





import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


snake_case 				=       random.Random()

if is_torch_available():
      import torch

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Union[str, Any]			,							snake_case__  :Any=1.0			,							snake_case__  :Union[str, Any]=None			,							snake_case__  :Union[str, Any]=None     )      ->						List[Any]:
  if rng is None:
    _lowercase							= global_rng

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

  return values





class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def __init__(    self	:	List[str]						,__A	:	Union[str, Any]						,__A	:	Dict=7						,__A	:	Any=400						,__A	:	Union[str, Any]=2000						,__A	:	List[Any]=1						,__A	:	Tuple=0.0						,__A	:	str=1_6000						,__A	:	List[Any]=True						,__A	:	Optional[Any]=True						,)						->					List[str]:
         _lowercase							= parent
         _lowercase							= batch_size
         _lowercase							= min_seq_length
         _lowercase							= max_seq_length
         _lowercase							= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         _lowercase							= feature_size
         _lowercase							= padding_value
         _lowercase							= sampling_rate
         _lowercase							= return_attention_mask
         _lowercase							= do_normalize







       def    __UpperCAmelCase (    self	:	int  )						->					Union[str, Any]:
         return {
             "feature_size": self.feature_size,
             "padding_value": self.padding_value,
             "sampling_rate": self.sampling_rate,
             "return_attention_mask": self.return_attention_mask,
             "do_normalize": self.do_normalize,
         }







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	Union[str, Any]=False						,__A	:	Union[str, Any]=False  )						->					Optional[int]:
         def _flatten(__A	:	Any  ):
           return list(itertools.chain(*__A  )  )

         if equal_length:
           _lowercase							= floats_list((self.batch_size, self.max_seq_length)  )
         else:
           # make sure that inputs increase in size
           _lowercase							= [
               _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:
           _lowercase							= [np.asarray(__A  ) for x in speech_inputs]

         return speech_inputs





@require_torch
@require_torchaudio
class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : str      =  ASTFeatureExtractor







       def    __UpperCAmelCase (    self	:	Dict  )						->					int:
         _lowercase							= ASTFeatureExtractionTester(self  )







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Any:
         # Tests that all call wrap to encode_plus and batch_encode_plus
         _lowercase							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
         # create three inputs of length 800, 1000, and 1200
         _lowercase							= [floats_list((1, x)  )[0] for x in range(800						,1400						,200  )]
         _lowercase							= [np.asarray(__A  ) for speech_input in speech_inputs]

         # Test not batched input
         _lowercase							= feat_extract(speech_inputs[0]						,return_tensors='np'  ).input_values
         _lowercase							= feat_extract(np_speech_inputs[0]						,return_tensors='np'  ).input_values
         self.assertTrue(np.allclose(__A						,__A						,atol=1e-3  )  )

         # Test batched
         _lowercase							= feat_extract(__A						,padding=__A						,return_tensors='np'  ).input_values
         _lowercase							= 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  )  )

         # Test 2-D numpy arrays are batched.
         _lowercase							= [floats_list((1, x)  )[0] for x in (800, 800, 800)]
         _lowercase							= np.asarray(__A  )
         _lowercase							= feat_extract(__A						,return_tensors='np'  ).input_values
         _lowercase							= feat_extract(__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  )  )







       @require_torch
       def    __UpperCAmelCase (    self	:	Tuple  )						->					List[str]:
         import torch

         _lowercase							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
         _lowercase							= np.random.rand(100  ).astype(np.floataa  )
         _lowercase							= np_speech_inputs.tolist()

         for inputs in [py_speech_inputs, np_speech_inputs]:
           _lowercase							= feature_extractor.pad([{'input_values': inputs}]						,return_tensors='np'  )
           self.assertTrue(np_processed.input_values.dtype == np.floataa  )
           _lowercase							= feature_extractor.pad([{'input_values': inputs}]						,return_tensors='pt'  )
           self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	Dict  )						->					Optional[int]:
         from datasets import load_dataset

         _lowercase							= load_dataset('hf-internal-testing/librispeech_asr_dummy'						,'clean'						,split='validation'  )
         # automatic decoding with librispeech
         _lowercase							= ds.sort('id'  ).select(range(__A  )  )[:num_samples]['audio']

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







       @require_torch
       def    __UpperCAmelCase (    self	:	str  )						->					Dict:
         # fmt: off
         _lowercase							= torch.tensor(
             [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
              -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
              -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
              -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]  )
         # fmt: on

         _lowercase							= self._load_datasamples(1  )
         _lowercase							= ASTFeatureExtractor()
         _lowercase							= feature_extractor(__A						,return_tensors='pt'  ).input_values
         self.assertEquals(input_values.shape						,(1, 1024, 128)  )
         self.assertTrue(torch.allclose(input_values[0, 0, :30]						,__A						,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


snake_case 				=       logging.get_logger(__name__)

snake_case 				=       {
    """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 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : str      =  '''gptj'''
       SCREAMING_SNAKE_CASE_      : int      =  {
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }







       def __init__(    self	:	str						,__A	:	List[str]=5_0400						,__A	:	Dict=2048						,__A	:	List[str]=4096						,__A	:	Dict=28						,__A	:	Optional[int]=16						,__A	:	Tuple=64						,__A	:	Optional[int]=None						,__A	:	Optional[int]="gelu_new"						,__A	:	Dict=0.0						,__A	:	List[str]=0.0						,__A	:	Optional[int]=0.0						,__A	:	str=1e-5						,__A	:	List[Any]=0.02						,__A	:	str=True						,__A	:	int=5_0256						,__A	:	Union[str, Any]=5_0256						,__A	:	int=False						,**__A	:	List[Any]						,)						->					str:
         _lowercase							= vocab_size
         _lowercase							= n_positions
         _lowercase							= n_embd
         _lowercase							= n_layer
         _lowercase							= n_head
         _lowercase							= n_inner
         _lowercase							= rotary_dim
         _lowercase							= activation_function
         _lowercase							= resid_pdrop
         _lowercase							= embd_pdrop
         _lowercase							= attn_pdrop
         _lowercase							= layer_norm_epsilon
         _lowercase							= initializer_range
         _lowercase							= use_cache

         _lowercase							= bos_token_id
         _lowercase							= eos_token_id

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





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





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







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Mapping[str, Mapping[int, str]]:
         _lowercase							= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
         if self.use_past:
           self.fill_with_past_key_values_(__A						,direction='inputs'  )
           _lowercase							= {0: 'batch', 1: 'past_sequence + sequence'}
         else:
           _lowercase							= {0: 'batch', 1: 'sequence'}

         return common_inputs







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					int:
         return self._config.n_layer







       @property
       def    __UpperCAmelCase (    self	:	Tuple  )						->					int:
         return self._config.n_head







       def    __UpperCAmelCase (    self	:	Any						,__A	:	PreTrainedTokenizer						,__A	:	int = -1						,__A	:	int = -1						,__A	:	bool = False						,__A	:	Optional[TensorType] = None						,)						->					Mapping[str, Any]:
         _lowercase							= super(__A						,self  ).generate_dummy_inputs(
             __A						,batch_size=__A						,seq_length=__A						,is_pair=__A						,framework=__A  )

         # We need to order the input in the way they appears in the forward()
         _lowercase							= 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

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

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

         return ordered_inputs







       @property
       def    __UpperCAmelCase (    self	:	str  )						->					int:
         return 13