




import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
						from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


SCREAMING_SNAKE_CASE											=   get_tests_dir('''fixtures/test_sentencepiece.model''')



@require_torch
@require_torchaudio
@require_sentencepiece
class       lowercase_		(unittest.TestCase   ):



						def   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       tempfile.mkdtemp()

								SCREAMING_SNAKE_CASE_							=       ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
								SCREAMING_SNAKE_CASE_							=       dict(zip(snake_case__				,       range(len(snake_case__     )     )     )     )
								SCREAMING_SNAKE_CASE_							=       Path(self.tmpdirname     )
								save_json(snake_case__				,       save_dir / VOCAB_FILES_NAMES['vocab_file']     )
								if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
										copyfile(snake_case__				,       save_dir / VOCAB_FILES_NAMES['spm_file']     )

								SCREAMING_SNAKE_CASE_							=       SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
								tokenizer.save_pretrained(self.tmpdirname     )

								SCREAMING_SNAKE_CASE_							=       {
								    'feature_size': 24,
								    'num_mel_bins': 24,
								    'padding_value': 0.0,
								    'sampling_rate': 1_60_00,
								    'return_attention_mask': False,
								    'do_normalize': True,
								}
								save_json(snake_case__				,       save_dir / FEATURE_EXTRACTOR_NAME     )



						def   __a				( self  :      List[Any]				,       **snake_case__  :      Any     ):

								"""simple docstring"""

								return SpeechaTextTokenizer.from_pretrained(self.tmpdirname				,       **snake_case__     )



						def   __a				( self  :      List[str]				,       **snake_case__  :      int     ):

								"""simple docstring"""

								return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname				,       **snake_case__     )



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								shutil.rmtree(self.tmpdirname     )



						def   __a				( self  :      Any     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.get_tokenizer()
								SCREAMING_SNAKE_CASE_							=       self.get_feature_extractor()

								SCREAMING_SNAKE_CASE_							=       SpeechaTextProcessor(tokenizer=snake_case__				,       feature_extractor=snake_case__     )

								processor.save_pretrained(self.tmpdirname     )
								SCREAMING_SNAKE_CASE_							=       SpeechaTextProcessor.from_pretrained(self.tmpdirname     )

								self.assertEqual(processor.tokenizer.get_vocab()				,       tokenizer.get_vocab()     )
								self.assertIsInstance(processor.tokenizer				,       snake_case__     )

								self.assertEqual(processor.feature_extractor.to_json_string()				,       feature_extractor.to_json_string()     )
								self.assertIsInstance(processor.feature_extractor				,       snake_case__     )



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       SpeechaTextProcessor(
								    tokenizer=self.get_tokenizer()				,       feature_extractor=self.get_feature_extractor()     )
								processor.save_pretrained(self.tmpdirname     )

								SCREAMING_SNAKE_CASE_							=       self.get_tokenizer(bos_token='(BOS)'				,       eos_token='(EOS)'     )
								SCREAMING_SNAKE_CASE_							=       self.get_feature_extractor(do_normalize=snake_case__				,       padding_value=1.0     )

								SCREAMING_SNAKE_CASE_							=       SpeechaTextProcessor.from_pretrained(
								    self.tmpdirname				,       bos_token='(BOS)'				,       eos_token='(EOS)'				,       do_normalize=snake_case__				,       padding_value=1.0     )

								self.assertEqual(processor.tokenizer.get_vocab()				,       tokenizer_add_kwargs.get_vocab()     )
								self.assertIsInstance(processor.tokenizer				,       snake_case__     )

								self.assertEqual(processor.feature_extractor.to_json_string()				,       feature_extractor_add_kwargs.to_json_string()     )
								self.assertIsInstance(processor.feature_extractor				,       snake_case__     )



						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.get_feature_extractor()
								SCREAMING_SNAKE_CASE_							=       self.get_tokenizer()

								SCREAMING_SNAKE_CASE_							=       SpeechaTextProcessor(tokenizer=snake_case__				,       feature_extractor=snake_case__     )

								SCREAMING_SNAKE_CASE_							=       floats_list((3, 10_00)     )

								SCREAMING_SNAKE_CASE_							=       feature_extractor(snake_case__				,       return_tensors='np'     )
								SCREAMING_SNAKE_CASE_							=       processor(snake_case__				,       return_tensors='np'     )

								for key in input_feat_extract.keys():
										self.assertAlmostEqual(input_feat_extract[key].sum()				,       input_processor[key].sum()				,       delta=1e-2     )



						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.get_feature_extractor()
								SCREAMING_SNAKE_CASE_							=       self.get_tokenizer()

								SCREAMING_SNAKE_CASE_							=       SpeechaTextProcessor(tokenizer=snake_case__				,       feature_extractor=snake_case__     )

								SCREAMING_SNAKE_CASE_							=       'This is a test string'

								SCREAMING_SNAKE_CASE_							=       processor(text=snake_case__     )

								SCREAMING_SNAKE_CASE_							=       tokenizer(snake_case__     )

								for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]				,       encoded_processor[key]     )



						def   __a				( self  :      int     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.get_feature_extractor()
								SCREAMING_SNAKE_CASE_							=       self.get_tokenizer()

								SCREAMING_SNAKE_CASE_							=       SpeechaTextProcessor(tokenizer=snake_case__				,       feature_extractor=snake_case__     )

								SCREAMING_SNAKE_CASE_							=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								SCREAMING_SNAKE_CASE_							=       processor.batch_decode(snake_case__     )
								SCREAMING_SNAKE_CASE_							=       tokenizer.batch_decode(snake_case__     )

								self.assertListEqual(snake_case__				,       snake_case__     )






						def   __a				( self  :      Any     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.get_feature_extractor()
								SCREAMING_SNAKE_CASE_							=       self.get_tokenizer()

								SCREAMING_SNAKE_CASE_							=       SpeechaTextProcessor(tokenizer=snake_case__				,       feature_extractor=snake_case__     )

								self.assertListEqual(
								    processor.model_input_names				,       feature_extractor.model_input_names				,       msg='`processor` and `feature_extractor` model input names do not match'				,       )





import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


SCREAMING_SNAKE_CASE:    Any											=   logging.get_logger(__name__)



class       lowercase_		:



						def __init__( self  :      Union[str, Any]				,       snake_case__  :      List[str]				,       snake_case__  :      Dict=21				,       snake_case__  :      List[str]=[]				,       snake_case__  :      List[str]=None     ):

								"""simple docstring"""

								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([]				,       snake_case__     )
								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   __a				( self  :      Union[str, Any]				,       snake_case__  :      Optional[int]=None     ):

								"""simple docstring"""

								if frame is not None:
										self.expand_frame(snake_case__     )
								self.frames.append('\n'.join(self.frame     )     )
								SCREAMING_SNAKE_CASE_							=       []  # start a new frame



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      Any     ):

								"""simple docstring"""

								self.frame.append(snake_case__     )



						def   __a				( self  :      Optional[int]     ):

								"""simple docstring"""

								print('\n'.join(self.frames     )     )
								SCREAMING_SNAKE_CASE_							=       []



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       []



						def   __a				( self  :      int     ):

								"""simple docstring"""

								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   __a				( self  :      str     ):

								"""simple docstring"""

								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   __a				( self  :      List[str]				,       snake_case__  :      List[str]				,       snake_case__  :      Union[str, Any]     ):

								"""simple docstring"""

								if torch.is_tensor(snake_case__     ):
										self.expand_frame(get_abs_min_max(snake_case__				,       snake_case__     )     )
										if detect_overflow(snake_case__				,       snake_case__     ):
												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   __a				( self  :      str     ):

								"""simple docstring"""

								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   __a				( self  :      List[str]     ):

								"""simple docstring"""

								self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''     )



						def   __a				( self  :      Optional[int]				,       snake_case__  :      int				,       snake_case__  :      List[Any]				,       snake_case__  :      str     ):

								"""simple docstring"""

								self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''     )

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

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

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

								self.save_frame()



						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								self.model.apply(self._register_forward_hook     )



						def   __a				( self  :      int				,       snake_case__  :      Optional[int]     ):

								"""simple docstring"""

								module.register_forward_hook(self.forward_hook     )






						def   __a				( self  :      Optional[Any]				,       snake_case__  :      str				,       snake_case__  :      Optional[Any]				,       snake_case__  :      Dict     ):

								"""simple docstring"""

								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(snake_case__				,       snake_case__				,       snake_case__     )

								# 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   )->  Union[str, Any]:
		SCREAMING_SNAKE_CASE_							=       var.abs()
		return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  int:
		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()						,	100   )]
				if naaa.numel() > 0:
						print(F'''{ctx}:  n100={naaa.numel()}'''   )
				SCREAMING_SNAKE_CASE_							=       var[torch.ge(var.abs()						,	1000   )]
				if naaaa.numel() > 0:
						print(F'''{ctx}: n1000={naaaa.numel()}'''   )
				SCREAMING_SNAKE_CASE_							=       var[torch.ge(var.abs()						,	10000   )]
				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       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			="underflow_overflow"
						lowerCAmelCase__ 			="tpu_metrics_debug"