tester.num_stages
																				self.assertEqual(len(__UpperCAmelCase		)      ,							expected_num_stages + 1		)

																				# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
																				self.assertListEqual(
																				    list(hidden_states[0].shape[-2:]		)      ,							[self.model_tester.image_size // 2, self.model_tester.image_size // 2]      ,							)

													__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE									=					self.model_tester.prepare_config_and_inputs_for_common()
													__SCREAMING_SNAKE_CASE									=					['basic', 'bottleneck']
													for model_class in self.all_model_classes:
																				for layer_type in layers_type:
																											__SCREAMING_SNAKE_CASE									=					layer_type
																											__SCREAMING_SNAKE_CASE									=					True
																											check_hidden_states_output(__UpperCAmelCase      ,							__UpperCAmelCase      ,							__UpperCAmelCase		)

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

																											check_hidden_states_output(__UpperCAmelCase      ,							__UpperCAmelCase      ,							__UpperCAmelCase		)






						def        _A	(    self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase		)






						@slow
						def        _A	(    self		):







													'''simple docstring'''

													for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__SCREAMING_SNAKE_CASE									=					RegNetModel.from_pretrained(__UpperCAmelCase		)
																				self.assertIsNotNone(__UpperCAmelCase		)







def 	__lowercase   ( )				-> Tuple:
							__SCREAMING_SNAKE_CASE									=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
							return image

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

						'''simple docstring'''




						@cached_property
						def        _A	(    self		):







													'''simple docstring'''

													return (
													    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
													    if is_vision_available()
													    else None
													)






						@slow
						def        _A	(    self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(__UpperCAmelCase		)

													__SCREAMING_SNAKE_CASE									=					self.default_image_processor
													__SCREAMING_SNAKE_CASE									=					prepare_img()
													__SCREAMING_SNAKE_CASE									=					image_processor(images=__UpperCAmelCase      ,							return_tensors='pt'		).to(__UpperCAmelCase		)

													# forward pass
													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									=					model(**__UpperCAmelCase		)

													# verify the logits
													__SCREAMING_SNAKE_CASE									=					torch.Size((1, 1_000)		)
													self.assertEqual(outputs.logits.shape      ,							__UpperCAmelCase		)

													__SCREAMING_SNAKE_CASE									=					torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]		).to(__UpperCAmelCase		)

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







import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


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




def    lowerCAmelCase_ (     )     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  argparse.ArgumentParser(
          description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."							)
      parser.add_argument("--file_path",  type=__A,  default="data/dump.txt",  help="The path to the data."							)
      parser.add_argument("--tokenizer_type",  type=__A,  default="bert",  choices=["bert", "roberta", "gpt2"]							)
      parser.add_argument("--tokenizer_name",  type=__A,  default="bert-base-uncased",  help="The tokenizer to use."							)
      parser.add_argument("--dump_file",  type=__A,  default="data/dump",  help="The dump file prefix."							)
      UpperCAmelCase__								=  parser.parse_args()

      logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""							)
      if args.tokenizer_type == "bert":
            UpperCAmelCase__								=  BertTokenizer.from_pretrained(args.tokenizer_name							)
            UpperCAmelCase__								=  tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
            UpperCAmelCase__								=  tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            UpperCAmelCase__								=  RobertaTokenizer.from_pretrained(args.tokenizer_name							)
            UpperCAmelCase__								=  tokenizer.special_tokens_map["cls_token"]  # `<s>`
            UpperCAmelCase__								=  tokenizer.special_tokens_map["sep_token"]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            UpperCAmelCase__								=  GPTaTokenizer.from_pretrained(args.tokenizer_name							)
            UpperCAmelCase__								=  tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
            UpperCAmelCase__								=  tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

      logger.info(f"""Loading text from {args.file_path}"""							)
      with open(args.file_path,  "r",  encoding="utf8"							) as fp:
            UpperCAmelCase__								=  fp.readlines()

      logger.info("Start encoding"							)
      logger.info(f"""{len(__A							)} examples to process."""							)

      UpperCAmelCase__								=  []
      UpperCAmelCase__								=  0
      UpperCAmelCase__								=  10_000
      UpperCAmelCase__								=  time.time()
      for text in data:
            UpperCAmelCase__								=  f"""{bos} {text.strip()} {sep}"""
            UpperCAmelCase__								=  tokenizer.encode(__A,  add_special_tokens=__A							)
            rslt.append(__A							)

            iter += 1
            if iter % interval == 0:
                  UpperCAmelCase__								=  time.time()
                  logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""							)
                  UpperCAmelCase__								=  time.time()
      logger.info("Finished binarization"							)
      logger.info(f"""{len(__A							)} examples processed."""							)

      UpperCAmelCase__								=  f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
      UpperCAmelCase__								=  tokenizer.vocab_size
      if vocab_size < (1 << 16):
            UpperCAmelCase__								=  [np.uintaa(__A							) for d in rslt]
      else:
            UpperCAmelCase__								=  [np.intaa(__A							) for d in rslt]
      random.shuffle(rslt_							)
      logger.info(f"""Dump to {dp_file}"""							)
      with open(__A,  "wb"							) as handle:
            pickle.dump(rslt_,  __A,  protocol=pickle.HIGHEST_PROTOCOL							)


if __name__ == "__main__":
      main()



