


import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



def        UpperCamelCase_(   lowerCamelCase_				)					->		Optional[int]:
    return 1.0 / (1.0 + np.exp(-_outputs				))



def        UpperCamelCase_(   lowerCamelCase_				)					->		Any:
    _lowercase :							Optional[int]   	=	np.max(_outputs							,				axis=-1							,				keepdims=__UpperCAmelCase				)
    _lowercase :							Tuple   	=	np.exp(_outputs - maxes				)
    return shifted_exp / shifted_exp.sum(axis=-1							,				keepdims=__UpperCAmelCase				)






class _lowerCamelCase(		_snake_case     ):
       lowercase_				:			Any									=    'sigmoid'
       lowercase_				:			Tuple									=    'softmax'
       lowercase_				:			Optional[Any]									=    'none'






@add_end_docstrings(
    _snake_case,				R"""\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    """,				)
class _lowerCamelCase(		_snake_case     ):
       lowercase_				:			Optional[int]									=    False
       lowercase_				:			List[str]									=    ClassificationFunction.NONE
       def __init__(      self,   **lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           super().__init__(**lowerCamelCase)

           self.check_model_type(
               TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
               if self.framework == 'tf'
               else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)
       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase="",   **lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							Dict   	=	tokenizer_kwargs

           _lowercase :							List[str]   	=	{}
           if hasattr(self.model.config,   'return_all_scores') and return_all_scores is None:
               _lowercase :							Any   	=	self.model.config.return_all_scores

           if isinstance(lowerCamelCase,   lowerCamelCase) or top_k is None:
               _lowercase :							Optional[int]   	=	top_k
               _lowercase :							Tuple   	=	False
           elif return_all_scores is not None:
               warnings.warn(
                   '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
                   ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.',   lowerCamelCase,   )
               if return_all_scores:
                   _lowercase :							Any   	=	None
               else:
                   _lowercase :							str   	=	1

           if isinstance(lowerCamelCase,   lowerCamelCase):
               _lowercase :							List[str]   	=	ClassificationFunction[function_to_apply.upper()]

           if function_to_apply is not None:
               _lowercase :							Dict   	=	function_to_apply
           return preprocess_params, {}, postprocess_params
       def __call__(      self,   *lowerCamelCase,   **lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	super().__call__(*lowerCamelCase,   **lowerCamelCase)
           # TODO try and retrieve it in a nicer way from _sanitize_parameters.
           _lowercase :							Optional[Any]   	=	'top_k' not in kwargs
           if isinstance(args[0],   lowerCamelCase) and _legacy:
               # This pipeline is odd, and return a list when single item is run
               return [result]
           else:
               return result
       def     UpperCamelCase  (      self,   lowerCamelCase,   **lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.framework
           if isinstance(lowerCamelCase,   lowerCamelCase):
               return self.tokenizer(**lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)
           elif isinstance(lowerCamelCase,   lowerCamelCase) and len(lowerCamelCase) == 1 and isinstance(inputs[0],   lowerCamelCase) and len(inputs[0]) == 2:
               # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
               return self.tokenizer(
                   text=inputs[0][0],   text_pair=inputs[0][1],   return_tensors=lowerCamelCase,   **lowerCamelCase)
           elif isinstance(lowerCamelCase,   lowerCamelCase):
               # This is likely an invalid usage of the pipeline attempting to pass text pairs.
               raise ValueError(
                   'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
                   ' dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair.')
           return self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     int:




           """simple docstring"""

           return self.model(**lowerCamelCase)




       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=None,   lowerCamelCase=1,   lowerCamelCase=True) ->     Dict:




           """simple docstring"""

           if function_to_apply is None:
               if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                   _lowercase :							List[str]   	=	ClassificationFunction.SIGMOID
               elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                   _lowercase :							int   	=	ClassificationFunction.SOFTMAX
               elif hasattr(self.model.config,   'function_to_apply') and function_to_apply is None:
                   _lowercase :							Tuple   	=	self.model.config.function_to_apply
               else:
                   _lowercase :							Optional[int]   	=	ClassificationFunction.NONE

           _lowercase :							Union[str, Any]   	=	model_outputs['logits'][0]
           _lowercase :							Tuple   	=	outputs.numpy()

           if function_to_apply == ClassificationFunction.SIGMOID:
               _lowercase :							Union[str, Any]   	=	sigmoid(lowerCamelCase)
           elif function_to_apply == ClassificationFunction.SOFTMAX:
               _lowercase :							List[str]   	=	softmax(lowerCamelCase)
           elif function_to_apply == ClassificationFunction.NONE:
               _lowercase :							Union[str, Any]   	=	outputs
           else:
               raise ValueError(F'''Unrecognized `function_to_apply` argument: {function_to_apply}''')

           if top_k == 1 and _legacy:
               return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

           _lowercase :							Dict   	=	[
               {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(lowerCamelCase)
           ]
           if not _legacy:
               dict_scores.sort(key=lambda lowerCamelCase: x["score"],   reverse=lowerCamelCase)
               if top_k is not None:
                   _lowercase :							int   	=	dict_scores[:top_k]
           return dict_scores






import subprocess
import sys

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






class _lowerCamelCase(		_a     ):
       @require_torch
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

           _lowercase :							Union[str, Any]   	=	'\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        '

           _lowercase :							List[str]   	=	'\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
           _lowercase :							Union[str, Any]   	=	'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
           _lowercase :							Any   	=	[sys.executable, '-c', '\n'.join([load, run, mock])]

           # should succeed
           _lowercase :							List[str]   	=	self.get_env()
           # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
           _lowercase :							Any   	=	'1'
           _lowercase :							List[Any]   	=	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) ->     List[Any]:




           """simple docstring"""

           _lowercase :							int   	=	'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

           _lowercase :							Union[str, Any]   	=	'\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        '

           _lowercase :							List[Any]   	=	'\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
           _lowercase :							Any   	=	'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
           _lowercase :							List[str]   	=	[sys.executable, '-c', '\n'.join([load, run, mock])]

           # should succeed
           _lowercase :							Any   	=	self.get_env()
           _lowercase :							str   	=	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"""

           _lowercase :							Tuple   	=	'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

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

           _lowercase :							List[Any]   	=	'\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
           _lowercase :							int   	=	[sys.executable, '-c', '\n'.join([load, run])]

           # should succeed
           _lowercase :							List[str]   	=	self.get_env()
           _lowercase :							Dict   	=	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
           _lowercase :							Union[str, Any]   	=	[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
           _lowercase :							Optional[Any]   	=	'1'
           _lowercase :							Optional[Any]   	=	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) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	'\nfrom transformers import pipeline\n        '
           _lowercase :							Dict   	=	'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

           _lowercase :							Optional[Any]   	=	'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
           _lowercase :							Tuple   	=	self.get_env()
           _lowercase :							Tuple   	=	'1'
           _lowercase :							Union[str, Any]   	=	[sys.executable, '-c', '\n'.join([load, mock, run])]
           _lowercase :							Tuple   	=	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) ->     Tuple:




           """simple docstring"""

           _lowercase :							Dict   	=	'\nfrom transformers import AutoModel\n        '
           _lowercase :							int   	=	'\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
           _lowercase :							Optional[int]   	=	[sys.executable, '-c', '\n'.join([load, run])]

           # should succeed
           _lowercase :							int   	=	self.get_env()
           _lowercase :							List[str]   	=	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
           _lowercase :							Tuple   	=	'1'
           _lowercase :							Dict   	=	subprocess.run(lowerCamelCase,   env=lowerCamelCase,   check=lowerCamelCase,   capture_output=lowerCamelCase)
           self.assertEqual(result.returncode,   0,   result.stderr)
           self.assertIn('success',   result.stdout.decode())


