


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
    from ...feature_extraction_utils import FeatureExtractionMixin
    from ...tokenization_utils_base import PreTrainedTokenizerBase
    from ...utils import TensorType

__snake_case   							=						logging.get_logger(__name__)

__snake_case   							=						{
    '''openai/whisper-base''': '''https://huggingface.co/openai/whisper-base/resolve/main/config.json''',
}

# fmt: off
__snake_case   							=						[
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
    7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
    1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
    4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
    1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
    1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
    3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
__snake_case   							=						[
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
    8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
    3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
    7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
    1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
    2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
    4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]



class 	__lowerCamelCase			(_a						):
       _lowercase											=     """whisper"""
       _lowercase											=     ["""past_key_values"""]
       _lowercase											=     {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







       def __init__(			self: Union[str, Any],A_: List[str]=5_1865,A_: Tuple=80,A_: List[Any]=6,A_: Dict=4,A_: Dict=6,A_: List[str]=4,A_: List[str]=1536,A_: int=1536,A_: List[str]=0.0,A_: Any=0.0,A_: List[str]=5_0257,A_: Tuple=True,A_: Dict=True,A_: Optional[Any]="gelu",A_: Tuple=256,A_: Dict=0.0,A_: List[Any]=0.0,A_: Dict=0.0,A_: int=0.0_2,A_: List[Any]=False,A_: List[str]=1500,A_: int=448,A_: Dict=5_0256,A_: Dict=5_0256,A_: List[str]=5_0256,A_: Dict=None,A_: List[Any]=[220, 5_0256],A_: Dict=False,A_: str=256,A_: Tuple=False,A_: List[Any]=0.0_5,A_: Dict=10,A_: Optional[int]=2,A_: List[str]=0.0,A_: Optional[Any]=10,A_: Union[str, Any]=0,A_: Dict=7,**A_: List[Any],):






            '''simple docstring'''




            __UpperCamelCase =			vocab_size
            __UpperCamelCase =			num_mel_bins
            __UpperCamelCase =			d_model
            __UpperCamelCase =			encoder_layers
            __UpperCamelCase =			encoder_attention_heads
            __UpperCamelCase =			decoder_layers
            __UpperCamelCase =			decoder_attention_heads
            __UpperCamelCase =			decoder_ffn_dim
            __UpperCamelCase =			encoder_ffn_dim
            __UpperCamelCase =			dropout
            __UpperCamelCase =			attention_dropout
            __UpperCamelCase =			activation_dropout
            __UpperCamelCase =			activation_function
            __UpperCamelCase =			init_std
            __UpperCamelCase =			encoder_layerdrop
            __UpperCamelCase =			decoder_layerdrop
            __UpperCamelCase =			use_cache
            __UpperCamelCase =			encoder_layers
            __UpperCamelCase =			scale_embedding  # scale factor will be sqrt(d_model) if True
            __UpperCamelCase =			max_source_positions
            __UpperCamelCase =			max_target_positions

            # Audio Classification-specific parameters. Feel free to ignore for other classes.
            __UpperCamelCase =			classifier_proj_size
            __UpperCamelCase =			use_weighted_layer_sum

            # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
            __UpperCamelCase =			apply_spec_augment
            __UpperCamelCase =			mask_time_prob
            __UpperCamelCase =			mask_time_length
            __UpperCamelCase =			mask_time_min_masks
            __UpperCamelCase =			mask_feature_prob
            __UpperCamelCase =			mask_feature_length
            __UpperCamelCase =			mask_feature_min_masks

            __UpperCamelCase =			median_filter_width

            super().__init__(
                pad_token_id=A_,bos_token_id=A_,eos_token_id=A_,is_encoder_decoder=A_,decoder_start_token_id=A_,suppress_tokens=A_,begin_suppress_tokens=A_,**A_,)



class 	__lowerCamelCase			(_a						):







       @property
       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			OrderedDict(
                [
                    ('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
                ]					)
            if self.use_past:
                 __UpperCamelCase =			{0: 'batch'}
            else:
                 __UpperCamelCase =			{0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                 self.fill_with_past_key_values_(A_,direction='inputs'					)

            return common_inputs







       def 		snake_case_	(			self: Tuple,A_: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],A_: int = -1,A_: int = -1,A_: bool = False,A_: Optional["TensorType"] = None,A_: int = 2_2050,A_: float = 5.0,A_: int = 220,):






            '''simple docstring'''




            __UpperCamelCase =			OrderedDict()
            __UpperCamelCase =			OnnxConfig.generate_dummy_inputs(
                self,preprocessor=preprocessor.feature_extractor,batch_size=A_,framework=A_,sampling_rate=A_,time_duration=A_,frequency=A_,)
            __UpperCamelCase =			encoder_inputs['input_features'].shape[2]
            __UpperCamelCase =			encoder_sequence_length // 2 if self.use_past else seq_length

            __UpperCamelCase =			super().generate_dummy_inputs(
                preprocessor.tokenizer,A_,A_,A_,A_					)

            __UpperCamelCase =			encoder_inputs.pop('input_features'					)
            __UpperCamelCase =			decoder_inputs.pop('decoder_input_ids'					)

            if "past_key_values" in decoder_inputs:
                 __UpperCamelCase =			decoder_inputs.pop('past_key_values'					)

            return dummy_inputs







       @property
       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            return 1E-3






"""simple docstring"""


import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
A	:  List[str]            =     pytest.mark.integration


A	:  Optional[Any]            =     {"comet"}
A	:  int            =     importlib.util.find_spec("fairseq") is not None

A	:  Union[str, Any]            =     {"code_eval"}
A	:  Dict            =     os.name == "nt"

A	:  Dict            =     {"bertscore", "frugalscore", "perplexity"}
A	:  Any            =     importlib.util.find_spec("transformers") is not None


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			@wraps(_UpperCamelCase )
			def wrapper(self       ,					_UpperCamelCase ):
						if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
									self.skipTest("\"test requires Fairseq\"" )
						else:
									test_case(self       ,					_UpperCamelCase )

			return wrapper


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			@wraps(_UpperCamelCase )
			def wrapper(self       ,					_UpperCamelCase ):
						if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
									self.skipTest("\"test requires transformers\"" )
						else:
									test_case(self       ,					_UpperCamelCase )

			return wrapper


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			@wraps(_UpperCamelCase )
			def wrapper(self       ,					_UpperCamelCase ):
						if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
									self.skipTest("\"test not supported on Windows\"" )
						else:
									test_case(self       ,					_UpperCamelCase )

			return wrapper


def _lowerCamelCase      (						):

			'''simple docstring'''




			__lowerCAmelCase							       =     [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("./metrics/*/" )]
			return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names()						)
@for_all_test_methods(
    lowerCAmelCase__			,lowerCAmelCase__			,lowerCAmelCase__						)
@local
class     _UpperCamelCase    (  parameterized.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       Any							 ={}
							__UpperCAmelCase		:       List[str]							 =None

							@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"      )
							@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning"      )
							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     "[...]"
										__lowerCAmelCase							       =     importlib.import_module(
										    datasets.load.metric_module_factory(os.path.join("metrics"       ,  __a      )      ).module_path      )
										__lowerCAmelCase							       =     datasets.load.import_main_class(metric_module.__name__       ,  dataset=__a      )
										# check parameters
										__lowerCAmelCase							       =     inspect.signature(metric._compute      ).parameters
										self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()      )      )  # no **kwargs
										# run doctest
										with self.patch_intensive_calls(__a       ,  metric_module.__name__      ):
													with self.use_local_metrics():
																try:
																			__lowerCAmelCase							       =     doctest.testmod(__a       ,  verbose=__a       ,  raise_on_error=__a      )
																except doctest.UnexpectedException as e:
																			raise e.exc_info[1]  # raise the exception that doctest caught
										self.assertEqual(results.failed       ,  0      )
										self.assertGreater(results.attempted       ,  1      )

							@slow
							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     "[...]"
										__lowerCAmelCase							       =     importlib.import_module(
										    datasets.load.metric_module_factory(os.path.join("metrics"       ,  __a      )      ).module_path      )
										# run doctest
										with self.use_local_metrics():
													__lowerCAmelCase							       =     doctest.testmod(__a       ,  verbose=__a       ,  raise_on_error=__a      )
										self.assertEqual(results.failed       ,  0      )
										self.assertGreater(results.attempted       ,  1      )

							@contextmanager
							def      snake_case							(	self       ,  __a       ,  __a      ):
										if metric_name in self.INTENSIVE_CALLS_PATCHER:
													with self.INTENSIVE_CALLS_PATCHER[metric_name](__a      ):
																yield
										else:
													yield

							@contextmanager
							def      snake_case							(	self      ):
										def load_local_metric(__a       ,  *__a       ,  **__a      ):
													return load_metric(os.path.join("metrics"       ,  __a      )       ,  *__a       ,  **__a      )

										with patch("datasets.load_metric"      ) as mock_load_metric:
													__lowerCAmelCase							       =     load_local_metric
													yield



							@classmethod
							def      snake_case							(	cls       ,  __a      ):
										def wrapper(__a      ):
													__lowerCAmelCase							       =     contextmanager(__a      )
													__lowerCAmelCase							       =     patcher
													return patcher

										return wrapper


@LocalMetricTest.register_intensive_calls_patcher("bleurt" )
def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

			tf.flags.DEFINE_string("sv"       ,					""       ,					"" )  # handle pytest cli flags
			class     _UpperCamelCase    (  lowerCAmelCase__						):
										'''simple docstring'''







										def      snake_case							(	self       ,  __a      ):
													assert len(input_dict["input_ids"]      ) == 2
													return np.array([1.0_3, 1.0_4]      )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
			with patch("bleurt.score._create_predictor" ) as mock_create_predictor:
						__lowerCAmelCase							       =     MockedPredictor()
						yield


@LocalMetricTest.register_intensive_calls_patcher("bertscore" )
def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			import torch

			def bert_cos_score_idf(_UpperCamelCase       ,					_UpperCamelCase       ,					*_UpperCamelCase       ,					**_UpperCamelCase ):
						return torch.tensor([[1.0, 1.0, 1.0]] * len(_UpperCamelCase ) )

			# mock get_model which is supposed to do download a bert model
			# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
			with patch("bert_score.scorer.get_model" ), patch(
			    "bert_score.scorer.bert_cos_score_idf" ) as mock_bert_cos_score_idf:
						__lowerCAmelCase							       =     bert_cos_score_idf
						yield


@LocalMetricTest.register_intensive_calls_patcher("comet" )
def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			def load_from_checkpoint(_UpperCamelCase ):
						class     _UpperCamelCase    :
													'''simple docstring'''







													def      snake_case							(	self       ,  __a       ,  *__a       ,  **__a      ):
																assert len(__a      ) == 2
																__lowerCAmelCase							       =     [0.1_9, 0.9_2]
																return scores, sum(__a      ) / len(__a      )
						return Model()

			# mock load_from_checkpoint which is supposed to do download a bert model
			# mock load_from_checkpoint which is supposed to do download a bert model
			with patch("comet.download_model" ) as mock_download_model:
						__lowerCAmelCase							       =     None
						with patch("comet.load_from_checkpoint" ) as mock_load_from_checkpoint:
									__lowerCAmelCase							       =     load_from_checkpoint
									yield


def _lowerCamelCase      (						):

			'''simple docstring'''




			__lowerCAmelCase							       =     load_metric(os.path.join("metrics"       ,					"seqeval" ) )
			__lowerCAmelCase							       =     "ERROR"
			__lowerCAmelCase							       =     f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"
			with pytest.raises(_UpperCamelCase       ,					match=re.escape(_UpperCamelCase ) ):
						metric.compute(predictions=[]       ,					references=[]       ,					scheme=_UpperCamelCase )




