





import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf

if is_vision_available():
      from PIL import Image

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[int]=""     )      ->						Union[str, Any]:
  _lowercase							= tempfile.mkdtemp()
  return os.path.join(_lowerCamelCase			,							str(uuid.uuida()     ) + suffix     )





@require_soundfile
@require_torch
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Tuple  )						->					Union[str, Any]:
         _lowercase							= torch.rand(12						,dtype=torch.floataa  ) - 0.5
         _lowercase							= AgentAudio(__A  )
         _lowercase							= str(agent_type.to_string()  )

         # Ensure that the tensor and the agent_type's tensor are the same
         self.assertTrue(torch.allclose(__A						,agent_type.to_raw()						,atol=1e-4  )  )

         del agent_type

         # Ensure the path remains even after the object deletion
         self.assertTrue(os.path.exists(__A  )  )

         # Ensure that the file contains the same value as the original tensor
         _lowercase							= sf.read(__A  )
         self.assertTrue(torch.allclose(__A						,torch.tensor(__A  )						,atol=1e-4  )  )







       def    __UpperCAmelCase (    self	:	List[str]  )						->					str:
         _lowercase							= torch.rand(12						,dtype=torch.floataa  ) - 0.5
         _lowercase							= get_new_path(suffix='.wav'  )
         sf.write(__A						,__A						,1_6000  )

         _lowercase							= AgentAudio(__A  )

         self.assertTrue(torch.allclose(__A						,agent_type.to_raw()						,atol=1e-4  )  )
         self.assertEqual(agent_type.to_string()						,__A  )





@require_vision
@require_torch
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Any  )						->					Tuple:
         _lowercase							= torch.randint(0						,256						,(64, 64, 3)  )
         _lowercase							= AgentImage(__A  )
         _lowercase							= str(agent_type.to_string()  )

         # Ensure that the tensor and the agent_type's tensor are the same
         self.assertTrue(torch.allclose(__A						,agent_type._tensor						,atol=1e-4  )  )

         self.assertIsInstance(agent_type.to_raw()						,Image.Image  )

         # Ensure the path remains even after the object deletion
         del agent_type
         self.assertTrue(os.path.exists(__A  )  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Union[str, Any]:
         _lowercase							= Path(get_tests_dir('fixtures/tests_samples/COCO'  )  ) / "000000039769.png"
         _lowercase							= Image.open(__A  )
         _lowercase							= AgentImage(__A  )

         self.assertTrue(path.samefile(agent_type.to_string()  )  )
         self.assertTrue(image == agent_type.to_raw()  )

         # Ensure the path remains even after the object deletion
         del agent_type
         self.assertTrue(os.path.exists(__A  )  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Union[str, Any]:
         _lowercase							= Path(get_tests_dir('fixtures/tests_samples/COCO'  )  ) / "000000039769.png"
         _lowercase							= Image.open(__A  )
         _lowercase							= AgentImage(__A  )

         self.assertFalse(path.samefile(agent_type.to_string()  )  )
         self.assertTrue(image == agent_type.to_raw()  )

         # Ensure the path remains even after the object deletion
         del agent_type
         self.assertTrue(os.path.exists(__A  )  )





class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	List[str]  )						->					int:
         _lowercase							= "Hey!"
         _lowercase							= AgentText(__A  )

         self.assertEqual(__A						,agent_type.to_string()  )
         self.assertEqual(__A						,agent_type.to_raw()  )
         self.assertEqual(__A						,__A  )






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