
"""simple docstring"""
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    (     lowerCamelCase__							:							Tuple,						lowerCamelCase__							:							Union[str, Any]=1.0,						lowerCamelCase__							:							Union[str, Any]=None,						lowerCamelCase__							:							int=None				)  ->    Optional[Any]:
  if rng is None:
    _SCREAMING_SNAKE_CASE   :				str							    = global_rng

  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
  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  ):
     def __init__(     self  ,     snake_case__  ,     snake_case__=7  ,     snake_case__=400  ,     snake_case__=2000  ,     snake_case__=1  ,     snake_case__=0.0  ,     snake_case__=16000  ,     snake_case__=True  ,     snake_case__=True  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = parent
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = batch_size
       _SCREAMING_SNAKE_CASE   :				str							    = min_seq_length
       _SCREAMING_SNAKE_CASE   :				Tuple							    = max_seq_length
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       _SCREAMING_SNAKE_CASE   :				int							    = feature_size
       _SCREAMING_SNAKE_CASE   :				Any							    = padding_value
       _SCREAMING_SNAKE_CASE   :				Any							    = sampling_rate
       _SCREAMING_SNAKE_CASE   :				Tuple							    = return_attention_mask
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = do_normalize
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       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 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=False  ,     snake_case__=False				):




       """simple docstring"""


       def _flatten(snake_case__				):
         return list(itertools.chain(*snake_case__				)				)

       if equal_length:
         _SCREAMING_SNAKE_CASE   :				Dict							    = floats_list((self.batch_size, self.max_seq_length)				)
       else:
         # make sure that inputs increase in size
         _SCREAMING_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:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = [np.asarray(snake_case__				) for x in speech_inputs]

       return speech_inputs
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
     A__      =			WavaVecaFeatureExtractor
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = WavaVecaFeatureExtractionTester(self				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       self.assertTrue(np.all(np.mean(snake_case__  ,     axis=0				) < 1E-3				)				)
       self.assertTrue(np.all(np.abs(np.var(snake_case__  ,     axis=0				) - 1				) < 1E-3				)				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_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
       _SCREAMING_SNAKE_CASE   :				int							    = [floats_list((1, x)				)[0] for x in range(800  ,     1400  ,     200				)]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [np.asarray(snake_case__				) for speech_input in speech_inputs]

       # Test not batched input
       _SCREAMING_SNAKE_CASE   :				List[str]							    = feat_extract(speech_inputs[0]  ,     return_tensors="np"				).input_values
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = feat_extract(np_speech_inputs[0]  ,     return_tensors="np"				).input_values
       self.assertTrue(np.allclose(snake_case__  ,     snake_case__  ,     atol=1E-3				)				)

       # Test batched
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = feat_extract(snake_case__  ,     return_tensors="np"				).input_values
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = feat_extract(snake_case__  ,     return_tensors="np"				).input_values
       for enc_seq_a, enc_seq_a in zip(snake_case__  ,     snake_case__				):
         self.assertTrue(np.allclose(snake_case__  ,     snake_case__  ,     atol=1E-3				)				)

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




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [floats_list((1, x)				)[0] for x in range(800  ,     1400  ,     200				)]

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = ["longest", "max_length", "do_not_pad"]
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = [None, 1600, None]
       for max_length, padding in zip(snake_case__  ,     snake_case__				):
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = feat_extract(snake_case__  ,     padding=snake_case__  ,     max_length=snake_case__  ,     return_tensors="np"				)
         _SCREAMING_SNAKE_CASE   :				int							    = 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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = range(800  ,     1400  ,     200				)
       _SCREAMING_SNAKE_CASE   :				int							    = [floats_list((1, x)				)[0] for x in lengths]

       _SCREAMING_SNAKE_CASE   :				Tuple							    = ["longest", "max_length", "do_not_pad"]
       _SCREAMING_SNAKE_CASE   :				int							    = [None, 1600, None]

       for max_length, padding in zip(snake_case__  ,     snake_case__				):
         _SCREAMING_SNAKE_CASE   :				int							    = feat_extract(snake_case__  ,     max_length=snake_case__  ,     padding=snake_case__				)
         _SCREAMING_SNAKE_CASE   :				List[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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = [floats_list((1, x)				)[0] for x in range(800  ,     1400  ,     200				)]
       _SCREAMING_SNAKE_CASE   :				Any							    = feat_extract(
           snake_case__  ,     truncation=snake_case__  ,     max_length=1000  ,     padding="max_length"  ,     return_tensors="np"				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       _SCREAMING_SNAKE_CASE   :				Any							    = [floats_list((1, x)				)[0] for x in range(800  ,     1400  ,     200				)]
       _SCREAMING_SNAKE_CASE   :				str							    = feat_extract(
           snake_case__  ,     truncation=snake_case__  ,     max_length=1000  ,     padding="longest"  ,     return_tensors="np"				)
       _SCREAMING_SNAKE_CASE   :				Union[str, 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]				)

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

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [floats_list((1, x)				)[0] for x in range(800  ,     1400  ,     200				)]
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = feat_extract(
           snake_case__  ,     truncation=snake_case__  ,     max_length=2000  ,     padding="longest"  ,     return_tensors="np"				)
       _SCREAMING_SNAKE_CASE   :				Optional[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 longest
       self.assertTrue(input_values.shape == (3, 1200)				)
     @require_torch
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       import torch

       _SCREAMING_SNAKE_CASE   :				str							    = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       _SCREAMING_SNAKE_CASE   :				Any							    = np.random.rand(100				).astype(np.floataa				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = np_speech_inputs.tolist()

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




     @slow
     @require_torch
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = WavaVecaConfig.from_pretrained(snake_case__				)
         _SCREAMING_SNAKE_CASE   :				List[str]							    = WavaVecaFeatureExtractor.from_pretrained(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"				)





"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be trained."""}  )
     A__      =			field(
         default="""./"""      ,       metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path of training dataset."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for training."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for evaluation."""}  )
     A__      =			field(default=0.1      ,       metadata={"""help""": """Value of weight decay."""}  )
     A__      =			field(
         default=10000      ,       metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}  )
     A__      =			field(default=2e-4      ,       metadata={"""help""": """Learning rate fo training."""}  )
     A__      =			field(default="""cosine"""      ,       metadata={"""help""": """Learning rate."""}  )
     A__      =			field(
         default=750      ,       metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}  )
     A__      =			field(
         default=16      ,       metadata={"""help""": """Number of gradient accumulation steps."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}  )
     A__      =			field(default=50000      ,       metadata={"""help""": """Maximum number of training steps."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Sequence lengths used for training."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Training seed."""}  )
     A__      =			field(
         default=1024      ,       metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True the data is pretokenized."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size used for evaluation."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Length of sequences to be evaluated."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Sample from the language model's output distribution."""}  )
     A__      =			field(default=0.2      ,       metadata={"""help""": """Sampling temperature used for generation."""}  )
     A__      =			field(default=256      ,       metadata={"""help""": """Maximum number of newly generated tokens."""}  )
     A__      =			field(default=0      ,       metadata={"""help""": """Top-k parameter used for generation."""}  )
     A__      =			field(default=0.95      ,       metadata={"""help""": """Top-p parameter used for nucleus sampling."""}  )
     A__      =			field(default=10      ,       metadata={"""help""": """Number of generations to run in parallel."""}  )
     A__      =			field(
         default=200      ,       metadata={"""help""": """Number of completions to generate for each sample."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""eval_results.json"""      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""0"""      ,       metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}  )
     A__      =			field(
         default=-1      ,       metadata={
             """help""": (
                 """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
                 """ number corresponds to which GPU device id to run on."""
             )
         }      ,       )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={
             """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
         }      ,       )
     A__      =			field(
         default="""transformersbook/codeparrot"""      ,       metadata={"""help""": """Folder or name of dataset to process."""}  )
     A__      =			field(
         default="""codeparrot-clean"""      ,       metadata={"""help""": """Folder to save processed processed dataset."""}  )
     A__      =			field(
         default=100000      ,       metadata={"""help""": """Number of files to save per JSON output file."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(
         default=1000      ,       metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=100      ,       metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.25      ,       metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}  )
     A__      =			field(
         default=1.5      ,       metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.7      ,       metadata={"""help""": """Probability for filtering config, test and uncommon files."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True, near-duplicate samples are removed."""}  )
     A__      =			field(
         default=0.85      ,       metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2"""      ,       metadata={"""help""": """Base tokenizer to build new tokenizer from."""}  )
     A__      =			field(
         default="""transformersbook/codeparrot-train"""      ,       metadata={"""help""": """Dataset to train tokenizer on."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(default=200000      ,       metadata={"""help""": """Number of examples to train tokenizer on."""}  )
     A__      =			field(
         default=32768      ,       metadata={"""help""": """Number of examples to train the tokenizer on."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of new tokenizer."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path to the dataset to pretokenize."""}  )
     A__      =			field(
         default="""tokenized-codeparrot-train"""      ,       metadata={"""help""": """Repo name of the pretokenized data."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2-large"""      ,       metadata={"""help""": """Configuration to use for model initialization."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Tokenizer attached to model."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of the created model."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )



