

import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 						lowercase     (					_a     ,				unittest.TestCase				):


		"""simple docstring"""




		a__       							=	BartTokenizer
		a__       							=	BartTokenizerFast
		a__       							=	True
		a__       							=	filter_roberta_detectors


		def 		A__       (					self):
			super().setUp()
			_UpperCamelCase :		Any								=    [
			    'l',
			    'o',
			    'w',
			    'e',
			    'r',
			    's',
			    't',
			    'i',
			    'd',
			    'n',
			    '\u0120',
			    '\u0120l',
			    '\u0120n',
			    '\u0120lo',
			    '\u0120low',
			    'er',
			    '\u0120lowest',
			    '\u0120newer',
			    '\u0120wider',
			    '<unk>',
			]
			_UpperCamelCase :		int								=    dict(zip(_A						,							range(len(_A))))
			_UpperCamelCase :		Union[str, Any]								=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
			_UpperCamelCase :		Any								=    {'unk_token': '<unk>'}

			_UpperCamelCase :		Union[str, Any]								=    os.path.join(self.tmpdirname						,							VOCAB_FILES_NAMES['vocab_file'])
			_UpperCamelCase :		Union[str, Any]								=    os.path.join(self.tmpdirname						,							VOCAB_FILES_NAMES['merges_file'])
			with open(self.vocab_file						,							'w'						,							encoding='utf-8') as fp:
				fp.write(json.dumps(_A) + '\n')
			with open(self.merges_file						,							'w'						,							encoding='utf-8') as fp:
				fp.write('\n'.join(_A))


		def 		A__       (					self						,							**__snake_case):
			kwargs.update(self.special_tokens_map)
			return self.tokenizer_class.from_pretrained(self.tmpdirname						,							**_A)


		def 		A__       (					self						,							**__snake_case):
			kwargs.update(self.special_tokens_map)
			return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,							**_A)


		def 		A__       (					self						,							__snake_case):
			return "lower newer", "lower newer"


		@cached_property
		def 		A__       (					self):
			return BartTokenizer.from_pretrained('facebook/bart-large')


		@cached_property
		def 		A__       (					self):
			return BartTokenizerFast.from_pretrained('facebook/bart-large')


		@require_torch
		def 		A__       (					self):
			_UpperCamelCase :		List[str]								=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
			_UpperCamelCase :		Any								=    [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				_UpperCamelCase :		Union[str, Any]								=    tokenizer(_A						,							max_length=len(_A)						,							padding=_A						,							return_tensors='pt')
				self.assertIsInstance(_A						,							_A)

				self.assertEqual((2, 9)						,							batch.input_ids.shape)
				self.assertEqual((2, 9)						,							batch.attention_mask.shape)
				_UpperCamelCase :		List[str]								=    batch.input_ids.tolist()[0]
				self.assertListEqual(_A						,							_A)
				# Test that special tokens are reset


		@require_torch
		def 		A__       (					self):
			_UpperCamelCase :		List[str]								=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				_UpperCamelCase :		str								=    tokenizer(_A						,							padding=_A						,							return_tensors='pt')
				# check if input_ids are returned and no labels
				self.assertIn('input_ids'						,							_A)
				self.assertIn('attention_mask'						,							_A)
				self.assertNotIn('labels'						,							_A)
				self.assertNotIn('decoder_attention_mask'						,							_A)


		@require_torch
		def 		A__       (					self):
			_UpperCamelCase :		Any								=    [
			    'Summary of the text.',
			    'Another summary.',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				_UpperCamelCase :		Dict								=    tokenizer(text_target=_A						,							max_length=32						,							padding='max_length'						,							return_tensors='pt')
				self.assertEqual(32						,							targets['input_ids'].shape[1])


		@require_torch
		def 		A__       (					self):
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				_UpperCamelCase :		int								=    tokenizer(
				    ['I am a small frog' * 10_24, 'I am a small frog']						,							padding=_A						,							truncation=_A						,							return_tensors='pt')
				self.assertIsInstance(_A						,							_A)
				self.assertEqual(batch.input_ids.shape						,							(2, 10_24))


		@require_torch
		def 		A__       (					self):
			_UpperCamelCase :		Any								=    ['A long paragraph for summarization.']
			_UpperCamelCase :		Any								=    [
			    'Summary of the text.',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
				_UpperCamelCase :		int								=    tokenizer(_A						,							return_tensors='pt')
				_UpperCamelCase :		Any								=    tokenizer(text_target=_A						,							return_tensors='pt')
				_UpperCamelCase :		str								=    inputs['input_ids']
				_UpperCamelCase :		Tuple								=    targets['input_ids']
				self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
				self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
				self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
				self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())


		def 		A__       (					self):
			pass






		def 		A__       (					self):
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
				with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
					_UpperCamelCase :		List[Any]								=    self.rust_tokenizer_class.from_pretrained(_A						,							**_A)
					_UpperCamelCase :		List[Any]								=    self.tokenizer_class.from_pretrained(_A						,							**_A)
					_UpperCamelCase :		Optional[int]								=    'A, <mask> AllenNLP sentence.'
					_UpperCamelCase :		List[str]								=    tokenizer_r.encode_plus(_A						,							add_special_tokens=_A						,							return_token_type_ids=_A)
					_UpperCamelCase :		Any								=    tokenizer_p.encode_plus(_A						,							add_special_tokens=_A						,							return_token_type_ids=_A)

					# token_type_ids should put 0 everywhere
					self.assertEqual(sum(tokens_r['token_type_ids'])						,							sum(tokens_p['token_type_ids']))

					# attention_mask should put 1 everywhere, so sum over length should be 1
					self.assertEqual(
					    sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])						,							sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])						,							)

					_UpperCamelCase :		Optional[Any]								=    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
					_UpperCamelCase :		int								=    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

					# Rust correctly handles the space before the mask while python doesnt
					self.assertSequenceEqual(tokens_p['input_ids']						,							[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
					self.assertSequenceEqual(tokens_r['input_ids']						,							[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

					self.assertSequenceEqual(
					    _A						,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
					self.assertSequenceEqual(
					    _A						,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])







import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


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

lowerCamelCase   					=  logging.get_logger(__name__)


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					__lowerCAmelCase       =				F"""{sampling_rate}"""
					__lowerCAmelCase       =				'1'
					__lowerCAmelCase       =				'f32le'
					__lowerCAmelCase       =				[
					    'ffmpeg',
					    '-i',
					    'pipe:0',
					    '-ac',
					    ac,
					    '-ar',
					    ar,
					    '-f',
					    format_for_conversion,
					    '-hide_banner',
					    '-loglevel',
					    'quiet',
					    'pipe:1',
					]

					try:
										__lowerCAmelCase       =				subprocess.Popen(UpperCAmelCase__		,       stdin=subprocess.PIPE		,       stdout=subprocess.PIPE   )
					except FileNotFoundError:
										raise ValueError('ffmpeg was not found but is required to load audio files from filename'   )
					__lowerCAmelCase       =				ffmpeg_process.communicate(UpperCAmelCase__   )
					__lowerCAmelCase       =				output_stream[0]

					__lowerCAmelCase       =				np.frombuffer(UpperCAmelCase__		,       np.floataa   )
					if audio.shape[0] == 0:
										raise ValueError('Malformed soundfile'   )
					return audio





@add_end_docstrings(_a				)
class      snake_case_      (					_a				):


							"""simple docstring"""





							def __init__(			self       ,    *_A       ,    **_A				):
												# Default, might be overriden by the model.config.
												__lowerCAmelCase       =				5
												super().__init__(*_A       ,    **_A				)

												if self.framework != "pt":
																	raise ValueError(F"""The {self.__class__} is only available in PyTorch."""				)

												self.check_model_type(_A				)




							def __call__(			self       ,    _A       ,    **_A       ,    ):
												return super().__call__(_A       ,    **_A				)




							def 							A__	(			self       ,    _A=None       ,    **_A				):
												# No parameters on this pipeline right now
												__lowerCAmelCase       =				{}
												if top_k is not None:
																	if top_k > self.model.config.num_labels:
																						__lowerCAmelCase       =				self.model.config.num_labels
																	__lowerCAmelCase       =				top_k
												return {}, {}, postprocess_params




							def 							A__	(			self       ,    _A				):
												if isinstance(_A       ,    _A				):
																	if inputs.startswith('http://'				) or inputs.startswith('https://'				):
																						# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																						# like http_huggingface_co.png
																						__lowerCAmelCase       =				requests.get(_A				).content
																	else:
																						with open(_A       ,    'rb'				) as f:
																											__lowerCAmelCase       =				f.read()

												if isinstance(_A       ,    _A				):
																	__lowerCAmelCase       =				ffmpeg_read(_A       ,    self.feature_extractor.sampling_rate				)

												if isinstance(_A       ,    _A				):
																	# Accepting `"array"` which is the key defined in `datasets` for
																	# better integration
																	if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
																						raise ValueError(
																						    'When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '
																						    '"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '
																						    'containing the sampling_rate associated with that array'				)

																	__lowerCAmelCase       =				inputs.pop('raw'       ,    _A				)
																	if _inputs is None:
																						# Remove path which will not be used from `datasets`.
																						inputs.pop('path'       ,    _A				)
																						__lowerCAmelCase       =				inputs.pop('array'       ,    _A				)
																	__lowerCAmelCase       =				inputs.pop('sampling_rate'				)
																	__lowerCAmelCase       =				_inputs
																	if in_sampling_rate != self.feature_extractor.sampling_rate:
																						import torch

																						if is_torchaudio_available():
																											from torchaudio import functional as F
																						else:
																											raise ImportError(
																											    'torchaudio is required to resample audio samples in AudioClassificationPipeline. '
																											    'The torchaudio package can be installed through: `pip install torchaudio`.'				)

																						__lowerCAmelCase       =				F.resample(
																						    torch.from_numpy(_A				)       ,    _A       ,    self.feature_extractor.sampling_rate				).numpy()

												if not isinstance(_A       ,    np.ndarray				):
																	raise ValueError('We expect a numpy ndarray as input'				)
												if len(inputs.shape				) != 1:
																	raise ValueError('We expect a single channel audio input for AudioClassificationPipeline'				)

												__lowerCAmelCase       =				self.feature_extractor(
												    _A       ,    sampling_rate=self.feature_extractor.sampling_rate       ,    return_tensors='pt'				)
												return processed




							def 							A__	(			self       ,    _A				):
												__lowerCAmelCase       =				self.model(**_A				)
												return model_outputs




							def 							A__	(			self       ,    _A       ,    _A=5				):
												__lowerCAmelCase       =				model_outputs.logits[0].softmax(-1				)
												__lowerCAmelCase,						__lowerCAmelCase       =				probs.topk(_A				)

												__lowerCAmelCase       =				scores.tolist()
												__lowerCAmelCase       =				ids.tolist()

												__lowerCAmelCase       =				[{'score': score, 'label': self.model.config.idalabel[_id]} for score, _id in zip(_A       ,    _A				)]

												return labels






