






import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase  :							Dict			  =    random.Random()

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			Optional[int]=1.0  ,      _a       :			List[str]=None  ,      _a       :			str=None    ):
  if rng is None:
    snake_case_				:							List[Any]						 =       global_rng

  snake_case_				:							Union[str, Any]						 =       []
  for batch_idx in range(shape[0]    ):
    values.append([]    )
    for _ in range(shape[1]    ):
      values[-1].append(rng.random() * scale    )

  return values




class 		UpperCAmelCase_						(	unittest.TestCase					):


      '''simple docstring'''




      def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=7		,				_SCREAMING_SNAKE_CASE=400		,				_SCREAMING_SNAKE_CASE=2000		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=1_6000		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				) ->				List[Any]:
        snake_case_				:							Dict						 =       parent
        snake_case_				:							Optional[int]						 =       batch_size
        snake_case_				:							Dict						 =       min_seq_length
        snake_case_				:							Tuple						 =       max_seq_length
        snake_case_				:							Optional[int]						 =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        snake_case_				:							Dict						 =       feature_size
        snake_case_				:							List[Any]						 =       padding_value
        snake_case_				:							str						 =       sampling_rate
        snake_case_				:							Optional[int]						 =       return_attention_mask
        snake_case_				:							Optional[int]						 =       do_normalize






      def       _lowerCAmelCase       (	self				) ->				List[str]:
        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=False				) ->				str:
        def _flatten(_SCREAMING_SNAKE_CASE				):
          return list(itertools.chain(*_SCREAMING_SNAKE_CASE				)				)

        if equal_length:
          snake_case_				:							Tuple						 =       floats_list((self.batch_size, self.max_seq_length)				)
        else:
          # make sure that inputs increase in size
          snake_case_				:							str						 =       [
              _flatten(floats_list((x, self.feature_size)				)				)
              for x in range(self.min_seq_length		,				self.max_seq_length		,				self.seq_length_diff				)
          ]

        if numpify:
          snake_case_				:							int						 =       [np.asarray(_SCREAMING_SNAKE_CASE				) for x in speech_inputs]

        return speech_inputs




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


      '''simple docstring'''




      A     :   List[str]						=					WavaVecaFeatureExtractor






      def       _lowerCAmelCase       (	self				) ->				List[str]:
        snake_case_				:							Tuple						 =       WavaVecaFeatureExtractionTester(self				)






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
        self.assertTrue(np.all(np.mean(_SCREAMING_SNAKE_CASE		,				axis=0				) < 1e-3				)				)
        self.assertTrue(np.all(np.abs(np.var(_SCREAMING_SNAKE_CASE		,				axis=0				) - 1				) < 1e-3				)				)






      def       _lowerCAmelCase       (	self				) ->				Optional[int]:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        snake_case_				:							List[Any]						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
        # create three inputs of length 800, 1000, and 1200
        snake_case_				:							List[str]						 =       [floats_list((1, x)				)[0] for x in range(800		,				1400		,				200				)]
        snake_case_				:							Dict						 =       [np.asarray(_SCREAMING_SNAKE_CASE				) for speech_input in speech_inputs]

        # Test not batched input
        snake_case_				:							str						 =       feat_extract(speech_inputs[0]		,				return_tensors="np"				).input_values
        snake_case_				:							Dict						 =       feat_extract(np_speech_inputs[0]		,				return_tensors="np"				).input_values
        self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				atol=1e-3				)				)

        # Test batched
        snake_case_				:							str						 =       feat_extract(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				).input_values
        snake_case_				:							str						 =       feat_extract(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				).input_values
        for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
          self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				atol=1e-3				)				)

        # Test 2-D numpy arrays are batched.
        snake_case_				:							Optional[int]						 =       [floats_list((1, x)				)[0] for x in (800, 800, 800)]
        snake_case_				:							str						 =       np.asarray(_SCREAMING_SNAKE_CASE				)
        snake_case_				:							Tuple						 =       feat_extract(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				).input_values
        snake_case_				:							Any						 =       feat_extract(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				).input_values
        for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
          self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				atol=1e-3				)				)






      def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
        snake_case_				:							List[Any]						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
        snake_case_				:							Tuple						 =       [floats_list((1, x)				)[0] for x in range(800		,				1400		,				200				)]

        snake_case_				:							List[Any]						 =       ["longest", "max_length", "do_not_pad"]
        snake_case_				:							int						 =       [None, 1600, None]
        for max_length, padding in zip(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
          snake_case_				:							Any						 =       feat_extract(_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				return_tensors="np"				)
          snake_case_				:							Optional[Any]						 =       processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:800]				)
          self.assertTrue(input_values[0][800:].sum() < 1e-6				)
          self._check_zero_mean_unit_variance(input_values[1][:1000]				)
          self.assertTrue(input_values[0][1000:].sum() < 1e-6				)
          self._check_zero_mean_unit_variance(input_values[2][:1200]				)






      def       _lowerCAmelCase       (	self				) ->				Optional[int]:
        snake_case_				:							Dict						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
        snake_case_				:							Any						 =       range(800		,				1400		,				200				)
        snake_case_				:							Tuple						 =       [floats_list((1, x)				)[0] for x in lengths]

        snake_case_				:							Optional[Any]						 =       ["longest", "max_length", "do_not_pad"]
        snake_case_				:							Optional[Any]						 =       [None, 1600, None]

        for max_length, padding in zip(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
          snake_case_				:							Optional[int]						 =       feat_extract(_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE				)
          snake_case_				:							Any						 =       processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:800]				)
          self._check_zero_mean_unit_variance(input_values[1][:1000]				)
          self._check_zero_mean_unit_variance(input_values[2][:1200]				)






      def       _lowerCAmelCase       (	self				) ->				Tuple:
        snake_case_				:							str						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
        snake_case_				:							Union[str, Any]						 =       [floats_list((1, x)				)[0] for x in range(800		,				1400		,				200				)]
        snake_case_				:							str						 =       feat_extract(
            _SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				max_length=1000		,				padding="max_length"		,				return_tensors="np"				)
        snake_case_				:							int						 =       processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800]				)
        self._check_zero_mean_unit_variance(input_values[1]				)
        self._check_zero_mean_unit_variance(input_values[2]				)






      def       _lowerCAmelCase       (	self				) ->				str:
        snake_case_				:							int						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
        snake_case_				:							Any						 =       [floats_list((1, x)				)[0] for x in range(800		,				1400		,				200				)]
        snake_case_				:							Any						 =       feat_extract(
            _SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				max_length=1000		,				padding="longest"		,				return_tensors="np"				)
        snake_case_				:							int						 =       processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800]				)
        self._check_zero_mean_unit_variance(input_values[1, :1000]				)
        self._check_zero_mean_unit_variance(input_values[2]				)

        # make sure that if max_length < longest -> then pad to max_length
        self.assertTrue(input_values.shape == (3, 1000)				)

        snake_case_				:							Dict						 =       [floats_list((1, x)				)[0] for x in range(800		,				1400		,				200				)]
        snake_case_				:							Optional[int]						 =       feat_extract(
            _SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				max_length=2000		,				padding="longest"		,				return_tensors="np"				)
        snake_case_				:							Tuple						 =       processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800]				)
        self._check_zero_mean_unit_variance(input_values[1, :1000]				)
        self._check_zero_mean_unit_variance(input_values[2]				)

        # make sure that if max_length > longest -> then pad to longest
        self.assertTrue(input_values.shape == (3, 1200)				)






      @require_torch
      def       _lowerCAmelCase       (	self				) ->				Any:
        import torch

        snake_case_				:							int						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
        snake_case_				:							List[Any]						 =       np.random.rand(100				).astype(np.floataa				)
        snake_case_				:							Any						 =       np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
          snake_case_				:							List[Any]						 =       feature_extractor.pad([{"input_values": inputs}]		,				return_tensors="np"				)
          self.assertTrue(np_processed.input_values.dtype == np.floataa				)
          snake_case_				:							str						 =       feature_extractor.pad([{"input_values": inputs}]		,				return_tensors="pt"				)
          self.assertTrue(pt_processed.input_values.dtype == torch.floataa				)






      @slow
      @require_torch
      def       _lowerCAmelCase       (	self				) ->				Optional[int]:
        # this test makes sure that models that are using
        # group norm don't have their feature extractor return the
        # attention_mask
        for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
          snake_case_				:							Dict						 =       WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
          snake_case_				:							Union[str, Any]						 =       WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE				)

          # only "layer" feature extraction norm should make use of
          # attention_mask
          self.assertEqual(feat_extract.return_attention_mask		,				config.feat_extract_norm == "layer"				)








from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


lowercase  :							Dict			  =    logging.get_logger(__name__)




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							List[str]						 =       [label.strip() for label in labels.split(","				) if label.strip()]
								return labels






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								if len(_SCREAMING_SNAKE_CASE				) == 0 or len(_SCREAMING_SNAKE_CASE				) == 0:
										raise ValueError("You must include at least one label and at least one sequence."				)
								if hypothesis_template.format(labels[0]				) == hypothesis_template:
										raise ValueError(
										    (
										        "The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. "
										        "Make sure the passed template includes formatting syntax such as {{}} where the label should go."
										    ).format(_SCREAMING_SNAKE_CASE				)				)

								if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							List[str]						 =       [sequences]

								snake_case_				:							List[str]						 =       []
								for sequence in sequences:
										sequence_pairs.extend([[sequence, hypothesis_template.format(_SCREAMING_SNAKE_CASE				)] for label in labels]				)

								return sequence_pairs, sequences




@add_end_docstrings(SCREAMING_SNAKE_CASE__					)
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE=ZeroShotClassificationArgumentHandler()		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								snake_case_				:							Dict						 =       args_parser
								super().__init__(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)
								if self.entailment_id == -1:
										logger.warning(
										    "Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to "
										    "-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."				)






						@property
						def       _lowerCAmelCase       (	self				) ->				str:
								for label, ind in self.model.config.labelaid.items():
										if label.lower().startswith("entail"				):
												return ind
								return -1






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=TruncationStrategy.ONLY_FIRST		,				**_SCREAMING_SNAKE_CASE				) ->				Dict:
								snake_case_				:							List[Any]						 =       self.framework
								if self.tokenizer.pad_token is None:
										# Override for tokenizers not supporting padding
										logger.error(
										    "Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
										    " `pad_token=eos_token`"				)
										snake_case_				:							List[str]						 =       self.tokenizer.eos_token
								try:
										snake_case_				:							Union[str, Any]						 =       self.tokenizer(
										    _SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				)
								except Exception as e:
										if "too short" in str(_SCREAMING_SNAKE_CASE				):
												# tokenizers might yell that we want to truncate
												# to a value that is not even reached by the input.
												# In that case we don't want to truncate.
												# It seems there's not a really better way to catch that
												# exception.

												snake_case_				:							Union[str, Any]						 =       self.tokenizer(
												    _SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				truncation=TruncationStrategy.DO_NOT_TRUNCATE		,				)
										else:
												raise e

								return inputs






						def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
								if kwargs.get("multi_class"		,				_SCREAMING_SNAKE_CASE				) is not None:
										snake_case_				:							Any						 =       kwargs["multi_class"]
										logger.warning(
										    "The `multi_class` argument has been deprecated and renamed to `multi_label`. "
										    "`multi_class` will be removed in a future version of Transformers."				)
								snake_case_				:							Any						 =       {}
								if "candidate_labels" in kwargs:
										snake_case_				:							Tuple						 =       self._args_parser._parse_labels(kwargs["candidate_labels"]				)
								if "hypothesis_template" in kwargs:
										snake_case_				:							Optional[Any]						 =       kwargs["hypothesis_template"]

								snake_case_				:							Dict						 =       {}
								if "multi_label" in kwargs:
										snake_case_				:							List[Any]						 =       kwargs["multi_label"]
								return preprocess_params, {}, postprocess_params






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				) ->				str:
								if len(_SCREAMING_SNAKE_CASE				) == 0:
										pass
								elif len(_SCREAMING_SNAKE_CASE				) == 1 and "candidate_labels" not in kwargs:
										snake_case_				:							int						 =       args[0]
								else:
										raise ValueError(f'''Unable to understand extra arguments {args}'''				)

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






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE="This example is {}."				) ->				str:
								snake_case_ ,       snake_case_				:							Optional[int]						 =       self._args_parser(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								for i, (candidate_label, sequence_pair) in enumerate(zip(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)				):
										snake_case_				:							str						 =       self._parse_and_tokenize([sequence_pair]				)

										yield {
										    "candidate_label": candidate_label,
										    "sequence": sequences[0],
										    "is_last": i == len(_SCREAMING_SNAKE_CASE				) - 1,
										    **model_input,
										}






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								snake_case_				:							Optional[Any]						 =       inputs["candidate_label"]
								snake_case_				:							Dict						 =       inputs["sequence"]
								snake_case_				:							Optional[int]						 =       {k: inputs[k] for k in self.tokenizer.model_input_names}
								snake_case_				:							Dict						 =       self.model(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       {
								    "candidate_label": candidate_label,
								    "sequence": sequence,
								    "is_last": inputs["is_last"],
								    **outputs,
								}
								return model_outputs






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=False				) ->				Any:
								snake_case_				:							str						 =       [outputs["candidate_label"] for outputs in model_outputs]
								snake_case_				:							Union[str, Any]						 =       [outputs["sequence"] for outputs in model_outputs]
								snake_case_				:							List[Any]						 =       np.concatenate([output["logits"].numpy() for output in model_outputs]				)
								snake_case_				:							Tuple						 =       logits.shape[0]
								snake_case_				:							List[Any]						 =       len(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							int						 =       N // n
								snake_case_				:							Optional[Any]						 =       logits.reshape((num_sequences, n, -1)				)

								if multi_label or len(_SCREAMING_SNAKE_CASE				) == 1:
										# softmax over the entailment vs. contradiction dim for each label independently
										snake_case_				:							Any						 =       self.entailment_id
										snake_case_				:							List[str]						 =       -1 if entailment_id == 0 else 0
										snake_case_				:							Dict						 =       reshaped_outputs[..., [contradiction_id, entailment_id]]
										snake_case_				:							List[str]						 =       np.exp(_SCREAMING_SNAKE_CASE				) / np.exp(_SCREAMING_SNAKE_CASE				).sum(-1		,				keepdims=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, Any]						 =       scores[..., 1]
								else:
										# softmax the "entailment" logits over all candidate labels
										snake_case_				:							List[str]						 =       reshaped_outputs[..., self.entailment_id]
										snake_case_				:							Optional[Any]						 =       np.exp(_SCREAMING_SNAKE_CASE				) / np.exp(_SCREAMING_SNAKE_CASE				).sum(-1		,				keepdims=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							int						 =       list(reversed(scores[0].argsort()				)				)
								return {
								    "sequence": sequences[0],
								    "labels": [candidate_labels[i] for i in top_inds],
								    "scores": scores[0, top_inds].tolist(),
								}
