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

__a  :					Any          =  logging.get_logger(__name__)





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



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

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

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




@add_end_docstrings(_UpperCAmelCase			)
class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       *lowerCAmelCase__				,       **lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							5
							super().__init__(*lowerCAmelCase__				,       **lowerCAmelCase__						)

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

							self.check_model_type(lowerCAmelCase__						)





			def __call__(       self				,       lowerCAmelCase__				,       **lowerCAmelCase__				,       )					-> Optional[int]:

							'''simple docstring'''


							return super().__call__(lowerCAmelCase__				,       **lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							{}
							if top_k is not None:
											if top_k > self.model.config.num_labels:
															__lowercase			      =							self.model.config.num_labels
											__lowercase			      =							top_k
							return {}, {}, postprocess_params





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Dict:

							'''simple docstring'''


							if isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):
											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
															__lowercase			      =							requests.get(lowerCAmelCase__						).content
											else:
															with open(lowerCAmelCase__				,       '''rb'''						) as f:
																			__lowercase			      =							f.read()

							if isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):
											__lowercase			      =							ffmpeg_read(lowerCAmelCase__				,       self.feature_extractor.sampling_rate						)

							if isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):
											# 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'''						)

											__lowercase			      =							inputs.pop('''raw'''				,       lowerCAmelCase__						)
											if _inputs is None:
															# Remove path which will not be used from `datasets`.
															inputs.pop('''path'''				,       lowerCAmelCase__						)
															__lowercase			      =							inputs.pop('''array'''				,       lowerCAmelCase__						)
											__lowercase			      =							inputs.pop('''sampling_rate'''						)
											__lowercase			      =							_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`.'''						)

															__lowercase			      =							F.resample(
															    torch.from_numpy(lowerCAmelCase__						)				,       lowerCAmelCase__				,       self.feature_extractor.sampling_rate						).numpy()

							if not isinstance(lowerCAmelCase__				,       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'''						)

							__lowercase			      =							self.feature_extractor(
							    lowerCAmelCase__				,       sampling_rate=self.feature_extractor.sampling_rate				,       return_tensors='''pt'''						)
							return processed





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							self.model(**lowerCAmelCase__						)
							return model_outputs





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=5						)					-> str:

							'''simple docstring'''


							__lowercase			      =							model_outputs.logits[0].softmax(-1						)
							__lowercase      ,					__lowercase			      =							probs.topk(lowerCAmelCase__						)

							__lowercase			      =							scores.tolist()
							__lowercase			      =							ids.tolist()

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

							return labels






import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


__a  :					List[Any]          =  {0: """null""", 1: """short""", 2: """long""", 3: """yes""", 4: """no"""}
__a  :					List[Any]          =  set("""""".join(["""‘""", """’""", """´""", """`""", """.""", """,""", """-""", """\""""]))





def     UpperCAmelCase						(   lowercase							,      lowercase=0							,      lowercase=None							):







				"""simple docstring"""



				return [" ".join(x.split(''' '''							)[begin:end]							) for x in answers if len(x.split(''' '''							)							) > 1]





def     UpperCAmelCase						(   lowercase							,      lowercase=False							):







				"""simple docstring"""



				if make_sub_answers:
								# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
								# *e.g.* if the correct answer contains a prefix such as "the", or "a"
								__lowercase			      =							(
								    given_answers + get_sub_answers(lowercase							,      begin=1							) + get_sub_answers(lowercase							,      end=-1							)
								)
				__lowercase			      =							[]
				for answer in given_answers:
								__lowercase			      =							answer.replace('''_'''							,      ''' '''							).lower()
								__lowercase			      =							''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias							)
								answers.append(''' '''.join(alias.split()							).strip()							)
				return set(lowercase							)





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase=1							,      lowercase=100							):







				"""simple docstring"""



				__lowercase      ,					__lowercase			      =							jax.lax.top_k(lowercase							,      lowercase							)
				__lowercase      ,					__lowercase			      =							jax.lax.top_k(lowercase							,      lowercase							)

				__lowercase			      =							best_end_idx[:, None] - best_start_idx[None, :]
				__lowercase			      =							jnp.logical_or(widths < 0							,      widths > max_size							)
				__lowercase			      =							(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
				__lowercase			      =							jnp.argmax(lowercase							).item()

				return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							sample['''question''']['''text''']
				__lowercase			      =							sample['''document''']['''tokens''']['''token''']
				__lowercase			      =							sample['''document''']['''tokens''']['''is_html''']
				__lowercase			      =							sample['''annotations''']['''long_answer''']
				__lowercase			      =							sample['''annotations''']['''short_answers''']

				__lowercase			      =							''' '''.join([context[i] for i in range(len(lowercase							)							) if not is_html[i]]							)

				# 0 - No ; 1 - Yes
				for answer in sample["annotations"]["yes_no_answer"]:
								if answer == 0 or answer == 1:
												return {
												    "question": question,
												    "context": context_string,
												    "short": [],
												    "long": [],
												    "category": "no" if answer == 0 else "yes",
												}

				__lowercase			      =							[]
				for s in short_answers:
								short_targets.extend(s['''text''']							)
				__lowercase			      =							list(set(lowercase							)							)

				__lowercase			      =							[]
				for s in long_answers:
								if s["start_token"] == -1:
												continue
								__lowercase			      =							context[s['''start_token'''] : s['''end_token''']]
								__lowercase			      =							is_html[s['''start_token'''] : s['''end_token''']]
								__lowercase			      =							''' '''.join([answer[i] for i in range(len(lowercase							)							) if not html[i]]							)
								if new_answer not in long_targets:
												long_targets.append(lowercase							)

				__lowercase			      =							'''long_short''' if len(short_targets + long_targets							) > 0 else '''null'''

				return {
				    "question": question,
				    "context": context_string,
				    "short": short_targets,
				    "long": long_targets,
				    "category": category,
				}





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							load_from_disk('''natural-questions-validation'''							)
				__lowercase			      =							dataset.map(lowercase							).remove_columns(['''annotations''', '''document''', '''id''']							)
				print(lowercase							)

				__lowercase			      =							dataset.filter(lambda lowercase							: (len(x['''question''']							) + len(x['''context''']							)) < 4 * 4096							)
				__lowercase			      =							short_validation_dataset.filter(lambda lowercase							: x["category"] != "null"							)
				short_validation_dataset

				__lowercase			      =							'''vasudevgupta/flax-bigbird-natural-questions'''
				__lowercase			      =							FlaxBigBirdForNaturalQuestions.from_pretrained(lowercase							)
				__lowercase			      =							BigBirdTokenizerFast.from_pretrained(lowercase							)

				@jax.jit
				def forward(*lowercase							,      **lowercase							):
								__lowercase      ,					__lowercase      ,					__lowercase			      =							model(*lowercase							,      **lowercase							)
								return start_logits, end_logits, jnp.argmax(lowercase							,      axis=-1							)

				def evaluate(lowercase							):
								# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
								__lowercase			      =							tokenizer(
								    example['''question''']							,      example['''context''']							,      return_tensors='''np'''							,      max_length=4096							,      padding='''max_length'''							,      truncation=lowercase							,      )

								__lowercase      ,					__lowercase      ,					__lowercase			      =							forward(**lowercase							)

								__lowercase			      =							CATEGORY_MAPPING[category.item()]

								__lowercase			      =							example['''long'''] + example['''short''']
								if example["category"] in ["yes", "no", "null"]:
												__lowercase			      =							[example['''category''']]
								__lowercase			      =							example['''category'''] != '''null'''
								# Now target can be: "yes", "no", "null", "list of long & short answers"

								if predicted_category in ["yes", "no", "null"]:
												__lowercase			      =							[predicted_category]
												__lowercase			      =							example['''output'''] == example['''targets''']
												__lowercase			      =							predicted_category != '''null'''
												return example

								__lowercase			      =							38 if predicted_category == '''short''' else 1024
								__lowercase      ,					__lowercase			      =							get_best_valid_start_end_idx(
								    start_scores[0]							,      end_scores[0]							,      top_k=8							,      max_size=lowercase							)

								__lowercase			      =							inputs['''input_ids'''][0].tolist()
								__lowercase			      =							[tokenizer.decode(input_ids[start_score : end_score + 1]							)]

								__lowercase			      =							expand_to_aliases(example['''targets''']							,      make_sub_answers=lowercase							)
								__lowercase			      =							expand_to_aliases(example['''output''']							)

								# some preprocessing to both prediction and answer
								__lowercase			      =							{''''''.join(a.split()							) for a in answers}
								__lowercase			      =							{''''''.join(p.split()							) for p in predictions}
								__lowercase			      =							{s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

								# if there is a common element, it's a exact match
								__lowercase			      =							len(list(answers & predictions							)							) > 0
								__lowercase			      =							predicted_category != '''null''' and len(lowercase							) > 0

								return example

				__lowercase			      =							short_validation_dataset.map(lowercase							)

				__lowercase			      =							len(lowercase							)
				__lowercase			      =							len(short_validation_dataset.filter(lambda lowercase							: x["match"] == 1							)							)
				print('''EM score:'''							,      (matched / total) * 100							,      '''%'''							)


if __name__ == "__main__":
		main()