





'''simple docstring'''


import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

_lowerCamelCase					:						Dict					     =							logging.get_logger(__name__)




def  __a			(  UpperCAmelCase							,      UpperCAmelCase = True     )   ->Optional[int]:



	"""simple docstring"""







	A					=   {}
	for name in model.attn_processors.keys():
		A					=   name.endswith("""attn2.processor"""     ) and not (
		    name.startswith("""transformer_in"""     ) or """temp_attentions""" in name.split("""."""     )
		)
		A					=   model.config.cross_attention_dim if has_cross_attention else None
		if name.startswith("""mid_block"""     ):
			A					=   model.config.block_out_channels[-1]
		elif name.startswith("""up_blocks"""     ):
			A					=   int(name[len("""up_blocks."""     )]     )
			A					=   list(reversed(model.config.block_out_channels     )     )[block_id]
		elif name.startswith("""down_blocks"""     ):
			A					=   int(name[len("""down_blocks."""     )]     )
			A					=   model.config.block_out_channels[block_id]
		elif name.startswith("""transformer_in"""     ):
			# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
			A					=   8 * model.config.attention_head_dim

		A					=   LoRAAttnProcessor(hidden_size=UpperCAmelCase							,      cross_attention_dim=UpperCAmelCase     )
		A					=   lora_attn_procs[name].to(model.device     )

		if mock_weights:
			# add 1 to weights to mock trained weights
			with torch.no_grad():
				lora_attn_procs[name].to_q_lora.up.weight += 1
				lora_attn_procs[name].to_k_lora.up.weight += 1
				lora_attn_procs[name].to_v_lora.up.weight += 1
				lora_attn_procs[name].to_out_lora.up.weight += 1

	return lora_attn_procs



@skip_mps
class 				__UpperCAmelCase	(							A__		,       A__		,       unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     UNetaDConditionModel
		__lowerCAmelCase								=     '''sample'''







		@property
		def 							A  (self    :   Optional[int]   ):
			A					=   4
			A					=   4
			A					=   4
			A					=   (32, 32)

			A					=   floats_tensor((batch_size, num_channels, num_frames) + sizes   ).to(_lowerCAmelCase   )
			A					=   torch.tensor([10]   ).to(_lowerCAmelCase   )
			A					=   floats_tensor((batch_size, 4, 32)   ).to(_lowerCAmelCase   )

			return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}







		@property
		def 							A  (self    :   Dict   ):
			return (4, 4, 32, 32)







		@property
		def 							A  (self    :   Optional[Any]   ):
			return (4, 4, 32, 32)







		def 							A  (self    :   str   ):
			A					=   {
			    """block_out_channels""": (32, 64),
			    """down_block_types""": (
			        """CrossAttnDownBlock3D""",
			        """DownBlock3D""",
			    ),
			    """up_block_types""": ("""UpBlock3D""", """CrossAttnUpBlock3D"""),
			    """cross_attention_dim""": 32,
			    """attention_head_dim""": 8,
			    """out_channels""": 4,
			    """in_channels""": 4,
			    """layers_per_block""": 1,
			    """sample_size""": 32,
			}
			A					=   self.dummy_input
			return init_dict, inputs_dict







		@unittest.skipIf(
		    torch_device != """cuda""" or not is_xformers_available()   ,   reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,   )
		def 							A  (self    :   int   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()
			A					=   self.model_class(**_lowerCAmelCase   )

			model.enable_xformers_memory_efficient_attention()

			assert (
			    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
			    == "XFormersAttnProcessor"
			), "xformers is not enabled"







		def 							A  (self    :   List[str]   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   32

			A					=   self.model_class(**_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )
			model.eval()

			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   )

				if isinstance(_lowerCAmelCase   ,   _lowerCAmelCase   ):
					A					=   output.sample

			self.assertIsNotNone(_lowerCAmelCase   )
			A					=   inputs_dict["""sample"""].shape
			self.assertEqual(output.shape   ,   _lowerCAmelCase   ,   """Input and output shapes do not match"""   )







		def 							A  (self    :   str   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()
			A					=   self.model_class(**_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )
			model.eval()

			with torch.no_grad():
				# Warmup pass when using mps (see #372)
				if torch_device == "mps" and isinstance(_lowerCAmelCase   ,   _lowerCAmelCase   ):
					model(**self.dummy_input   )

				A					=   model(**_lowerCAmelCase   )
				if isinstance(_lowerCAmelCase   ,   _lowerCAmelCase   ):
					A					=   first.sample

				A					=   model(**_lowerCAmelCase   )
				if isinstance(_lowerCAmelCase   ,   _lowerCAmelCase   ):
					A					=   second.sample

			A					=   first.cpu().numpy()
			A					=   second.cpu().numpy()
			A					=   out_a[~np.isnan(_lowerCAmelCase   )]
			A					=   out_a[~np.isnan(_lowerCAmelCase   )]
			A					=   np.amax(np.abs(out_a - out_a   )   )
			self.assertLessEqual(_lowerCAmelCase   ,   1e-5   )







		def 							A  (self    :   Optional[int]   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			A					=   self.model_class(**_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )
			model.eval()

			model.set_attention_slice("""auto"""   )
			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   )
			assert output is not None

			model.set_attention_slice("""max"""   )
			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   )
			assert output is not None

			model.set_attention_slice(2   )
			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   )
			assert output is not None







		def 							A  (self    :   Any   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			A					=   self.model_class(**_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )

			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   ).sample

			A					=   create_lora_layers(_lowerCAmelCase   )

			# make sure we can set a list of attention processors
			model.set_attn_processor(_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )

			# test that attn processors can be set to itself
			model.set_attn_processor(model.attn_processors   )

			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   ,   cross_attention_kwargs={"""scale""": 0.0}   ).sample
				A					=   model(**_lowerCAmelCase   ,   cross_attention_kwargs={"""scale""": 0.5}   ).sample
				A					=   model(**_lowerCAmelCase   ,   cross_attention_kwargs={"""scale""": 0.5}   ).sample

			assert (samplea - samplea).abs().max() < 3e-3
			assert (samplea - samplea).abs().max() < 3e-3

			# sample 2 and sample 3 should be different
			assert (samplea - samplea).abs().max() > 3e-3







		def 							A  (self    :   Optional[Any]   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			torch.manual_seed(0   )
			A					=   self.model_class(**_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )

			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   ).sample

			A					=   create_lora_layers(_lowerCAmelCase   )
			model.set_attn_processor(_lowerCAmelCase   )

			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   ,   cross_attention_kwargs={"""scale""": 0.5}   ).sample

			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_attn_procs(_lowerCAmelCase   )
				self.assertTrue(os.path.isfile(os.path.join(_lowerCAmelCase   ,   """pytorch_lora_weights.bin"""   )   )   )
				torch.manual_seed(0   )
				A					=   self.model_class(**_lowerCAmelCase   )
				new_model.to(_lowerCAmelCase   )
				new_model.load_attn_procs(_lowerCAmelCase   )

			with torch.no_grad():
				A					=   new_model(**_lowerCAmelCase   ,   cross_attention_kwargs={"""scale""": 0.5}   ).sample

			assert (sample - new_sample).abs().max() < 1e-3

			# LoRA and no LoRA should NOT be the same
			assert (sample - old_sample).abs().max() > 1e-4







		def 							A  (self    :   Union[str, Any]   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			torch.manual_seed(0   )
			A					=   self.model_class(**_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )

			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   ).sample

			A					=   create_lora_layers(_lowerCAmelCase   )
			model.set_attn_processor(_lowerCAmelCase   )

			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   ,   cross_attention_kwargs={"""scale""": 0.5}   ).sample

			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_attn_procs(_lowerCAmelCase   ,   safe_serialization=_lowerCAmelCase   )
				self.assertTrue(os.path.isfile(os.path.join(_lowerCAmelCase   ,   """pytorch_lora_weights.safetensors"""   )   )   )
				torch.manual_seed(0   )
				A					=   self.model_class(**_lowerCAmelCase   )
				new_model.to(_lowerCAmelCase   )
				new_model.load_attn_procs(_lowerCAmelCase   )

			with torch.no_grad():
				A					=   new_model(**_lowerCAmelCase   ,   cross_attention_kwargs={"""scale""": 0.5}   ).sample

			assert (sample - new_sample).abs().max() < 3e-3

			# LoRA and no LoRA should NOT be the same
			assert (sample - old_sample).abs().max() > 1e-4







		def 							A  (self    :   List[Any]   ):
			# enable deterministic behavior for gradient checkpointing
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			torch.manual_seed(0   )
			A					=   self.model_class(**_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )

			A					=   create_lora_layers(_lowerCAmelCase   ,   mock_weights=_lowerCAmelCase   )
			model.set_attn_processor(_lowerCAmelCase   )
			# Saving as torch, properly reloads with directly filename
			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_attn_procs(_lowerCAmelCase   )
				self.assertTrue(os.path.isfile(os.path.join(_lowerCAmelCase   ,   """pytorch_lora_weights.bin"""   )   )   )
				torch.manual_seed(0   )
				A					=   self.model_class(**_lowerCAmelCase   )
				new_model.to(_lowerCAmelCase   )
				new_model.load_attn_procs(_lowerCAmelCase   ,   weight_name="""pytorch_lora_weights.bin"""   )







		def 							A  (self    :   Optional[int]   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			torch.manual_seed(0   )
			A					=   self.model_class(**_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )

			A					=   create_lora_layers(_lowerCAmelCase   ,   mock_weights=_lowerCAmelCase   )
			model.set_attn_processor(_lowerCAmelCase   )
			# Saving as torch, properly reloads with directly filename
			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_attn_procs(_lowerCAmelCase   )
				self.assertTrue(os.path.isfile(os.path.join(_lowerCAmelCase   ,   """pytorch_lora_weights.bin"""   )   )   )
				torch.manual_seed(0   )
				A					=   self.model_class(**_lowerCAmelCase   )
				new_model.to(_lowerCAmelCase   )
				with self.assertRaises(_lowerCAmelCase   ) as e:
					new_model.load_attn_procs(_lowerCAmelCase   ,   use_safetensors=_lowerCAmelCase   )
				self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""   ,   str(e.exception   )   )







		def 							A  (self    :   Dict   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   8

			torch.manual_seed(0   )
			A					=   self.model_class(**_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )

			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   ).sample

			A					=   create_lora_layers(_lowerCAmelCase   )
			model.set_attn_processor(_lowerCAmelCase   )

			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   ,   cross_attention_kwargs={"""scale""": 0.0}   ).sample

			model.set_attn_processor(AttnProcessor()   )

			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   ).sample

			assert (sample - new_sample).abs().max() < 1e-4
			assert (sample - old_sample).abs().max() < 3e-3







		@unittest.skipIf(
		    torch_device != """cuda""" or not is_xformers_available()   ,   reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,   )
		def 							A  (self    :   Any   ):
			# enable deterministic behavior for gradient checkpointing
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()

			A					=   4

			torch.manual_seed(0   )
			A					=   self.model_class(**_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )
			A					=   create_lora_layers(_lowerCAmelCase   )
			model.set_attn_processor(_lowerCAmelCase   )

			# default
			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   ).sample

				model.enable_xformers_memory_efficient_attention()
				A					=   model(**_lowerCAmelCase   ).sample

				model.disable_xformers_memory_efficient_attention()
				A					=   model(**_lowerCAmelCase   ).sample

			assert (sample - on_sample).abs().max() < 1e-4
			assert (sample - off_sample).abs().max() < 1e-4







		def 							A  (self    :   str   ):
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()
			A					=   32

			A					=   self.model_class(**_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )
			model.eval()

			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   )[0]

			model.enable_forward_chunking()
			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   )[0]

			self.assertEqual(output.shape   ,   output_a.shape   ,   """Shape doesn't match"""   )
			assert np.abs(output.cpu() - output_a.cpu()   ).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.








'''simple docstring'''


import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
				from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
				_lowerCamelCase					:						Optional[Any]					     =							True
				from torch.cuda.amp import autocast


_lowerCamelCase					:						List[Any]					     =							logging.getLogger(__name__)



@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     field(
		    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Whether to log verbose messages or not.'''}		,       )






def  __a			(  UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,      datefmt="""%m/%d/%Y %H:%M:%S"""							,      handlers=[logging.StreamHandler(sys.stdout     )]							,      )
	A					=   logging.WARNING
	if model_args.verbose_logging:
		A					=   logging.DEBUG
	elif trainer_utils.is_main_process(training_args.local_rank     ):
		A					=   logging.INFO
	logger.setLevel(UpperCAmelCase     )



@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}    )
		__lowerCAmelCase								=     field(
		    default='''train'''		,       metadata={
		        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
		    }		,       )
		__lowerCAmelCase								=     field(
		    default='''validation'''		,       metadata={
		        '''help''': (
		            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
		        )
		    }		,       )
		__lowerCAmelCase								=     field(
		    default='''text'''		,       metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''}		,       )
		__lowerCAmelCase								=     field(
		    default='''file'''		,       metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''}		,       )
		__lowerCAmelCase								=     field(
		    default='''librispeech'''		,       metadata={
		        '''help''': (
		            '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
		            ''' \'buckwalter\'.'''
		        )
		    }		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''The number of processes to use for the preprocessing.'''}		,       )






@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     False
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     "|"
		__lowerCAmelCase								=     field(default_factory=A__    )
		__lowerCAmelCase								=     field(default_factory=A__    )
		__lowerCAmelCase								=     None







		@classmethod
		def 							A  (cls    :   List[str]   ,   _lowerCAmelCase    :   str   ):
			if name == "librispeech":
				return cls()
			if name == "timit":
				return cls(
				    do_lower_case=_lowerCAmelCase   ,   translation_table=str.maketrans({"""-""": """ """}   )   ,   )
			if name == "buckwalter":
				A					=   {
				    """-""": """ """,  # sometimes used to represent pauses
				    """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
				}
				return cls(
				    vocab_file=pathlib.Path(_lowerCAmelCase   ).parent.joinpath("""vocab/buckwalter.json"""   )   ,   word_delimiter_token="""/"""   ,   translation_table=str.maketrans(_lowerCAmelCase   )   ,   words_to_remove={"""sil"""}   ,   untransliterator=arabic.buckwalter.untransliterate   ,   )
			raise ValueError(F"""Unsupported orthography: '{name}'."""   )







		def 							A  (self    :   Union[str, Any]   ,   _lowerCAmelCase    :   str   ):
			# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
			if len(self.translation_table   ) > 0:
				A					=   text.translate(self.translation_table   )
			if len(self.words_to_remove   ) == 0:
				A					=   """ """.join(text.split()   )  # clean up whitespaces
			else:
				A					=   """ """.join(w for w in text.split() if w not in self.words_to_remove   )  # and clean up whilespaces
			return text







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   ModelArguments   ):
			A					=   WavaVecaFeatureExtractor.from_pretrained(
			    model_args.model_name_or_path   ,   cache_dir=model_args.cache_dir   )
			if self.vocab_file:
				A					=   WavaVecaCTCTokenizer(
				    self.vocab_file   ,   cache_dir=model_args.cache_dir   ,   do_lower_case=self.do_lower_case   ,   word_delimiter_token=self.word_delimiter_token   ,   )
			else:
				A					=   WavaVecaCTCTokenizer.from_pretrained(
				    model_args.model_name_or_path   ,   cache_dir=model_args.cache_dir   ,   do_lower_case=self.do_lower_case   ,   word_delimiter_token=self.word_delimiter_token   ,   )
			return WavaVecaProcessor(_lowerCAmelCase   ,   _lowerCAmelCase   )






@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     42
		__lowerCAmelCase								=     True
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None







		def __call__(self    :   List[str]   ,   _lowerCAmelCase    :   List[Dict[str, Union[List[int], torch.Tensor]]]   ):
			# split inputs and labels since they have to be of different lenghts and need
			# different padding methods
			A					=   [{"""input_values""": feature["""input_values"""]} for feature in features]
			A					=   [{"""input_ids""": feature["""labels"""]} for feature in features]

			A					=   self.processor.pad(
			    _lowerCAmelCase   ,   padding=self.padding   ,   max_length=self.max_length   ,   pad_to_multiple_of=self.pad_to_multiple_of   ,   return_tensors="""pt"""   ,   )
			A					=   self.processor.pad(
			    labels=_lowerCAmelCase   ,   padding=self.padding   ,   max_length=self.max_length_labels   ,   pad_to_multiple_of=self.pad_to_multiple_of_labels   ,   return_tensors="""pt"""   ,   )

			# replace padding with -100 to ignore loss correctly
			A					=   labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1   )   ,   -100   )

			A					=   labels

			return batch






class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   nn.Module   ,   _lowerCAmelCase    :   Dict[str, Union[torch.Tensor, Any]]   ):
			model.train()
			A					=   self._prepare_inputs(_lowerCAmelCase   )

			if self.use_amp:
				with autocast():
					A					=   self.compute_loss(_lowerCAmelCase   ,   _lowerCAmelCase   )
			else:
				A					=   self.compute_loss(_lowerCAmelCase   ,   _lowerCAmelCase   )

			if self.args.n_gpu > 1:
				if model.module.config.ctc_loss_reduction == "mean":
					A					=   loss.mean()
				elif model.module.config.ctc_loss_reduction == "sum":
					A					=   loss.sum() / (inputs["""labels"""] >= 0).sum()
				else:
					raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""   )

			if self.args.gradient_accumulation_steps > 1:
				A					=   loss / self.args.gradient_accumulation_steps

			if self.use_amp:
				self.scaler.scale(_lowerCAmelCase   ).backward()
			elif self.use_apex:
				with amp.scale_loss(_lowerCAmelCase   ,   self.optimizer   ) as scaled_loss:
					scaled_loss.backward()
			elif self.deepspeed:
				self.deepspeed.backward(_lowerCAmelCase   )
			else:
				loss.backward()

			return loss.detach()






def  __a			(  )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )

	A	,     A	,     A					=   parser.parse_args_into_dataclasses()
	configure_logger(UpperCAmelCase							,      UpperCAmelCase     )

	A					=   Orthography.from_name(data_args.orthography.lower()     )
	A					=   orthography.create_processor(UpperCAmelCase     )
	A					=   WavaVecaForCTC.from_pretrained(
	    model_args.model_name_or_path							,      cache_dir=model_args.cache_dir							,      gradient_checkpointing=training_args.gradient_checkpointing							,      vocab_size=len(processor.tokenizer     )							,      )

	A					=   datasets.load_dataset(
	    data_args.dataset_name							,      data_args.dataset_config_name							,      split=data_args.train_split_name     )
	A					=   datasets.load_dataset(
	    data_args.dataset_name							,      data_args.dataset_config_name							,      split=data_args.validation_split_name     )

	A					=   datasets.load_metric("""wer"""     )
	A					=   processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
	A					=   """""".join(t for t in processor.tokenizer.get_vocab().keys() if len(UpperCAmelCase     ) == 1     )
	A					=   re.compile(  # remove characters not in vocabulary
	    Rf"""[^\s{re.escape(UpperCAmelCase     )}]"""							,      flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0							,      )
	A					=   []

	def prepare_example(UpperCAmelCase     ):  # TODO(elgeish) make use of multiprocessing?
		A	,     A					=   librosa.load(example[data_args.speech_file_column]							,      sr=UpperCAmelCase     )
		if data_args.max_duration_in_seconds is not None:
			A					=   len(example["""speech"""]     ) / example["""sampling_rate"""]
		# Normalize and clean up text; order matters!
		A					=   orthography.preprocess_for_training(example[data_args.target_text_column]     )
		A					=   vocabulary_text_cleaner.sub(""""""							,      UpperCAmelCase     )
		if updated_text != example[data_args.target_text_column]:
			text_updates.append((example[data_args.target_text_column], updated_text)     )
			A					=   updated_text
		return example

	A					=   train_dataset.map(UpperCAmelCase							,      remove_columns=[data_args.speech_file_column]     )
	A					=   val_dataset.map(UpperCAmelCase							,      remove_columns=[data_args.speech_file_column]     )

	if data_args.max_duration_in_seconds is not None:

		def filter_by_max_duration(UpperCAmelCase     ):
			return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

		A					=   len(UpperCAmelCase     )
		A					=   len(UpperCAmelCase     )
		A					=   train_dataset.filter(UpperCAmelCase							,      remove_columns=["""duration_in_seconds"""]     )
		A					=   val_dataset.filter(UpperCAmelCase							,      remove_columns=["""duration_in_seconds"""]     )
		if len(UpperCAmelCase     ) > old_train_size:
			logger.warning(
			    f"""Filtered out {len(UpperCAmelCase     ) - old_train_size} train example(s) longer than"""
			    f""" {data_args.max_duration_in_seconds} second(s)."""     )
		if len(UpperCAmelCase     ) > old_val_size:
			logger.warning(
			    f"""Filtered out {len(UpperCAmelCase     ) - old_val_size} validation example(s) longer than"""
			    f""" {data_args.max_duration_in_seconds} second(s)."""     )
	logger.info(f"""Split sizes: {len(UpperCAmelCase     )} train and {len(UpperCAmelCase     )} validation."""     )

	logger.warning(f"""Updated {len(UpperCAmelCase     )} transcript(s) using '{data_args.orthography}' orthography rules."""     )
	if logger.isEnabledFor(logging.DEBUG     ):
		for original_text, updated_text in text_updates:
			logger.debug(f"""Updated text: \"{original_text}\" -> \"{updated_text}\""""     )
	A					=   None

	def prepare_dataset(UpperCAmelCase     ):
		# check that all files have the correct sampling rate
		assert (
		    len(set(batch["""sampling_rate"""]     )     ) == 1
		), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""

		A					=   processor(
		    audio=batch["""speech"""]							,      text=batch[data_args.target_text_column]							,      sampling_rate=batch["""sampling_rate"""][0]     )
		batch.update(UpperCAmelCase     )
		return batch

	A					=   train_dataset.map(
	    UpperCAmelCase							,      batch_size=training_args.per_device_train_batch_size							,      batched=UpperCAmelCase							,      num_proc=data_args.preprocessing_num_workers							,      )
	A					=   val_dataset.map(
	    UpperCAmelCase							,      batch_size=training_args.per_device_train_batch_size							,      batched=UpperCAmelCase							,      num_proc=data_args.preprocessing_num_workers							,      )

	A					=   DataCollatorCTCWithPadding(processor=UpperCAmelCase							,      padding=UpperCAmelCase     )

	def compute_metrics(UpperCAmelCase     ):
		A					=   pred.predictions
		A					=   np.argmax(UpperCAmelCase							,      axis=-1     )

		A					=   processor.tokenizer.pad_token_id

		A					=   processor.batch_decode(UpperCAmelCase     )
		# we do not want to group tokens when computing the metrics
		A					=   processor.batch_decode(pred.label_ids							,      group_tokens=UpperCAmelCase     )
		if logger.isEnabledFor(logging.DEBUG     ):
			for reference, predicted in zip(UpperCAmelCase							,      UpperCAmelCase     ):
				logger.debug(f"""reference: \"{reference}\""""     )
				logger.debug(f"""predicted: \"{predicted}\""""     )
				if orthography.untransliterator is not None:
					logger.debug(f"""reference (untransliterated): \"{orthography.untransliterator(UpperCAmelCase     )}\""""     )
					logger.debug(f"""predicted (untransliterated): \"{orthography.untransliterator(UpperCAmelCase     )}\""""     )

		A					=   wer_metric.compute(predictions=UpperCAmelCase							,      references=UpperCAmelCase     )

		return {"wer": wer}

	if model_args.freeze_feature_extractor:
		model.freeze_feature_extractor()

	A					=   CTCTrainer(
	    model=UpperCAmelCase							,      data_collator=UpperCAmelCase							,      args=UpperCAmelCase							,      compute_metrics=UpperCAmelCase							,      train_dataset=UpperCAmelCase							,      eval_dataset=UpperCAmelCase							,      tokenizer=processor.feature_extractor							,      )

	trainer.train()


if __name__ == "__main__":
				main()

