





'''simple docstring'''




import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch







class   A_	(  lowerCAmelCase_			):




 @require_torch
 def      lowercase	(					self       :    str    ):
      # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
      # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
      # while running an external program

      # python one-liner segments

      # this must be loaded before socket.socket is monkey-patched
      _UpperCAmelCase     							=   "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

      _UpperCAmelCase     							=   "\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

      _UpperCAmelCase     							=   "\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")\nsocket.socket = offline_socket\n        "

      # Force fetching the files so that we can use the cache
      _UpperCAmelCase     							=   "hf-internal-testing/tiny-random-bert"
      BertConfig.from_pretrained(snake_case_    )
      BertModel.from_pretrained(snake_case_    )
      BertTokenizer.from_pretrained(snake_case_    )
      pipeline(task="fill-mask"	,			model=snake_case_    )

      # baseline - just load from_pretrained with normal network
      _UpperCAmelCase     							=   [sys.executable, "-c", "\n".join([load, run, mock]    )]

      # should succeed
      _UpperCAmelCase     							=   self.get_env()
      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      _UpperCAmelCase     							=   "1"
      _UpperCAmelCase     							=   subprocess.run(snake_case_	,			env=snake_case_	,			check=snake_case_	,			capture_output=snake_case_    )
      self.assertEqual(result.returncode	,			0	,			result.stderr    )
      self.assertIn("success"	,			result.stdout.decode()    )




 @require_torch
 def      lowercase	(					self       :    List[Any]    ):
      # python one-liner segments
      # this must be loaded before socket.socket is monkey-patched
      _UpperCAmelCase     							=   "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

      _UpperCAmelCase     							=   "\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

      _UpperCAmelCase     							=   "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")\nsocket.socket = offline_socket\n        "

      # Force fetching the files so that we can use the cache
      _UpperCAmelCase     							=   "hf-internal-testing/tiny-random-bert"
      BertConfig.from_pretrained(snake_case_    )
      BertModel.from_pretrained(snake_case_    )
      BertTokenizer.from_pretrained(snake_case_    )
      pipeline(task="fill-mask"	,			model=snake_case_    )

      # baseline - just load from_pretrained with normal network
      _UpperCAmelCase     							=   [sys.executable, "-c", "\n".join([load, run, mock]    )]

      # should succeed
      _UpperCAmelCase     							=   self.get_env()
      _UpperCAmelCase     							=   subprocess.run(snake_case_	,			env=snake_case_	,			check=snake_case_	,			capture_output=snake_case_    )
      self.assertEqual(result.returncode	,			0	,			result.stderr    )
      self.assertIn("success"	,			result.stdout.decode()    )




 @require_torch
 def      lowercase	(					self       :    Tuple    ):
      # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
      # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
      # while running an external program

      # python one-liner segments

      # this must be loaded before socket.socket is monkey-patched
      _UpperCAmelCase     							=   "\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        "

      _UpperCAmelCase     							=   "\nmname = \"hf-internal-testing/tiny-random-bert-sharded\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint(\"success\")\n        "

      _UpperCAmelCase     							=   "\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "

      # baseline - just load from_pretrained with normal network
      _UpperCAmelCase     							=   [sys.executable, "-c", "\n".join([load, run]    )]

      # should succeed
      _UpperCAmelCase     							=   self.get_env()
      _UpperCAmelCase     							=   subprocess.run(snake_case_	,			env=snake_case_	,			check=snake_case_	,			capture_output=snake_case_    )
      self.assertEqual(result.returncode	,			0	,			result.stderr    )
      self.assertIn("success"	,			result.stdout.decode()    )

      # next emulate no network
      _UpperCAmelCase     							=   [sys.executable, "-c", "\n".join([load, mock, run]    )]

      # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
      # env["TRANSFORMERS_OFFLINE"] = "0"
      # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
      # self.assertEqual(result.returncode, 1, result.stderr)

      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      _UpperCAmelCase     							=   "1"
      _UpperCAmelCase     							=   subprocess.run(snake_case_	,			env=snake_case_	,			check=snake_case_	,			capture_output=snake_case_    )
      self.assertEqual(result.returncode	,			0	,			result.stderr    )
      self.assertIn("success"	,			result.stdout.decode()    )




 @require_torch
 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   "\nfrom transformers import pipeline\n        "
      _UpperCAmelCase     							=   "\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n        "

      _UpperCAmelCase     							=   "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "
      _UpperCAmelCase     							=   self.get_env()
      _UpperCAmelCase     							=   "1"
      _UpperCAmelCase     							=   [sys.executable, "-c", "\n".join([load, mock, run]    )]
      _UpperCAmelCase     							=   subprocess.run(snake_case_	,			env=snake_case_	,			check=snake_case_	,			capture_output=snake_case_    )
      self.assertEqual(result.returncode	,			1	,			result.stderr    )
      self.assertIn(
          "You cannot infer task automatically within `pipeline` when using offline mode"	,			result.stderr.decode().replace("\n"	,			""    )	,			)




 @require_torch
 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   "\nfrom transformers import AutoModel\n        "
      _UpperCAmelCase     							=   "\nmname = \"hf-internal-testing/test_dynamic_model\"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint(\"success\")\n        "

      # baseline - just load from_pretrained with normal network
      _UpperCAmelCase     							=   [sys.executable, "-c", "\n".join([load, run]    )]

      # should succeed
      _UpperCAmelCase     							=   self.get_env()
      _UpperCAmelCase     							=   subprocess.run(snake_case_	,			env=snake_case_	,			check=snake_case_	,			capture_output=snake_case_    )
      self.assertEqual(result.returncode	,			0	,			result.stderr    )
      self.assertIn("success"	,			result.stdout.decode()    )

      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      _UpperCAmelCase     							=   "1"
      _UpperCAmelCase     							=   subprocess.run(snake_case_	,			env=snake_case_	,			check=snake_case_	,			capture_output=snake_case_    )
      self.assertEqual(result.returncode	,			0	,			result.stderr    )
      self.assertIn("success"	,			result.stdout.decode()    )











import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

a__:      List[Any]									=		logging.getLogger()


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						argparse.ArgumentParser()
       parser.add_argument('''-f'''  )
       A__											=						parser.parse_args()
       return args.f




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self ):
        A__											=						logging.StreamHandler(sys.stdout )
        logger.addHandler(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						get_gpu_count()

        if n_gpu > 1:
               pass
               # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
               # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
               # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
               # cmd = [sys.executable] + distributed_args + args
               # execute_subprocess_async(cmd, env=self.get_env())
               # XXX: test the results - need to save them first into .json file
        else:
               args.insert(0,'''run_glue_deebert.py''' )
               with patch.object(__lowerCamelCase,'''argv''',__lowerCamelCase ):
                      A__											=						run_glue_deebert.main()
                      for value in result.values():
                             self.assertGreaterEqual(__lowerCamelCase,0.666 )




 @slow
 @require_torch_non_multi_gpu
 def 				UpperCamelCase			(							self ):
        A__											=						'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
        self.run_and_check(__lowerCamelCase )

        A__											=						'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
        self.run_and_check(__lowerCamelCase )

        A__											=						'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
        self.run_and_check(__lowerCamelCase )





