

"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


__A	:       str        =						random.Random()
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__=1.0     ,				lowercase__=None     ,				lowercase__=None ):
					"""simple docstring"""
					if rng is None:
										A     =   global_rng

					A     =   []
					for batch_idx in range(shape[0] ):
										values.append([] )
										for _ in range(shape[1] ):
															values[-1].append(rng.random() * scale )

					return values





@require_torch
class      __UpperCamelCase					(  unittest.TestCase		):


		def __init__(self					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=7	,	__SCREAMING_SNAKE_CASE					:						List[Any]=4_0_0	,	__SCREAMING_SNAKE_CASE					:						str=2_0_0_0	,	__SCREAMING_SNAKE_CASE					:						List[str]=1	,	__SCREAMING_SNAKE_CASE					:						Tuple=0.0	,	__SCREAMING_SNAKE_CASE					:						Any=2_4_0_0_0	,	__SCREAMING_SNAKE_CASE					:						Optional[int]=True	,	):
							A     =   parent
							A     =   batch_size
							A     =   min_seq_length
							A     =   max_seq_length
							A     =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							A     =   feature_size
							A     =   padding_value
							A     =   sampling_rate
							A     =   return_attention_mask


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							}


		def    SCREAMING_SNAKE_CASE__  (self					:						Any	,	__SCREAMING_SNAKE_CASE					:						List[Any]=False	,	__SCREAMING_SNAKE_CASE					:						Any=False):
							def _flatten(__SCREAMING_SNAKE_CASE					:						List[Any]):
												return list(itertools.chain(*__SCREAMING_SNAKE_CASE))

							if equal_length:
												A     =   floats_list((self.batch_size, self.max_seq_length))
							else:
												# make sure that inputs increase in size
												A     =   [
												    _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:
												A     =   [np.asarray(__SCREAMING_SNAKE_CASE) for x in audio_inputs]

							return audio_inputs





@require_torch
class      __UpperCamelCase					(  _A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       EncodecFeatureExtractor


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   EnCodecFeatureExtractionTester(self)


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							# Tests that all call wrap to encode_plus and batch_encode_plus
							A     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
							# create three inputs of length 800, 1000, and 1200
							A     =   [floats_list((1, x))[0] for x in range(8_0_0	,	1_4_0_0	,	2_0_0)]
							A     =   [np.asarray(__SCREAMING_SNAKE_CASE) for audio_input in audio_inputs]

							# Test not batched input
							A     =   feat_extract(audio_inputs[0]	,	return_tensors="np").input_values
							A     =   feat_extract(np_audio_inputs[0]	,	return_tensors="np").input_values
							self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	atol=1E-3))

							# Test batched
							A     =   feat_extract(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="np").input_values
							A     =   feat_extract(__SCREAMING_SNAKE_CASE	,	padding=__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    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
							A     =   np.random.rand(1_0_0).astype(np.floataa)
							A     =   np_audio_inputs.tolist()

							for inputs in [py_audio_inputs, np_audio_inputs]:
												A     =   feature_extractor.pad([{"input_values": inputs}]	,	return_tensors="np")
												self.assertTrue(np_processed.input_values.dtype == np.floataa)
												A     =   feature_extractor.pad([{"input_values": inputs}]	,	return_tensors="pt")
												self.assertTrue(pt_processed.input_values.dtype == torch.floataa)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Optional[int]):
							from datasets import load_dataset

							A     =   load_dataset("hf-internal-testing/librispeech_asr_dummy"	,	"clean"	,	split="validation")
							# automatic decoding with librispeech
							A     =   ds.sort("id").select(range(__SCREAMING_SNAKE_CASE))[:num_samples]["audio"]

							return [x["array"] for x in audio_samples]


		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							# fmt: off
							A     =   torch.tensor(
							    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
							     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
							     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
							     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
							     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
							     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03])
							# fmt: on
							A     =   self._load_datasamples(1)
							A     =   EncodecFeatureExtractor()
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(input_values.shape	,	(1, 1, 9_3_6_8_0))
							self.assertTrue(torch.allclose(input_values[0, 0, :3_0]	,	__SCREAMING_SNAKE_CASE	,	atol=1E-6))


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							# fmt: off
							A     =   torch.tensor(
							    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
							     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
							     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
							     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
							     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
							     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03])
							# fmt: on
							A     =   self._load_datasamples(1)
							A     =   [np.tile(input_audio[0][None]	,	reps=(2, 1))]
							input_audio[0][1] *= 0.5
							A     =   EncodecFeatureExtractor(feature_size=2)
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(input_values.shape	,	(1, 2, 9_3_6_8_0))
							self.assertTrue(torch.allclose(input_values[0, 0, :3_0]	,	__SCREAMING_SNAKE_CASE	,	atol=1E-6))
							self.assertTrue(torch.allclose(input_values[0, 1, :3_0]	,	EXPECTED_INPUT_VALUES * 0.5	,	atol=1E-6))


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self._load_datasamples(2)
							# would be easier if the stride was like
							A     =   EncodecFeatureExtractor(feature_size=1	,	chunk_length_s=1	,	overlap=0.0_1)

							# pad and trunc raise an error ?
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE	,	"^Both padding and truncation were set. Make sure you only set one.$"	,	):
												A     =   feature_extractor(
												    __SCREAMING_SNAKE_CASE	,	padding="max_length"	,	truncation=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values

							# truncate to chunk
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	truncation=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(2, 1, 7_1_5_2_0))  # 2 chunks

							# force truncate to max_length
							A     =   feature_extractor(
							    __SCREAMING_SNAKE_CASE	,	truncation=__SCREAMING_SNAKE_CASE	,	max_length=4_8_0_0_0	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(2, 1, 4_8_0_0_0))

							# pad to chunk
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(padded_outputs.shape	,	(2, 1, 9_5_2_8_0))

							# pad to chunk
							A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(2, 1, 9_5_2_8_0))

							# force pad to max length
							A     =   feature_extractor(
							    __SCREAMING_SNAKE_CASE	,	padding="max_length"	,	max_length=1_0_0_0_0_0	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(2, 1, 1_0_0_0_0_0))

							# force no pad
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE	,	"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"	,	):
												A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values

							A     =   feature_extractor(input_audio[0]	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(1, 1, 9_3_6_8_0))

							# no pad if no chunk_length_s
							A     =   None
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE	,	"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"	,	):
												A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values

							A     =   feature_extractor(input_audio[0]	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(1, 1, 9_3_6_8_0))

							# no pad if no overlap
							A     =   2
							A     =   None
							with self.assertRaisesRegex(
							    __SCREAMING_SNAKE_CASE	,	"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"	,	):
												A     =   feature_extractor(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values

							A     =   feature_extractor(input_audio[0]	,	padding=__SCREAMING_SNAKE_CASE	,	return_tensors="pt").input_values
							self.assertEquals(truncated_outputs.shape	,	(1, 1, 9_3_6_8_0))







import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
   import torch


__UpperCAmelCase   						=     logging.get_logger(__name__)





class 				UpperCamelCase__		:






       """simple docstring"""


       def __init__(							self ,     _A ,     _A=21 ,     _A=[] ,     _A=None	)				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      model
          SCREAMING_SNAKE_CASE_	=      trace_batch_nums
          SCREAMING_SNAKE_CASE_	=      abort_after_batch_num

          # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
          SCREAMING_SNAKE_CASE_	=      collections.deque([] ,     _A	)
          SCREAMING_SNAKE_CASE_	=      []
          SCREAMING_SNAKE_CASE_	=      0
          SCREAMING_SNAKE_CASE_	=      0
          SCREAMING_SNAKE_CASE_	=      False
          SCREAMING_SNAKE_CASE_	=      '''                 '''

          self.analyse_model()

          self.register_forward_hook()



       def     _UpperCamelCase	(							self ,     _A=None	)				->				Any:
          if frame is not None:
             self.expand_frame(_A	)
          self.frames.append('''\n'''.join(self.frame	)	)
          SCREAMING_SNAKE_CASE_	=      []  # start a new frame



       def     _UpperCamelCase	(							self ,     _A	)				->				List[Any]:
          self.frame.append(_A	)



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          print('''\n'''.join(self.frames	)	)
          SCREAMING_SNAKE_CASE_	=      []



       def     _UpperCamelCase	(							self	)				->				int:
          SCREAMING_SNAKE_CASE_	=      []



       def     _UpperCamelCase	(							self	)				->				List[str]:
          print(F'''\nDetected inf/nan during batch_number={self.batch_number}'''	)
          print(F'''Last {len(self.frames	)} forward frames:'''	)
          print(F'''{"abs min":8} {"abs max":8} metadata'''	)
          print('''\n'''.join(self.frames	)	)
          print('''\n\n'''	)
          SCREAMING_SNAKE_CASE_	=      []



       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          # extract the fully qualified module names, to be able to report at run time. e.g.:
          # encoder.block.2.layer.0.SelfAttention.o
          #
          # for shared weights only the first shared module name will be registered
          SCREAMING_SNAKE_CASE_	=      {m: name for name, m in self.model.named_modules()}
          # self.longest_module_name = max(len(v) for v in self.module_names.values())



       def     _UpperCamelCase	(							self ,     _A ,     _A	)				->				Union[str, Any]:
          if torch.is_tensor(_A	):
             self.expand_frame(get_abs_min_max(_A ,     _A	)	)
             if detect_overflow(_A ,     _A	):
                SCREAMING_SNAKE_CASE_	=      True
          elif var is None:
             self.expand_frame(F'''{"None":>17} {ctx}'''	)
          else:
             self.expand_frame(F'''{"not a tensor":>17} {ctx}'''	)



       def     _UpperCamelCase	(							self	)				->				List[str]:
          self.expand_frame(F'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''	)
          self.expand_frame(F'''{"abs min":8} {"abs max":8} metadata'''	)



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          self.expand_frame(F'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''	)



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A	)				->				Union[str, Any]:
          self.expand_frame(F'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''	)

          # params
          for name, p in module.named_parameters(recurse=_A	):
             self.analyse_variable(_A ,     _A	)

          # inputs
          if isinstance(_A ,     _A	):
             for i, x in enumerate(_A	):
                self.analyse_variable(_A ,     F'''input[{i}]'''	)
          else:
             self.analyse_variable(_A ,     '''input'''	)

          # outputs
          if isinstance(_A ,     _A	):
             for i, x in enumerate(_A	):
                # possibly a tuple of tuples
                if isinstance(_A ,     _A	):
                   for j, y in enumerate(_A	):
                      self.analyse_variable(_A ,     F'''output[{i}][{j}]'''	)
                else:
                   self.analyse_variable(_A ,     F'''output[{i}]'''	)
          else:
             self.analyse_variable(_A ,     '''output'''	)

          self.save_frame()



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          self.model.apply(self._register_forward_hook	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Tuple:
          module.register_forward_hook(self.forward_hook	)




       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A	)				->				Any:
          # - input is a tuple of packed inputs (could be non-Tensors)
          # - output could be a Tensor or a tuple of Tensors and non-Tensors

          SCREAMING_SNAKE_CASE_	=      False

          SCREAMING_SNAKE_CASE_	=      True if self.batch_number in self.trace_batch_nums else False
          if trace_mode:
             self.reset_saved_frames()

          if self.total_calls == 0:
             self.batch_start_frame()
          self.total_calls += 1

          # count batch numbers - the very first forward hook of the batch will be called when the
          # batch completes - i.e. it gets called very last - we know this batch has finished
          if module == self.model:
             self.batch_number += 1
             SCREAMING_SNAKE_CASE_	=      True

          self.create_frame(_A ,     _A ,     _A	)

          # if last_frame_of_batch:
          #     self.batch_end_frame()

          if trace_mode:
             self.trace_frames()

          if last_frame_of_batch:
             self.batch_start_frame()

          if self.detected_overflow and not trace_mode:
             self.dump_saved_frames()

             # now we can abort, as it's pointless to continue running
             raise ValueError(
                 '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
                 '''Please scroll up above this traceback to see the activation values prior to this event.'''	)

          # abort after certain batch if requested to do so
          if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
             raise ValueError(
                 F'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
                 F''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''	)







def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      var.abs()
   return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      False
   if torch.isnan(__lowerCamelCase						).any().item():
      SCREAMING_SNAKE_CASE_	=      True
      print(F'''{ctx} has nans'''						)
   if torch.isinf(__lowerCamelCase						).any().item():
      SCREAMING_SNAKE_CASE_	=      True
      print(F'''{ctx} has infs'''						)

   # if needed to monitor large elements can enable the following
   if 0:  # and detected:
      SCREAMING_SNAKE_CASE_	=      var[torch.ge(var.abs(),  1_00						)]
      if naaa.numel() > 0:
         print(F'''{ctx}:  n100={naaa.numel()}'''						)
      SCREAMING_SNAKE_CASE_	=      var[torch.ge(var.abs(),  10_00						)]
      if naaaa.numel() > 0:
         print(F'''{ctx}: n1000={naaaa.numel()}'''						)
      SCREAMING_SNAKE_CASE_	=      var[torch.ge(var.abs(),  1_00_00						)]
      if naaaaa.numel() > 0:
         print(F'''{ctx}: n10000={naaaaa.numel()}'''						)

   if 0:
      print(F'''min={var.min():9.2e} max={var.max():9.2e}'''						)

   if 0:
      print(F'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'''						)

   return detected





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="underflow_overflow"
       UpperCAmelCase_				       ="tpu_metrics_debug"


