"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
   from huggingface_hub import snapshot_download
   from pyctcdecode import BeamSearchDecoderCTC

   from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
   from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
   from transformers import WavaVecaForCTC






@require_pyctcdecode
class 				snake_case__     (   unittest.TestCase   ):


 def 		a__ (				self		):
        __a     				=  "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
        __a     				=  dict(zip(lowerCamelCase  ,						range(len(lowerCamelCase		)		)		)		)

        __a     				=  {
            "unk_token": "<unk>",
            "bos_token": "<s>",
            "eos_token": "</s>",
        }
        __a     				=  {
            "feature_size": 1,
            "padding_value": 0.0,
            "sampling_rate": 16000,
            "return_attention_mask": False,
            "do_normalize": True,
        }

        __a     				=  tempfile.mkdtemp()
        __a     				=  os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["vocab_file"]		)
        __a     				=  os.path.join(self.tmpdirname  ,						lowerCamelCase		)
        with open(self.vocab_file  ,						"w"  ,						encoding="utf-8"		) as fp:
               fp.write(json.dumps(lowerCamelCase		) + "\n"		)

        with open(self.feature_extraction_file  ,						"w"  ,						encoding="utf-8"		) as fp:
               fp.write(json.dumps(lowerCamelCase		) + "\n"		)

        # load decoder from hub
        __a     				=  "hf-internal-testing/ngram-beam-search-decoder"


 def 		a__ (				self  ,						**lowerCamelCase		):
        __a     				=  self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCamelCase		)
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						**lowerCamelCase		):
        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						**lowerCamelCase		):
        return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name  ,						**lowerCamelCase		)


 def 		a__ (				self		):
        shutil.rmtree(self.tmpdirname		)


 def 		a__ (				self		):
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        processor.save_pretrained(self.tmpdirname		)
        __a     				=  WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname		)

        # tokenizer
        self.assertEqual(processor.tokenizer.get_vocab()  ,						tokenizer.get_vocab()		)
        self.assertIsInstance(processor.tokenizer  ,						lowerCamelCase		)

        # feature extractor
        self.assertEqual(processor.feature_extractor.to_json_string()  ,						feature_extractor.to_json_string()		)
        self.assertIsInstance(processor.feature_extractor  ,						lowerCamelCase		)

        # decoder
        self.assertEqual(processor.decoder._alphabet.labels  ,						decoder._alphabet.labels		)
        self.assertEqual(
            processor.decoder.model_container[decoder._model_key]._unigram_set  ,						decoder.model_container[decoder._model_key]._unigram_set  ,						)
        self.assertIsInstance(processor.decoder  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  WavaVecaProcessorWithLM(
            tokenizer=self.get_tokenizer()  ,						feature_extractor=self.get_feature_extractor()  ,						decoder=self.get_decoder()		)
        processor.save_pretrained(self.tmpdirname		)

        # make sure that error is thrown when decoder alphabet doesn't match
        __a     				=  WavaVecaProcessorWithLM.from_pretrained(
            self.tmpdirname  ,						alpha=5.0  ,						beta=3.0  ,						score_boundary=-7.0  ,						unk_score_offset=3		)

        # decoder
        self.assertEqual(processor.language_model.alpha  ,						5.0		)
        self.assertEqual(processor.language_model.beta  ,						3.0		)
        self.assertEqual(processor.language_model.score_boundary  ,						-7.0		)
        self.assertEqual(processor.language_model.unk_score_offset  ,						3		)


 def 		a__ (				self		):
        __a     				=  self.get_tokenizer()
        # add token to trigger raise
        tokenizer.add_tokens(["xx"]		)
        with self.assertRaisesRegex(lowerCamelCase  ,						"include"		):
               WavaVecaProcessorWithLM(
                   tokenizer=lowerCamelCase  ,						feature_extractor=self.get_feature_extractor()  ,						decoder=self.get_decoder()		)


 def 		a__ (				self		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        __a     				=  floats_list((3, 1000)		)

        __a     				=  feature_extractor(lowerCamelCase  ,						return_tensors="np"		)
        __a     				=  processor(lowerCamelCase  ,						return_tensors="np"		)

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()  ,						input_processor[key].sum()  ,						delta=1E-2		)


 def 		a__ (				self		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        __a     				=  "This is a test string"

        __a     				=  processor(text=lowerCamelCase		)

        __a     				=  tokenizer(lowerCamelCase		)

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]  ,						encoded_processor[key]		)


 def 		a__ (				self  ,						lowerCamelCase=(2, 10, 16)  ,						lowerCamelCase=77		):
        np.random.seed(lowerCamelCase		)
        return np.random.rand(*lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        __a     				=  self._get_dummy_logits(shape=(10, 16)  ,						seed=13		)

        __a     				=  processor.decode(lowerCamelCase		)

        __a     				=  decoder.decode_beams(lowerCamelCase		)[0]

        self.assertEqual(decoded_decoder[0]  ,						decoded_processor.text		)
        self.assertEqual("</s> <s> </s>"  ,						decoded_processor.text		)
        self.assertEqual(decoded_decoder[-2]  ,						decoded_processor.logit_score		)
        self.assertEqual(decoded_decoder[-1]  ,						decoded_processor.lm_score		)


 @parameterized.expand([[None], ["fork"], ["spawn"]]		)
 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        __a     				=  self._get_dummy_logits()

        # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
        #       otherwise, the LM won't be available to the pool's sub-processes.
        # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
        if pool_context is None:
               __a     				=  processor.batch_decode(lowerCamelCase		)
        else:
               with get_context(lowerCamelCase		).Pool() as pool:
                      __a     				=  processor.batch_decode(lowerCamelCase  ,						lowerCamelCase		)

        __a     				=  list(lowerCamelCase		)

        with get_context("fork"		).Pool() as p:
               __a     				=  decoder.decode_beams_batch(lowerCamelCase  ,						lowerCamelCase		)

        __a				, __a				, __a     				=  [], [], []
        for beams in decoded_beams:
               texts_decoder.append(beams[0][0]		)
               logit_scores_decoder.append(beams[0][-2]		)
               lm_scores_decoder.append(beams[0][-1]		)

        self.assertListEqual(lowerCamelCase  ,						decoded_processor.text		)
        self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"]  ,						decoded_processor.text		)
        self.assertListEqual(lowerCamelCase  ,						decoded_processor.logit_score		)
        self.assertListEqual(lowerCamelCase  ,						decoded_processor.lm_score		)


 def 		a__ (				self		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        __a     				=  self._get_dummy_logits()

        __a     				=  15
        __a     				=  -20.0
        __a     				=  -4.0

        __a     				=  processor.batch_decode(
            lowerCamelCase  ,						beam_width=lowerCamelCase  ,						beam_prune_logp=lowerCamelCase  ,						token_min_logp=lowerCamelCase  ,						)
        __a     				=  decoded_processor_out.text

        __a     				=  list(lowerCamelCase		)

        with get_context("fork"		).Pool() as pool:
               __a     				=  decoder.decode_beams_batch(
                   lowerCamelCase  ,						lowerCamelCase  ,						beam_width=lowerCamelCase  ,						beam_prune_logp=lowerCamelCase  ,						token_min_logp=lowerCamelCase  ,						)

        __a     				=  [d[0][0] for d in decoded_decoder_out]
        __a     				=  [d[0][2] for d in decoded_decoder_out]
        __a     				=  [d[0][3] for d in decoded_decoder_out]

        self.assertListEqual(lowerCamelCase  ,						lowerCamelCase		)
        self.assertListEqual(["</s> <s> <s>", "<s> <s> <s>"]  ,						lowerCamelCase		)

        self.assertTrue(np.array_equal(lowerCamelCase  ,						decoded_processor_out.logit_score		)		)
        self.assertTrue(np.allclose([-20.054, -18.447]  ,						lowerCamelCase  ,						atol=1E-3		)		)

        self.assertTrue(np.array_equal(lowerCamelCase  ,						decoded_processor_out.lm_score		)		)
        self.assertTrue(np.allclose([-15.554, -13.9474]  ,						lowerCamelCase  ,						atol=1E-3		)		)


 def 		a__ (				self		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        __a     				=  self._get_dummy_logits()

        __a     				=  2.0
        __a     				=  5.0
        __a     				=  -20.0
        __a     				=  True

        __a     				=  processor.batch_decode(
            lowerCamelCase  ,						alpha=lowerCamelCase  ,						beta=lowerCamelCase  ,						unk_score_offset=lowerCamelCase  ,						lm_score_boundary=lowerCamelCase  ,						)
        __a     				=  decoded_processor_out.text

        __a     				=  list(lowerCamelCase		)
        decoder.reset_params(
            alpha=lowerCamelCase  ,						beta=lowerCamelCase  ,						unk_score_offset=lowerCamelCase  ,						lm_score_boundary=lowerCamelCase  ,						)

        with get_context("fork"		).Pool() as pool:
               __a     				=  decoder.decode_beams_batch(
                   lowerCamelCase  ,						lowerCamelCase  ,						)

        __a     				=  [d[0][0] for d in decoded_decoder_out]

        self.assertListEqual(lowerCamelCase  ,						lowerCamelCase		)
        self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]  ,						lowerCamelCase		)
        __a     				=  processor.decoder.model_container[processor.decoder._model_key]
        self.assertEqual(lm_model.alpha  ,						2.0		)
        self.assertEqual(lm_model.beta  ,						5.0		)
        self.assertEqual(lm_model.unk_score_offset  ,						-20.0		)
        self.assertEqual(lm_model.score_boundary  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"		)

        __a     				=  processor.decoder.model_container[processor.decoder._model_key]
        __a     				=  Path(language_model._kenlm_model.path.decode("utf-8"		)		).parent.parent.absolute()

        __a     				=  os.listdir(lowerCamelCase		)
        __a     				=  ["alphabet.json", "language_model"]

        downloaded_decoder_files.sort()
        expected_decoder_files.sort()

        # test that only decoder relevant files from
        # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
        # are downloaded and none of the rest (e.g. README.md, ...)
        self.assertListEqual(lowerCamelCase  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  snapshot_download("hf-internal-testing/processor_with_lm"		)

        __a     				=  WavaVecaProcessorWithLM.from_pretrained(lowerCamelCase		)

        __a     				=  processor.decoder.model_container[processor.decoder._model_key]
        __a     				=  Path(language_model._kenlm_model.path.decode("utf-8"		)		).parent.parent.absolute()

        __a     				=  os.listdir(lowerCamelCase		)
        __a     				=  os.listdir(lowerCamelCase		)

        local_decoder_files.sort()
        expected_decoder_files.sort()

        # test that both decoder form hub and local files in cache are the same
        self.assertListEqual(lowerCamelCase  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"		)
        __a     				=  AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"		)

        __a     				=  floats_list((3, 1000)		)

        __a     				=  processor_wavaveca(lowerCamelCase  ,						return_tensors="np"		)
        __a     				=  processor_auto(lowerCamelCase  ,						return_tensors="np"		)

        for key in input_wavaveca.keys():
               self.assertAlmostEqual(input_wavaveca[key].sum()  ,						input_auto[key].sum()  ,						delta=1E-2		)

        __a     				=  self._get_dummy_logits()

        __a     				=  processor_wavaveca.batch_decode(lowerCamelCase		)
        __a     				=  processor_auto.batch_decode(lowerCamelCase		)

        self.assertListEqual(decoded_wavaveca.text  ,						decoded_auto.text		)


 def 		a__ (				self		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        self.assertListEqual(
            processor.model_input_names  ,						feature_extractor.model_input_names  ,						msg="`processor` and `feature_extractor` model input names do not match"  ,						)


 @staticmethod
 def 		a__ (				lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  [d[key] for d in offsets]
        return retrieved_list


 def 		a__ (				self		):
        __a     				=  WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"		)
        __a     				=  self._get_dummy_logits()[0]

        __a     				=  processor.decode(lowerCamelCase  ,						output_word_offsets=lowerCamelCase		)
        # check Wav2Vec2CTCTokenizerOutput keys for word
        self.assertEqual(len(outputs.keys()		)  ,						4		)
        self.assertTrue("text" in outputs		)
        self.assertTrue("word_offsets" in outputs		)
        self.assertTrue(isinstance(lowerCamelCase  ,						lowerCamelCase		)		)

        self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"]  ,						"word"		)		)  ,						outputs.text		)
        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]  ,						"word"		)  ,						["<s>", "<s>", "</s>"]		)
        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]  ,						"start_offset"		)  ,						[0, 2, 4]		)
        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]  ,						"end_offset"		)  ,						[1, 3, 5]		)


 def 		a__ (				self		):
        __a     				=  WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"		)
        __a     				=  self._get_dummy_logits()

        __a     				=  processor.batch_decode(lowerCamelCase  ,						output_word_offsets=lowerCamelCase		)

        # check Wav2Vec2CTCTokenizerOutput keys for word
        self.assertEqual(len(outputs.keys()		)  ,						4		)
        self.assertTrue("text" in outputs		)
        self.assertTrue("word_offsets" in outputs		)
        self.assertTrue(isinstance(lowerCamelCase  ,						lowerCamelCase		)		)

        self.assertListEqual(
            [" ".join(self.get_from_offsets(lowerCamelCase  ,						"word"		)		) for o in outputs["word_offsets"]]  ,						outputs.text		)
        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]  ,						"word"		)  ,						["<s>", "<s>", "</s>"]		)
        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]  ,						"start_offset"		)  ,						[0, 2, 4]		)
        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]  ,						"end_offset"		)  ,						[1, 3, 5]		)






 @slow
 @require_torch
 @require_torchaudio
 def 		a__ (				self		):
        import torch

        __a     				=  load_dataset("common_voice"  ,						"en"  ,						split="train"  ,						streaming=lowerCamelCase		)
        __a     				=  ds.cast_column("audio"  ,						datasets.Audio(sampling_rate=16000		)		)
        __a     				=  iter(lowerCamelCase		)
        __a     				=  next(lowerCamelCase		)

        __a     				=  AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"		)
        __a     				=  WavaVecaForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"		)

        # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
        __a     				=  processor(sample["audio"]["array"]  ,						return_tensors="pt"		).input_values

        with torch.no_grad():
               __a     				=  model(lowerCamelCase		).logits.cpu().numpy()

        __a     				=  processor.decode(logits[0]  ,						output_word_offsets=lowerCamelCase		)

        __a     				=  model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
        __a     				=  [
            {
                "start_time": d["start_offset"] * time_offset,
                "end_time": d["end_offset"] * time_offset,
                "word": d["word"],
            }
            for d in output["word_offsets"]
        ]

        __a     				=  "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"

        # output words
        self.assertEqual(" ".join(self.get_from_offsets(lowerCamelCase  ,						"word"		)		)  ,						lowerCamelCase		)
        self.assertEqual(" ".join(self.get_from_offsets(lowerCamelCase  ,						"word"		)		)  ,						output.text		)

        # output times
        __a     				=  torch.tensor(self.get_from_offsets(lowerCamelCase  ,						"start_time"		)		)
        __a     				=  torch.tensor(self.get_from_offsets(lowerCamelCase  ,						"end_time"		)		)

        # fmt: off
        __a     				=  torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]		)
        __a     				=  torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]		)
        # fmt: on

        self.assertTrue(torch.allclose(lowerCamelCase  ,						lowerCamelCase  ,						atol=0.01		)		)
        self.assertTrue(torch.allclose(lowerCamelCase  ,						lowerCamelCase  ,						atol=0.01		)		)

"""simple docstring"""



import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)



def 					_lowerCamelCase(					a							):
       __a     				=  torch.exp(a							)
       __a     				=  torch.sum(a ,  dim=1							)  # sum of exp(x_i)
       __a     				=  torch.sum(x * exp_x ,  dim=1							)  # sum of x_i * exp(x_i)
       return torch.log(a							) - B / A






class 				snake_case__     (   nn.Module   ):


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__()
        __a     				=  config.output_attentions
        __a     				=  config.output_hidden_states
        __a     				=  nn.ModuleList([BertLayer(lowerCamelCase		) for _ in range(config.num_hidden_layers		)]		)
        __a     				=  nn.ModuleList([BertHighway(lowerCamelCase		) for _ in range(config.num_hidden_layers		)]		)

        __a     				=  [-1 for _ in range(config.num_hidden_layers		)]


 def 		a__ (				self  ,						lowerCamelCase		):
        if (type(lowerCamelCase		) is float) or (type(lowerCamelCase		) is int):
               for i in range(len(self.early_exit_entropy		)		):
                      __a     				=  x
        else:
               __a     				=  x


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  pooler.state_dict()
        for highway in self.highway:
               for name, param in highway.pooler.state_dict().items():
                      param.copy_(loaded_model[name]		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						):
        __a     				=  ()
        __a     				=  ()
        __a     				=  ()
        for i, layer_module in enumerate(self.layer		):
               if self.output_hidden_states:
                      __a     				=  all_hidden_states + (hidden_states,)

               __a     				=  layer_module(
                   lowerCamelCase  ,						lowerCamelCase  ,						head_mask[i]  ,						lowerCamelCase  ,						lowerCamelCase		)
               __a     				=  layer_outputs[0]

               if self.output_attentions:
                      __a     				=  all_attentions + (layer_outputs[1],)

               __a     				=  (hidden_states,)
               if self.output_hidden_states:
                      __a     				=  current_outputs + (all_hidden_states,)
               if self.output_attentions:
                      __a     				=  current_outputs + (all_attentions,)

               __a     				=  self.highway[i](lowerCamelCase		)
               # logits, pooled_output

               if not self.training:
                      __a     				=  highway_exit[0]
                      __a     				=  entropy(lowerCamelCase		)
                      __a     				=  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                      __a     				=  all_highway_exits + (highway_exit,)

                      if highway_entropy < self.early_exit_entropy[i]:
                             __a     				=  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                             raise HighwayException(lowerCamelCase  ,						i + 1		)
               else:
                      __a     				=  all_highway_exits + (highway_exit,)

        # Add last layer
        if self.output_hidden_states:
               __a     				=  all_hidden_states + (hidden_states,)

        __a     				=  (hidden_states,)
        if self.output_hidden_states:
               __a     				=  outputs + (all_hidden_states,)
        if self.output_attentions:
               __a     				=  outputs + (all_attentions,)

        __a     				=  outputs + (all_highway_exits,)
        return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    """The Bert Model transformer with early exiting (DeeBERT). """,		snake_case_,		)
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__(lowerCamelCase		)
        __a     				=  config

        __a     				=  BertEmbeddings(lowerCamelCase		)
        __a     				=  DeeBertEncoder(lowerCamelCase		)
        __a     				=  BertPooler(lowerCamelCase		)

        self.init_weights()


 def 		a__ (				self		):
        self.encoder.init_highway_pooler(self.pooler		)


 def 		a__ (				self		):
        return self.embeddings.word_embeddings


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  value


 def 		a__ (				self  ,						lowerCamelCase		):
        for layer, heads in heads_to_prune.items():
               self.encoder.layer[layer].attention.prune_heads(lowerCamelCase		)






 @add_start_docstrings_to_model_forward(lowerCamelCase		)
 def 		a__ (				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						):
        if input_ids is not None and inputs_embeds is not None:
               raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"		)
        elif input_ids is not None:
               __a     				=  input_ids.size()
        elif inputs_embeds is not None:
               __a     				=  inputs_embeds.size()[:-1]
        else:
               raise ValueError("You have to specify either input_ids or inputs_embeds"		)

        __a     				=  input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
               __a     				=  torch.ones(lowerCamelCase  ,						device=lowerCamelCase		)
        if encoder_attention_mask is None:
               __a     				=  torch.ones(lowerCamelCase  ,						device=lowerCamelCase		)
        if token_type_ids is None:
               __a     				=  torch.zeros(lowerCamelCase  ,						dtype=torch.long  ,						device=lowerCamelCase		)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        __a     				=  self.get_extended_attention_mask(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if encoder_attention_mask.dim() == 3:
               __a     				=  encoder_attention_mask[:, None, :, :]
        if encoder_attention_mask.dim() == 2:
               __a     				=  encoder_attention_mask[:, None, None, :]

        __a     				=  encoder_extended_attention_mask.to(
            dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
        __a     				=  (1.0 - encoder_extended_attention_mask) * -1_0000.0

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        __a     				=  self.get_head_mask(lowerCamelCase  ,						self.config.num_hidden_layers		)

        __a     				=  self.embeddings(
            input_ids=lowerCamelCase  ,						position_ids=lowerCamelCase  ,						token_type_ids=lowerCamelCase  ,						inputs_embeds=lowerCamelCase		)
        __a     				=  self.encoder(
            lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						head_mask=lowerCamelCase  ,						encoder_hidden_states=lowerCamelCase  ,						encoder_attention_mask=lowerCamelCase  ,						)
        __a     				=  encoder_outputs[0]
        __a     				=  self.pooler(lowerCamelCase		)

        __a     				=  (
            sequence_output,
            pooled_output,
        ) + encoder_outputs[
            1:
        ]  # add hidden_states and attentions if they are here
        return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  message
        __a     				=  exit_layer  # start from 1!






class 				snake_case__     (   nn.Module   ):


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__()
        __a     				=  BertPooler(lowerCamelCase		)
        __a     				=  nn.Dropout(config.hidden_dropout_prob		)
        __a     				=  nn.Linear(config.hidden_size  ,						config.num_labels		)






 def 		a__ (				self  ,						lowerCamelCase		):
        # Pooler
        __a     				=  encoder_outputs[0]
        __a     				=  self.pooler(lowerCamelCase		)
        # "return" pooler_output

        # BertModel
        __a     				=  (pooler_input, pooler_output) + encoder_outputs[1:]
        # "return" bmodel_output

        # Dropout and classification
        __a     				=  bmodel_output[1]

        __a     				=  self.dropout(lowerCamelCase		)
        __a     				=  self.classifier(lowerCamelCase		)

        return logits, pooled_output






@add_start_docstrings(
    """Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. """,		snake_case_,		)
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__(lowerCamelCase		)
        __a     				=  config.num_labels
        __a     				=  config.num_hidden_layers

        __a     				=  DeeBertModel(lowerCamelCase		)
        __a     				=  nn.Dropout(config.hidden_dropout_prob		)
        __a     				=  nn.Linear(config.hidden_size  ,						self.config.num_labels		)

        self.init_weights()






 @add_start_docstrings_to_model_forward(lowerCamelCase		)
 def 		a__ (				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=-1  ,						lowerCamelCase=False  ,						):
        __a     				=  self.num_layers
        try:
               __a     				=  self.bert(
                   lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						token_type_ids=lowerCamelCase  ,						position_ids=lowerCamelCase  ,						head_mask=lowerCamelCase  ,						inputs_embeds=lowerCamelCase  ,						)
               # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

               __a     				=  outputs[1]

               __a     				=  self.dropout(lowerCamelCase		)
               __a     				=  self.classifier(lowerCamelCase		)
               __a     				=  (logits,) + outputs[2:]  # add hidden states and attention if they are here
        except HighwayException as e:
               __a     				=  e.message
               __a     				=  e.exit_layer
               __a     				=  outputs[0]

        if not self.training:
               __a     				=  entropy(lowerCamelCase		)
               __a     				=  []
               __a     				=  []
        if labels is not None:
               if self.num_labels == 1:
                      #  We are doing regression
                      __a     				=  MSELoss()
                      __a     				=  loss_fct(logits.view(-1		)  ,						labels.view(-1		)		)
               else:
                      __a     				=  CrossEntropyLoss()
                      __a     				=  loss_fct(logits.view(-1  ,						self.num_labels		)  ,						labels.view(-1		)		)

               # work with highway exits
               __a     				=  []
               for highway_exit in outputs[-1]:
                      __a     				=  highway_exit[0]
                      if not self.training:
                             highway_logits_all.append(lowerCamelCase		)
                             highway_entropy.append(highway_exit[2]		)
                      if self.num_labels == 1:
                             #  We are doing regression
                             __a     				=  MSELoss()
                             __a     				=  loss_fct(highway_logits.view(-1		)  ,						labels.view(-1		)		)
                      else:
                             __a     				=  CrossEntropyLoss()
                             __a     				=  loss_fct(highway_logits.view(-1  ,						self.num_labels		)  ,						labels.view(-1		)		)
                      highway_losses.append(lowerCamelCase		)

               if train_highway:
                      __a     				=  (sum(highway_losses[:-1]		),) + outputs
                      # exclude the final highway, of course
               else:
                      __a     				=  (loss,) + outputs
        if not self.training:
               __a     				=  outputs + ((original_entropy, highway_entropy), exit_layer)
               if output_layer >= 0:
                      __a     				=  (
                          (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                      )  # use the highway of the last layer

        return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)
