
"""simple docstring"""


import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
_lowercase							     =			logging.getLogger(__name__)

_lowercase							     =			int(1_00_00)  # Hardcoded max length to avoid infinite loop

_lowercase							     =			{
    '''gpt2''': (GPTaLMHeadModel, GPTaTokenizer),
    '''ctrl''': (CTRLLMHeadModel, CTRLTokenizer),
    '''openai-gpt''': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    '''xlnet''': (XLNetLMHeadModel, XLNetTokenizer),
    '''transfo-xl''': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    '''xlm''': (XLMWithLMHeadModel, XLMTokenizer),
    '''gptj''': (GPTJForCausalLM, AutoTokenizer),
    '''bloom''': (BloomForCausalLM, BloomTokenizerFast),
    '''llama''': (LlamaForCausalLM, LlamaTokenizer),
    '''opt''': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
_lowercase							     =			'''In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>'''






def  _snake_case			(						snake_case__				:       int       ):
	np.random.seed(args.seed       )
	torch.manual_seed(args.seed       )
	if args.n_gpu > 0:
		torch.cuda.manual_seed_all(args.seed       )






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







	if args.temperature > 0.7:
		logger.info('CTRL typically works better with lower temperatures (and lower top_k).'       )

	A  				=				tokenizer.encode(__UpperCamelCase		, add_special_tokens=__UpperCamelCase       )
	if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()       ):
		logger.info('WARNING! You are not starting your generation from a control code so you won\'t get good results'       )
	return prompt_text






def  _snake_case			(						snake_case__				:       Tuple		, snake_case__				:       int		, snake_case__				:       Optional[int]		, snake_case__				:       Optional[Any]       ):
	A  				=				hasattr(model.config		, 'use_lang_emb'       ) and model.config.use_lang_emb
	if hasattr(model.config		, 'lang2id'       ) and use_lang_emb:
		A  				=				model.config.langaid.keys()
		if args.xlm_language in available_languages:
			A  				=				args.xlm_language
		else:
			A  				=				None
			while language not in available_languages:
				A  				=				input('Using XLM. Select language in ' + str(list(__UpperCamelCase       )       ) + ' >>> '       )

		A  				=				model.config.langaid[language]
		# kwargs["language"] = tokenizer.lang2id[language]

	# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
	# XLM masked-language modeling (MLM) models need masked token
	# is_xlm_mlm = "mlm" in args.model_name_or_path
	# if is_xlm_mlm:
	#     kwargs["mask_token_id"] = tokenizer.mask_token_id

	return prompt_text






def  _snake_case			(						snake_case__				:       Dict		, snake_case__				:       Tuple		, snake_case__				:       Union[str, Any]		, snake_case__				:       str       ):
	A  				=				args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
	A  				=				prefix + prompt_text
	return prompt_text






def  _snake_case			(						snake_case__				:       List[Any]		, snake_case__				:       Optional[int]		, snake_case__				:       Optional[Any]		, snake_case__				:       Any       ):
	A  				=				args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
	A  				=				prefix + prompt_text
	return prompt_text


_lowercase							     =			{
    '''ctrl''': prepare_ctrl_input,
    '''xlm''': prepare_xlm_input,
    '''xlnet''': prepare_xlnet_input,
    '''transfo-xl''': prepare_transfoxl_input,
}






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       List[Any]       ):







	if length < 0 and max_sequence_length > 0:
		A  				=				max_sequence_length
	elif 0 < max_sequence_length < length:
		A  				=				max_sequence_length  # No generation bigger than model size
	elif length < 0:
		A  				=				MAX_LENGTH  # avoid infinite loop
	return length






def  _snake_case			(						snake_case__				:       Optional[int]       ):
	A  				=				None
	if hasattr(__UpperCamelCase		, 'hidden_size'       ):
		A  				=				model_config.hidden_size
	elif hasattr(__UpperCamelCase		, 'n_embed'       ):
		A  				=				model_config.n_embed
	elif hasattr(__UpperCamelCase		, 'n_embd'       ):
		A  				=				model_config.n_embd

	A  				=				None
	if hasattr(__UpperCamelCase		, 'num_attention_heads'       ):
		A  				=				model_config.num_attention_heads
	elif hasattr(__UpperCamelCase		, 'n_head'       ):
		A  				=				model_config.n_head

	if embedding_size is None or num_head is None or num_head == 0:
		raise ValueError('Check the model config'       )

	A  				=				int(embedding_size / num_head       )
	if hasattr(__UpperCamelCase		, 'n_layer'       ):
		A  				=				model_config.n_layer
	elif hasattr(__UpperCamelCase		, 'num_hidden_layers'       ):
		A  				=				model_config.num_hidden_layers
	else:
		raise ValueError('Number of hidden layers couldn\'t be determined from the model config'       )

	return num_layer, num_head, num_embedding_size_per_head






def  _snake_case			(						snake_case__				:       Optional[int]		, snake_case__				:       List[Any]		, snake_case__				:       Tuple       ):
	A				, A				, A  				=				sparse_model_config(model.config       )
	if model.config.model_type == "bloom":
		A  				=				tuple(
		    (
		        torch.empty(int(num_attention_heads * batch_size       )		, __UpperCamelCase		, __UpperCamelCase       )
		        .to(model.dtype       )
		        .to(model.device       ),
		        torch.empty(int(num_attention_heads * batch_size       )		, __UpperCamelCase		, __UpperCamelCase       )
		        .to(model.dtype       )
		        .to(model.device       ),
		    )
		    for _ in range(__UpperCamelCase       )       )
	else:
		A  				=				tuple(
		    (
		        torch.empty(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase       )
		        .to(model.dtype       )
		        .to(model.device       ),
		        torch.empty(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase       )
		        .to(model.dtype       )
		        .to(model.device       ),
		    )
		    for _ in range(__UpperCamelCase       )       )
	return past_key_values






def  _snake_case			(						snake_case__				:       Any		, snake_case__				:       Optional[int]		, snake_case__				:       int       ):
	A  				=				len(__UpperCamelCase       )
	A  				=				tokenizer.batch_encode_plus(__UpperCamelCase		, return_tensors='pt'       )
	A  				=				dummy_input.to(model.device       )
	if model.config.use_cache:
		A  				=				generate_past_key_values(__UpperCamelCase		, __UpperCamelCase		, 1       )
	A  				=				torch.cat(
	    [
	        torch.zeros(dummy_input['attention_mask'].shape[0]		, 1       )
	        .to(dummy_input['attention_mask'].dtype       )
	        .to(model.device       ),
	        dummy_input['attention_mask'],
	    ]		, -1		, )
	return dummy_input







class 			lowerCAmelCase_			( _lowerCAmelCase      ):



						'''simple docstring'''


						_lowerCamelCase:      Any =      ("_optimized", "_default")


						def __init__(	self       : Optional[int]					,A_       : Optional[Any]					,A_       : Any )       ->				Optional[Any]:
							A  				=				optimized
							A  				=				default


						def __call__(	self       : Tuple					,*A_       : Optional[int]					,**A_       : str )       ->				List[str]:
							if kwargs["past_key_values"] is None and self._default.config.use_cache:
								A  				=				generate_past_key_values(self._default					,kwargs['input_ids'].shape[0]					,0 )
							kwargs.pop('position_ids'					,_lowerCamelCase )
							for k in list(kwargs.keys() ):
								if kwargs[k] is None or isinstance(kwargs[k]					,_lowerCamelCase ):
									kwargs.pop(_lowerCamelCase )
							A  				=				self._optimized(**_lowerCamelCase )
							A  				=				outputs[0]
							A  				=				outputs[1]
							A  				=				CausalLMOutputWithPast(
							    loss=_lowerCamelCase					,logits=_lowerCamelCase					,past_key_values=_lowerCamelCase					,hidden_states=_lowerCamelCase					,attentions=_lowerCamelCase					,)
							return fixed_output


						def __getattr__(	self       : str					,A_       : int )       ->				Union[str, Any]:
							return getattr(self._default					,_lowerCamelCase )


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : Union[str, Any]					,A_       : Dict=None					,A_       : List[Any]=None					,A_       : List[Any]=None					,**A_       : Tuple )       ->				List[str]:
							return self._default.prepare_inputs_for_generation(
							    _lowerCamelCase					,past_key_values=_lowerCamelCase					,inputs_embeds=_lowerCamelCase					,use_cache=_lowerCamelCase					,**_lowerCamelCase )




						def   _SCREAMING_SNAKE_CASE (	self       : List[str]					,A_       : Tuple[Tuple[torch.Tensor]]					,A_       : torch.Tensor )       ->				Union[str, Any]:
							return self._default._reorder_cache(_lowerCamelCase					,_lowerCamelCase )






def  _snake_case			(						):
	A  				=				argparse.ArgumentParser()
	parser.add_argument(
	    '--model_type'		, default=__UpperCamelCase		, type=__UpperCamelCase		, required=__UpperCamelCase		, help='Model type selected in the list: ' + ', '.join(MODEL_CLASSES.keys()       )		, )
	parser.add_argument(
	    '--model_name_or_path'		, default=__UpperCamelCase		, type=__UpperCamelCase		, required=__UpperCamelCase		, help='Path to pre-trained model or shortcut name selected in the list: ' + ', '.join(MODEL_CLASSES.keys()       )		, )

	parser.add_argument('--prompt'		, type=__UpperCamelCase		, default=''       )
	parser.add_argument('--length'		, type=__UpperCamelCase		, default=20       )
	parser.add_argument('--stop_token'		, type=__UpperCamelCase		, default=__UpperCamelCase		, help='Token at which text generation is stopped'       )

	parser.add_argument(
	    '--temperature'		, type=__UpperCamelCase		, default=1.0		, help='temperature of 1.0 has no effect, lower tend toward greedy sampling'		, )
	parser.add_argument(
	    '--repetition_penalty'		, type=__UpperCamelCase		, default=1.0		, help='primarily useful for CTRL model; in that case, use 1.2'       )
	parser.add_argument('--k'		, type=__UpperCamelCase		, default=0       )
	parser.add_argument('--p'		, type=__UpperCamelCase		, default=0.9       )

	parser.add_argument('--prefix'		, type=__UpperCamelCase		, default=''		, help='Text added prior to input.'       )
	parser.add_argument('--padding_text'		, type=__UpperCamelCase		, default=''		, help='Deprecated, the use of `--prefix` is preferred.'       )
	parser.add_argument('--xlm_language'		, type=__UpperCamelCase		, default=''		, help='Optional language when used with the XLM model.'       )

	parser.add_argument('--seed'		, type=__UpperCamelCase		, default=42		, help='random seed for initialization'       )
	parser.add_argument('--no_cuda'		, action='store_true'		, help='Avoid using CUDA when available'       )
	parser.add_argument('--num_return_sequences'		, type=__UpperCamelCase		, default=1		, help='The number of samples to generate.'       )
	parser.add_argument(
	    '--fp16'		, action='store_true'		, help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'		, )
	parser.add_argument('--jit'		, action='store_true'		, help='Whether or not to use jit trace to accelerate inference'       )
	A  				=				parser.parse_args()

	A  				=				torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'       )
	A  				=				0 if args.no_cuda else torch.cuda.device_count()

	logger.warning(F'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'       )

	set_seed(__UpperCamelCase       )

	# Initialize the model and tokenizer
	try:
		A  				=				args.model_type.lower()
		A				, A  				=				MODEL_CLASSES[args.model_type]
	except KeyError:
		raise KeyError('the model {} you specified is not supported. You are welcome to add it and open a PR :)'       )

	A  				=				tokenizer_class.from_pretrained(args.model_name_or_path       )
	if tokenizer.pad_token is None:
		A  				=				tokenizer.eos_token
	A  				=				model_class.from_pretrained(args.model_name_or_path       )
	model.to(args.device       )

	if args.fpaa:
		model.half()
	A  				=				getattr(model.config		, 'max_position_embeddings'		, 0       )
	A  				=				adjust_length_to_model(args.length		, max_sequence_length=__UpperCamelCase       )
	logger.info(__UpperCamelCase       )

	A  				=				args.prompt if args.prompt else input('Model prompt >>> '       )

	# Different models need different input formatting and/or extra arguments
	A  				=				args.model_type in PREPROCESSING_FUNCTIONS.keys()
	if requires_preprocessing:
		A  				=				PREPROCESSING_FUNCTIONS.get(args.model_type       )
		A  				=				prepare_input(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase       )

		if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
			A  				=				{'add_space_before_punct_symbol': True}
		else:
			A  				=				{}

		A  				=				tokenizer.encode(
		    __UpperCamelCase		, add_special_tokens=__UpperCamelCase		, return_tensors='pt'		, **__UpperCamelCase       )
	else:
		A  				=				args.prefix if args.prefix else args.padding_text
		A  				=				tokenizer.encode(prefix + prompt_text		, add_special_tokens=__UpperCamelCase		, return_tensors='pt'       )
	A  				=				encoded_prompt.to(args.device       )

	if encoded_prompt.size()[-1] == 0:
		A  				=				None
	else:
		A  				=				encoded_prompt

	if args.jit:
		A  				=				['enable jit']
		A  				=				prepare_jit_inputs(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase       )
		torch._C._jit_set_texpr_fuser_enabled(__UpperCamelCase       )
		A  				=				False
		if hasattr(__UpperCamelCase		, 'forward'       ):
			A  				=				inspect.signature(model.forward       )
		else:
			A  				=				inspect.signature(model.__call__       )
		A  				=				tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(__UpperCamelCase		, __UpperCamelCase       ) is not None       )
		A  				=				torch.jit.trace(__UpperCamelCase		, __UpperCamelCase		, strict=__UpperCamelCase       )
		A  				=				torch.jit.freeze(traced_model.eval()       )
		traced_model(*__UpperCamelCase       )
		traced_model(*__UpperCamelCase       )

		A  				=				_ModelFallbackWrapper(__UpperCamelCase		, __UpperCamelCase       )

	A  				=				model.generate(
	    input_ids=__UpperCamelCase		, max_length=args.length + len(encoded_prompt[0]       )		, temperature=args.temperature		, top_k=args.k		, top_p=args.p		, repetition_penalty=args.repetition_penalty		, do_sample=__UpperCamelCase		, num_return_sequences=args.num_return_sequences		, )

	# Remove the batch dimension when returning multiple sequences
	if len(output_sequences.shape       ) > 2:
		output_sequences.squeeze_()

	A  				=				[]

	for generated_sequence_idx, generated_sequence in enumerate(__UpperCamelCase       ):
		print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='       )
		A  				=				generated_sequence.tolist()

		# Decode text
		A  				=				tokenizer.decode(__UpperCamelCase		, clean_up_tokenization_spaces=__UpperCamelCase       )

		# Remove all text after the stop token
		A  				=				text[: text.find(args.stop_token       ) if args.stop_token else None]

		# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
		A  				=				(
		    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]		, clean_up_tokenization_spaces=__UpperCamelCase       )       ) :]
		)

		generated_sequences.append(__UpperCamelCase       )
		print(__UpperCamelCase       )

	return generated_sequences


if __name__ == "__main__":
		main()

"""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


_lowercase											=			random.Random()






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       Union[str, Any]=1.0		, snake_case__				:       int=None		, snake_case__				:       Dict=None       ):
	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 			lowerCAmelCase_			( unittest.TestCase      ):



						'''simple docstring'''


						def __init__(	self       : int					,A_       : str					,A_       : List[str]=7					,A_       : Tuple=400					,A_       : Union[str, Any]=2000					,A_       : Optional[Any]=1					,A_       : Optional[int]=0.0					,A_       : str=2_4000					,A_       : Optional[int]=True					,)       ->				Dict:
							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       : Optional[Any] )       ->				Optional[int]:
							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       : Dict					,A_       : List[str]=False					,A_       : str=False )       ->				Optional[Any]:
							def _flatten(A_       : int ):
								return list(itertools.chain(*A_ ) )

							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(A_ ) for x in audio_inputs]

							return audio_inputs







@require_torch
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      Union[str, Any] =      EncodecFeatureExtractor


						def   _SCREAMING_SNAKE_CASE (	self       : List[str] )       ->				List[Any]:
							A  				=				EnCodecFeatureExtractionTester(self )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				str:
							# 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(800					,1400					,200 )]
							A  				=				[np.asarray(A_ ) 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(A_					,A_					,atol=1e-3 ) )

							# Test batched
							A  				=				feat_extract(A_					,padding=A_					,return_tensors='np' ).input_values
							A  				=				feat_extract(A_					,padding=A_					,return_tensors='np' ).input_values
							for enc_seq_a, enc_seq_a in zip(A_					,A_ ):
								self.assertTrue(np.allclose(A_					,A_					,atol=1e-3 ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Dict:
							A  				=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
							A  				=				np.random.rand(100 ).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       : Optional[int]					,A_       : int )       ->				List[Any]:
							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(A_ ) )[:num_samples]['audio']

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


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Optional[int]:
							# fmt: off
							A  				=				torch.tensor(
							    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
							     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
							     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
							     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
							     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
							     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03] )
							# fmt: on
							A  				=				self._load_datasamples(1 )
							A  				=				EncodecFeatureExtractor()
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(input_values.shape					,(1, 1, 9_3680) )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]					,A_					,atol=1e-6 ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				List[Any]:
							# fmt: off
							A  				=				torch.tensor(
							    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
							     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
							     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
							     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
							     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
							     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-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(A_					,return_tensors='pt' ).input_values
							self.assertEquals(input_values.shape					,(1, 2, 9_3680) )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]					,A_					,atol=1e-6 ) )
							self.assertTrue(torch.allclose(input_values[0, 1, :30]					,EXPECTED_INPUT_VALUES * 0.5					,atol=1e-6 ) )




						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				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=0.01 )

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

							# truncate to chunk
							A  				=				feature_extractor(A_					,truncation=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 7_1520) )  # 2 chunks

							# force truncate to max_length
							A  				=				feature_extractor(
							    A_					,truncation=A_					,max_length=4_8000					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 4_8000) )

							# pad to chunk
							A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(padded_outputs.shape					,(2, 1, 9_5280) )

							# pad to chunk
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 9_5280) )

							# force pad to max length
							A  				=				feature_extractor(
							    A_					,padding='max_length'					,max_length=10_0000					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 10_0000) )

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

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )

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

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )

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

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )