






import subprocess
import sys

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



class       __UpperCAmelCase		(lowercase__    ):







			@require_torch
			def 							UpperCamelCase      (	self:  str     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

									_SCREAMING_SNAKE_CASE	      =  "\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        "

									_SCREAMING_SNAKE_CASE	      =  "\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
									_SCREAMING_SNAKE_CASE	      =  "hf-internal-testing/tiny-random-bert"
									BertConfig.from_pretrained(__lowerCamelCase     )
									BertModel.from_pretrained(__lowerCamelCase     )
									BertTokenizer.from_pretrained(__lowerCamelCase     )
									pipeline(task="""fill-mask"""      ,	model=__lowerCamelCase     )

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

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







			@require_torch
			def 							UpperCamelCase      (	self:  Any     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

									_SCREAMING_SNAKE_CASE	      =  "\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        "

									_SCREAMING_SNAKE_CASE	      =  "\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
									_SCREAMING_SNAKE_CASE	      =  "hf-internal-testing/tiny-random-bert"
									BertConfig.from_pretrained(__lowerCamelCase     )
									BertModel.from_pretrained(__lowerCamelCase     )
									BertTokenizer.from_pretrained(__lowerCamelCase     )
									pipeline(task="""fill-mask"""      ,	model=__lowerCamelCase     )

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

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







			@require_torch
			def 							UpperCamelCase      (	self:  Any     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  "\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        "

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

									_SCREAMING_SNAKE_CASE	      =  "\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
									_SCREAMING_SNAKE_CASE	      =  [sys.executable, "-c", "\n".join([load, run]     )]

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

									# next emulate no network
									_SCREAMING_SNAKE_CASE	      =  [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
									_SCREAMING_SNAKE_CASE	      =  "1"
									_SCREAMING_SNAKE_CASE	      =  subprocess.run(__lowerCamelCase      ,	env=__lowerCamelCase      ,	check=__lowerCamelCase      ,	capture_output=__lowerCamelCase     )
									self.assertEqual(result.returncode      ,	0      ,	result.stderr     )
									self.assertIn("""success"""      ,	result.stdout.decode()     )







			@require_torch
			def 							UpperCamelCase      (	self:  Union[str, Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  "\nfrom transformers import pipeline\n        "
									_SCREAMING_SNAKE_CASE	      =  "\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n        "

									_SCREAMING_SNAKE_CASE	      =  "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "
									_SCREAMING_SNAKE_CASE	      =  self.get_env()
									_SCREAMING_SNAKE_CASE	      =  "1"
									_SCREAMING_SNAKE_CASE	      =  [sys.executable, "-c", "\n".join([load, mock, run]     )]
									_SCREAMING_SNAKE_CASE	      =  subprocess.run(__lowerCamelCase      ,	env=__lowerCamelCase      ,	check=__lowerCamelCase      ,	capture_output=__lowerCamelCase     )
									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 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  "\nfrom transformers import AutoModel\n        "
									_SCREAMING_SNAKE_CASE	      =  "\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
									_SCREAMING_SNAKE_CASE	      =  [sys.executable, "-c", "\n".join([load, run]     )]

									# should succeed
									_SCREAMING_SNAKE_CASE	      =  self.get_env()
									_SCREAMING_SNAKE_CASE	      =  subprocess.run(__lowerCamelCase      ,	env=__lowerCamelCase      ,	check=__lowerCamelCase      ,	capture_output=__lowerCamelCase     )
									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
									_SCREAMING_SNAKE_CASE	      =  "1"
									_SCREAMING_SNAKE_CASE	      =  subprocess.run(__lowerCamelCase      ,	env=__lowerCamelCase      ,	check=__lowerCamelCase      ,	capture_output=__lowerCamelCase     )
									self.assertEqual(result.returncode      ,	0      ,	result.stderr     )
									self.assertIn("""success"""      ,	result.stdout.decode()     )





import datasets

from .evaluate import evaluate


A      :    Dict		=					"\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

A      :    Any		=					"\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

A      :    str		=					"\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,  _KWARGS_DESCRIPTION)
class 			_lowercase (							datasets.Metric):


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             return datasets.MetricInfo(
                 description=_DESCRIPTION					,      citation=_CITATION					,      inputs_description=_KWARGS_DESCRIPTION					,      features=datasets.Features(
                     {
                         "predictions": {
                             "id": datasets.Value("string"       ),
                             "prediction_text": datasets.features.Sequence(datasets.Value("string"       )       ),
                         },
                         "references": {
                             "id": datasets.Value("string"       ),
                             "answers": datasets.features.Sequence(
                                 {
                                     "text": datasets.Value("string"       ),
                                     "answer_start": datasets.Value("int32"       ),
                                 }       ),
                         },
                     }       )					,      codebase_urls=["https://www.atticusprojectai.org/cuad"]					,      reference_urls=["https://www.atticusprojectai.org/cuad"]					,      )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
             lowerCamelCase__		:				Any    =  [
                 {
                     "paragraphs": [
                         {
                             "qas": [
                                 {
                                     "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                                     "id": ref["id"],
                                 }
                                 for ref in references
                             ]
                         }
                     ]
                 }
             ]
             lowerCamelCase__		:				Optional[int]    =  evaluate(dataset=__lowerCamelCase					,      predictions=__lowerCamelCase       )
             return score


