



'''simple docstring'''



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


lowerCAmelCase_     :       Optional[Any]								=						logging.get_logger(__name__)

lowerCAmelCase_     :       Union[str, Any]								=						{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   Tuple	     =				'gptj'
						_lowerCAmelCase     :   Union[str, Any]	     =				{
						    'max_position_embeddings': 'n_positions',
						    'hidden_size': 'n_embd',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}







						def __init__(  self   :      Optional[Any]				,    lowerCAmelCase__   :      Union[str, Any]=5_04_00				,    lowerCAmelCase__   :      List[Any]=20_48				,    lowerCAmelCase__   :      Union[str, Any]=40_96				,    lowerCAmelCase__   :      Optional[int]=28				,    lowerCAmelCase__   :      str=16				,    lowerCAmelCase__   :      List[Any]=64				,    lowerCAmelCase__   :      Tuple=None				,    lowerCAmelCase__   :      List[Any]="gelu_new"				,    lowerCAmelCase__   :      List[Any]=0.0				,    lowerCAmelCase__   :      List[Any]=0.0				,    lowerCAmelCase__   :      str=0.0				,    lowerCAmelCase__   :      str=1e-5				,    lowerCAmelCase__   :      List[Any]=0.02				,    lowerCAmelCase__   :      List[Any]=True				,    lowerCAmelCase__   :      Optional[int]=5_02_56				,    lowerCAmelCase__   :      Any=5_02_56				,    lowerCAmelCase__   :      str=False				,    **lowerCAmelCase__   :      Tuple				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			vocab_size
													SCREAMING_SNAKE_CASE					:      Tuple     					=			n_positions
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			n_embd
													SCREAMING_SNAKE_CASE					:      Dict     					=			n_layer
													SCREAMING_SNAKE_CASE					:      int     					=			n_head
													SCREAMING_SNAKE_CASE					:      Tuple     					=			n_inner
													SCREAMING_SNAKE_CASE					:      str     					=			rotary_dim
													SCREAMING_SNAKE_CASE					:      Tuple     					=			activation_function
													SCREAMING_SNAKE_CASE					:      int     					=			resid_pdrop
													SCREAMING_SNAKE_CASE					:      List[str]     					=			embd_pdrop
													SCREAMING_SNAKE_CASE					:      Dict     					=			attn_pdrop
													SCREAMING_SNAKE_CASE					:      Any     					=			layer_norm_epsilon
													SCREAMING_SNAKE_CASE					:      Dict     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      Any     					=			use_cache

													SCREAMING_SNAKE_CASE					:      Tuple     					=			bos_token_id
													SCREAMING_SNAKE_CASE					:      int     					=			eos_token_id

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


class 	lowerCamelCase_							(					snake_case_			):







						def __init__(  self   :      Tuple				,    lowerCAmelCase__   :      PretrainedConfig				,    lowerCAmelCase__   :      str = "default"				,    lowerCAmelCase__   :      List[PatchingSpec] = None				,    lowerCAmelCase__   :      bool = False				,    ):

													"""simple docstring"""
													super().__init__(lowerCAmelCase__				,    task=lowerCAmelCase__				,    patching_specs=lowerCAmelCase__				,    use_past=lowerCAmelCase__				)
													if not getattr(self._config				,    '''pad_token_id'''				,    lowerCAmelCase__				):
																				# TODO: how to do that better?
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			0







						@property
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}				)
													if self.use_past:
																				self.fill_with_past_key_values_(lowerCAmelCase__				,    direction='''inputs'''				)
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			{0: '''batch''', 1: '''past_sequence + sequence'''}
													else:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			{0: '''batch''', 1: '''sequence'''}

													return common_inputs







						@property
						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													return self._config.n_layer







						@property
						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													return self._config.n_head







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      PreTrainedTokenizer				,    lowerCAmelCase__   :      int = -1				,    lowerCAmelCase__   :      int = -1				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      Optional[TensorType] = None				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			super(lowerCAmelCase__				,    self				).generate_dummy_inputs(
													    lowerCAmelCase__				,    batch_size=lowerCAmelCase__				,    seq_length=lowerCAmelCase__				,    is_pair=lowerCAmelCase__				,    framework=lowerCAmelCase__				)

													# We need to order the input in the way they appears in the forward()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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

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

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

													return ordered_inputs







						@property
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													return 13









'''simple docstring'''



import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class 	lowerCamelCase_							(					unittest.TestCase			):







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			'''laion/clap-htsat-unfused'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			tempfile.mkdtemp()







						def 					__lowercase    (  self   :      Tuple				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return RobertaTokenizer.from_pretrained(self.checkpoint				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      int				,    **lowerCAmelCase__   :      str				):

													"""simple docstring"""
													return ClapFeatureExtractor.from_pretrained(self.checkpoint				,    **lowerCAmelCase__				)







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

													"""simple docstring"""
													shutil.rmtree(self.tmpdirname				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_feature_extractor()

													SCREAMING_SNAKE_CASE					:      List[str]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													processor.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ClapProcessor.from_pretrained(self.tmpdirname				)

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

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







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			ClapProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()				)
													processor.save_pretrained(self.tmpdirname				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer(bos_token='''(BOS)'''				,    eos_token='''(EOS)'''				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_feature_extractor(do_normalize=lowerCAmelCase__				,    padding_value=1.0				)

													SCREAMING_SNAKE_CASE					:      str     					=			ClapProcessor.from_pretrained(
													    self.tmpdirname				,    bos_token='''(BOS)'''				,    eos_token='''(EOS)'''				,    do_normalize=lowerCAmelCase__				,    padding_value=1.0				)

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

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







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			floats_list((3, 10_00)				)

													SCREAMING_SNAKE_CASE					:      Any     					=			feature_extractor(lowerCAmelCase__				,    return_tensors='''np'''				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			processor(audios=lowerCAmelCase__				,    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 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			'''This is a test string'''

													SCREAMING_SNAKE_CASE					:      int     					=			processor(text=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer(lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Any     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													SCREAMING_SNAKE_CASE					:      Tuple     					=			processor.batch_decode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tokenizer.batch_decode(lowerCAmelCase__				)

													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													self.assertListEqual(
													    processor.model_input_names[2:]				,    feature_extractor.model_input_names				,    msg='''`processor` and `feature_extractor` model input names do not match'''				,    )




