



"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''facebook/bart-large-mnli'''
     a__									=(
         '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
         '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
         '''It returns the most likely label in the list of provided `labels` for the input text.'''
     )
     a__									='''text_classifier'''
     a__									=AutoTokenizer
     a__									=AutoModelForSequenceClassification

     a__									=['''text''', ['''text''']]
     a__									=['''text''']





     def  __lowerCAmelCase	(					self		)							->					Any:
         super().setup()
         _UpperCAmelCase							:  int											=    self.model.config
         _UpperCAmelCase							:  int											=    -1
         for idx, label in config.idalabel.items():
             if label.lower().startswith('''entail'''		):
                 _UpperCAmelCase							:  Optional[int]											=    int(A		)
         if self.entailment_id == -1:
             raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.'''		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					str:
         _UpperCAmelCase							:  Dict											=    labels
         return self.pre_processor(
             [text] * len(A		)       ,	[f'This example is {label}' for label in labels]       ,	return_tensors='''pt'''       ,	padding='''max_length'''       ,	)





     def  __lowerCAmelCase	(					self       ,	A		)							->					List[Any]:
         _UpperCAmelCase							:  str											=    outputs.logits
         _UpperCAmelCase							:  int											=    torch.argmax(logits[:, 2]		).item()
         return self._labels[label_id]







"""simple docstring"""
import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_lowerCAmelCase    :str     =					object()

# For specifying empty leaf dict `{}`
_lowerCAmelCase    :str     =					object()





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		int							):
    _UpperCAmelCase							:  Dict											=    tuple((re.compile(x + '''$'''							) for x in qs)							)
    for i in range(len(UpperCamelCase__							) - len(UpperCamelCase__							) + 1							):
        _UpperCAmelCase							:  str											=    [x.match(UpperCamelCase__							) for x, y in zip(UpperCamelCase__     ,       ks[i:]							)]
        if matches and all(UpperCamelCase__							):
            return True
    return False





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]							):
    def replace(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		Tuple							):
        for rule, replacement in rules:
            if _match(UpperCamelCase__     ,       UpperCamelCase__							):
                return replacement
        return val

    return replace





def  lowerCamelCase_						():
    return [
        # embeddings
        (("transformer", "wpe", "embedding"), P('''mp'''     ,       UpperCamelCase__							)),
        (("transformer", "wte", "embedding"), P('''mp'''     ,       UpperCamelCase__							)),
        # atention
        (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__     ,       '''mp'''							)),
        (("attention", "out_proj", "kernel"), P('''mp'''     ,       UpperCamelCase__							)),
        (("attention", "out_proj", "bias"), None),
        # mlp
        (("mlp", "c_fc", "kernel"), P(UpperCamelCase__     ,       '''mp'''							)),
        (("mlp", "c_fc", "bias"), P('''mp'''							)),
        (("mlp", "c_proj", "kernel"), P('''mp'''     ,       UpperCamelCase__							)),
        (("mlp", "c_proj", "bias"), None),
        # layer norms
        ((r"ln_\d+", "bias"), None),
        ((r"\d+", r"ln_\d+", "scale"), None),
        (("ln_f", "bias"), None),
        (("ln_f", "scale"), None),
    ]





def  lowerCamelCase_						(UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[str]											=    _get_partition_rules()
    _UpperCAmelCase							:  List[str]											=    _replacement_rules(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    {k: _unmatched for k in flatten_dict(UpperCamelCase__							)}
    _UpperCAmelCase							:  int											=    {k: replace(UpperCamelCase__     ,       UpperCamelCase__							) for k, v in initd.items()}
    assert _unmatched not in result.values(), "Incomplete partition spec."
    return freeze(unflatten_dict(UpperCamelCase__							)							)


