






import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
			import torch

if is_datasets_available():
			from datasets import load_dataset

_UpperCAmelCase			:		Dict   				=    random.Random()





def 	lowerCAmelCase_		(lowercase__						:       int     ,					lowercase__						:       Optional[int]=1.0     ,					lowercase__						:       Tuple=None     ,					lowercase__						:       List[str]=None       )		->    Any:


				'''simple docstring'''




				if rng is None:
								lowerCAmelCase__      			=						global_rng

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

				return values
class  lowerCAmelCase_       (				unittest.TestCase					):




							def __init__(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=7 ,							SCREAMING_SNAKE_CASE_     :   List[str]=400 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=2_000 ,							SCREAMING_SNAKE_CASE_     :   List[str]=2_048 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=128 ,							SCREAMING_SNAKE_CASE_     :   Dict=1 ,							SCREAMING_SNAKE_CASE_     :   List[str]=512 ,							SCREAMING_SNAKE_CASE_     :   Any=30 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=44_100 ,							):
											lowerCAmelCase__      			=						parent
											lowerCAmelCase__      			=						batch_size
											lowerCAmelCase__      			=						min_seq_length
											lowerCAmelCase__      			=						max_seq_length
											lowerCAmelCase__      			=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											lowerCAmelCase__      			=						spectrogram_length
											lowerCAmelCase__      			=						feature_size
											lowerCAmelCase__      			=						num_audio_channels
											lowerCAmelCase__      			=						hop_length
											lowerCAmelCase__      			=						chunk_length
											lowerCAmelCase__      			=						sampling_rate




							def 						__snake_case					(   self     :   Any	):
											return {
											    "spectrogram_length": self.spectrogram_length,
											    "feature_size": self.feature_size,
											    "num_audio_channels": self.num_audio_channels,
											    "hop_length": self.hop_length,
											    "chunk_length": self.chunk_length,
											    "sampling_rate": self.sampling_rate,
											}




							def 						__snake_case					(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   List[str]=False ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=False	):
											def _flatten(SCREAMING_SNAKE_CASE_     :   int	):
															return list(itertools.chain(*SCREAMING_SNAKE_CASE_	)	)

											if equal_length:
															lowerCAmelCase__      			=						[floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
											else:
															# make sure that inputs increase in size
															lowerCAmelCase__      			=						[
															    floats_list((x, self.feature_size)	)
															    for x in range(self.min_seq_length ,							self.max_seq_length ,							self.seq_length_diff	)
															]
											if numpify:
															lowerCAmelCase__      			=						[np.asarray(SCREAMING_SNAKE_CASE_	) for x in speech_inputs]
											return speech_inputs



@require_torch
@require_torchaudio
class  lowerCAmelCase_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:str		      =    TvltFeatureExtractor




							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						TvltFeatureExtractionTester(self	)




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						self.feature_extraction_class(**self.feat_extract_dict	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''spectrogram_length'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''feature_size'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''num_audio_channels'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''hop_length'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''chunk_length'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''sampling_rate'''	)	)




							def 						__snake_case					(   self     :   Dict	):
											lowerCAmelCase__      			=						self.feature_extraction_class(**self.feat_extract_dict	)

											with tempfile.TemporaryDirectory() as tmpdirname:
															lowerCAmelCase__      			=						feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE_	)[0]
															check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_	)
															lowerCAmelCase__      			=						self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						feat_extract_first.to_dict()
											lowerCAmelCase__      			=						feat_extract_second.to_dict()
											lowerCAmelCase__      			=						dict_first.pop('''mel_filters'''	)
											lowerCAmelCase__      			=						dict_second.pop('''mel_filters'''	)
											self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)	)
											self.assertEqual(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						self.feature_extraction_class(**self.feat_extract_dict	)

											with tempfile.TemporaryDirectory() as tmpdirname:
															lowerCAmelCase__      			=						os.path.join(SCREAMING_SNAKE_CASE_ ,							'''feat_extract.json'''	)
															feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE_	)
															lowerCAmelCase__      			=						self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						feat_extract_first.to_dict()
											lowerCAmelCase__      			=						feat_extract_second.to_dict()
											lowerCAmelCase__      			=						dict_first.pop('''mel_filters'''	)
											lowerCAmelCase__      			=						dict_second.pop('''mel_filters'''	)
											self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)	)
											self.assertEqual(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[str]	):
											# Initialize feature_extractor
											lowerCAmelCase__      			=						self.feature_extraction_class(**self.feat_extract_dict	)

											# create three inputs of length 800, 1000, and 1200
											lowerCAmelCase__      			=						[floats_list((1, x)	)[0] for x in range(800 ,							1_400 ,							200	)]
											lowerCAmelCase__      			=						[np.asarray(SCREAMING_SNAKE_CASE_	) for speech_input in speech_inputs]

											# Test not batched input
											lowerCAmelCase__      			=						feature_extractor(np_speech_inputs[0] ,							return_tensors='''np''' ,							sampling_rate=44_100	).audio_values

											self.assertTrue(encoded_audios.ndim == 4	)
											self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
											self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
											self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

											# Test batched
											lowerCAmelCase__      			=						feature_extractor(SCREAMING_SNAKE_CASE_ ,							return_tensors='''np''' ,							sampling_rate=44_100	).audio_values

											self.assertTrue(encoded_audios.ndim == 4	)
											self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
											self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
											self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

											# Test audio masking
											lowerCAmelCase__      			=						feature_extractor(
											    SCREAMING_SNAKE_CASE_ ,							return_tensors='''np''' ,							sampling_rate=44_100 ,							mask_audio=SCREAMING_SNAKE_CASE_	).audio_values

											self.assertTrue(encoded_audios.ndim == 4	)
											self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
											self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
											self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

											# Test 2-D numpy arrays are batched.
											lowerCAmelCase__      			=						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
											lowerCAmelCase__      			=						np.asarray(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						feature_extractor(SCREAMING_SNAKE_CASE_ ,							return_tensors='''np''' ,							sampling_rate=44_100	).audio_values
											self.assertTrue(encoded_audios.ndim == 4	)
											self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
											self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
											self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)




							def 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Dict	):
											lowerCAmelCase__      			=						load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,							'''clean''' ,							split='''validation'''	)
											# automatic decoding with librispeech
											lowerCAmelCase__      			=						ds.sort('''id'''	).select(range(SCREAMING_SNAKE_CASE_	)	)[:num_samples]['''audio''']

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




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						self._load_datasamples(1	)
											lowerCAmelCase__      			=						TvltFeatureExtractor()
											lowerCAmelCase__      			=						feature_extractor(SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt'''	).audio_values

											self.assertEquals(audio_values.shape ,							(1, 1, 192, 128)	)

											lowerCAmelCase__      			=						torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]	)
											self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)













from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase			:		Tuple   				=    logging.get_logger(__name__)


_UpperCAmelCase			:		int   				=    {
    "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
    "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
    "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
    "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
    "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
    "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
    "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
    "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
    "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
    "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
    "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
    "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:int		      =    'codegen'
							UpperCamelCase_		:int		      =    {
							    'max_position_embeddings': 'n_positions',
							    'hidden_size': 'n_embd',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}




							def __init__(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   str=50_400 ,							SCREAMING_SNAKE_CASE_     :   str=2_048 ,							SCREAMING_SNAKE_CASE_     :   int=2_048 ,							SCREAMING_SNAKE_CASE_     :   Any=4_096 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=28 ,							SCREAMING_SNAKE_CASE_     :   str=16 ,							SCREAMING_SNAKE_CASE_     :   str=64 ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   Dict="gelu_new" ,							SCREAMING_SNAKE_CASE_     :   str=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=1e-5 ,							SCREAMING_SNAKE_CASE_     :   Any=0.02 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=True ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=50_256 ,							SCREAMING_SNAKE_CASE_     :   Any=50_256 ,							SCREAMING_SNAKE_CASE_     :   List[str]=False ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							):
											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						n_ctx
											lowerCAmelCase__      			=						n_positions
											lowerCAmelCase__      			=						n_embd
											lowerCAmelCase__      			=						n_layer
											lowerCAmelCase__      			=						n_head
											lowerCAmelCase__      			=						n_inner
											lowerCAmelCase__      			=						rotary_dim
											lowerCAmelCase__      			=						activation_function
											lowerCAmelCase__      			=						resid_pdrop
											lowerCAmelCase__      			=						embd_pdrop
											lowerCAmelCase__      			=						attn_pdrop
											lowerCAmelCase__      			=						layer_norm_epsilon
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						use_cache

											lowerCAmelCase__      			=						bos_token_id
											lowerCAmelCase__      			=						eos_token_id

											super().__init__(
											    bos_token_id=SCREAMING_SNAKE_CASE_ ,							eos_token_id=SCREAMING_SNAKE_CASE_ ,							tie_word_embeddings=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)



class  lowerCAmelCase_       (				snake_case__					):




							def __init__(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   PretrainedConfig ,							SCREAMING_SNAKE_CASE_     :   str = "default" ,							SCREAMING_SNAKE_CASE_     :   List[PatchingSpec] = None ,							SCREAMING_SNAKE_CASE_     :   bool = False ,							):
											super().__init__(SCREAMING_SNAKE_CASE_ ,							task=SCREAMING_SNAKE_CASE_ ,							patching_specs=SCREAMING_SNAKE_CASE_ ,							use_past=SCREAMING_SNAKE_CASE_	)
											if not getattr(self._config ,							'''pad_token_id''' ,							SCREAMING_SNAKE_CASE_	):
															# TODO: how to do that better?
															lowerCAmelCase__      			=						0




							@property
							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
											if self.use_past:
															self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ ,							direction='''inputs'''	)
															lowerCAmelCase__      			=						{0: '''batch''', 1: '''past_sequence + sequence'''}
											else:
															lowerCAmelCase__      			=						{0: '''batch''', 1: '''sequence'''}

											return common_inputs




							@property
							def 						__snake_case					(   self     :   Dict	):
											return self._config.n_layer




							@property
							def 						__snake_case					(   self     :   Union[str, Any]	):
											return self._config.n_head




							def 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   PreTrainedTokenizer ,							SCREAMING_SNAKE_CASE_     :   int = -1 ,							SCREAMING_SNAKE_CASE_     :   int = -1 ,							SCREAMING_SNAKE_CASE_     :   bool = False ,							SCREAMING_SNAKE_CASE_     :   Optional[TensorType] = None ,							):
											lowerCAmelCase__      			=						super(SCREAMING_SNAKE_CASE_ ,							self	).generate_dummy_inputs(
											    SCREAMING_SNAKE_CASE_ ,							batch_size=SCREAMING_SNAKE_CASE_ ,							seq_length=SCREAMING_SNAKE_CASE_ ,							is_pair=SCREAMING_SNAKE_CASE_ ,							framework=SCREAMING_SNAKE_CASE_	)

											# We need to order the input in the way they appears in the forward()
											lowerCAmelCase__      			=						OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

											# Need to add the past_keys
											if self.use_past:
															if not is_torch_available():
																			raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
															else:
																			import torch

																			lowerCAmelCase__			,		lowerCAmelCase__      			=						common_inputs['''input_ids'''].shape
																			# Not using the same length for past_key_values
																			lowerCAmelCase__      			=						seqlen + 2
																			lowerCAmelCase__      			=						(
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			lowerCAmelCase__      			=						[
																			    (torch.zeros(SCREAMING_SNAKE_CASE_	), torch.zeros(SCREAMING_SNAKE_CASE_	)) for _ in range(self.num_layers	)
																			]

											lowerCAmelCase__      			=						common_inputs['''attention_mask''']
											if self.use_past:
															lowerCAmelCase__      			=						ordered_inputs['''attention_mask'''].dtype
															lowerCAmelCase__      			=						torch.cat(
															    [ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							dtype=SCREAMING_SNAKE_CASE_	)] ,							dim=1	)

											return ordered_inputs




							@property
							def 						__snake_case					(   self     :   Optional[int]	):
											return 13





