






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   UpperCamelCase  (       snake_case__     :   Tuple=""  ):




     '''simple docstring'''






     __snake_case			:Tuple          =						tempfile.mkdtemp()
     return os.path.join(snake_case__	,str(uuid.uuida()  ) + suffix  )




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

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:str          =						torch.rand(12			,						dtype=torch.floataa    ) - 0.5
            __snake_case			:Dict          =						AgentAudio(a__    )
            __snake_case			:Union[str, Any]          =						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
            __snake_case       , __snake_case			:Tuple          =						sf.read(a__    )
            self.assertTrue(torch.allclose(a__			,						torch.tensor(a__    )			,						atol=1e-4    )    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:str          =						torch.rand(12			,						dtype=torch.floataa    ) - 0.5
            __snake_case			:List[str]          =						get_new_path(suffix=""".wav"""    )
            sf.write(a__			,						a__			,						1_60_00    )

            __snake_case			:Optional[int]          =						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 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Dict          =						torch.randint(0			,						2_56			,						(64, 64, 3)    )
            __snake_case			:Union[str, Any]          =						AgentImage(a__    )
            __snake_case			:List[str]          =						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 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Any          =						Path(get_tests_dir("""fixtures/tests_samples/COCO"""    )    ) / """000000039769.png"""
            __snake_case			:str          =						Image.open(a__    )
            __snake_case			:List[str]          =						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 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:Dict          =						Path(get_tests_dir("""fixtures/tests_samples/COCO"""    )    ) / """000000039769.png"""
            __snake_case			:Optional[int]          =						Image.open(a__    )
            __snake_case			:int          =						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 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:Dict          =						"""Hey!"""
            __snake_case			:Any          =						AgentText(a__    )

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










import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__=2			,						a__=3			,						a__=64			,						a__=None    ) ->      int:






            '''simple docstring'''





            __snake_case			:Any          =						np.random.default_rng(a__    )
            __snake_case			:List[str]          =						length
            __snake_case			:Optional[Any]          =						rng.normal(size=(length,)    ).astype(np.floataa    )
            __snake_case			:Optional[int]          =						a * self.x + b + rng.normal(scale=0.1			,						size=(length,)    ).astype(np.floataa    )



       def __len__(							self    ) ->      str:






            '''simple docstring'''





            return self.length



       def __getitem__(							self			,						a__    ) ->      int:






            '''simple docstring'''





            return {"x": self.x[i], "y": self.y[i]}




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Any          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:int          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:Dict          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      Optional[Any]:






            '''simple docstring'''





            if self.first_batch:
                 print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
                 __snake_case			:Tuple          =						False
            return x * self.a[0] + self.b[0]




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Optional[int]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:List[str]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:str          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      str:






            '''simple docstring'''





            if self.first_batch:
                 print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
                 __snake_case			:List[Any]          =						False
            return x * self.a + self.b






def   UpperCamelCase  (       snake_case__     :   Optional[int]	,snake_case__     :   int = 16  ):




     '''simple docstring'''






     from datasets import load_dataset
     from transformers import AutoTokenizer

     __snake_case			:Union[str, Any]          =						AutoTokenizer.from_pretrained("""bert-base-cased"""  )
     __snake_case			:Dict          =						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
     __snake_case			:Any          =						load_dataset("""csv"""	,data_files=snake_case__  )
     __snake_case			:Dict          =						datasets["""train"""].unique("""label"""  )

     __snake_case			:List[Any]          =						{v: i for i, v in enumerate(snake_case__  )}

     def tokenize_function(snake_case__     :   List[Any]  ):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case			:Optional[Any]          =						tokenizer(
              examples["""sentence1"""]	,examples["""sentence2"""]	,truncation=snake_case__	,max_length=snake_case__	,padding="""max_length"""  )
          if "label" in examples:
               __snake_case			:Dict          =						[label_to_id[l] for l in examples["""label"""]]
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     __snake_case			:List[str]          =						datasets.map(
         snake_case__	,batched=snake_case__	,remove_columns=["""sentence1""", """sentence2""", """label"""]	,)

     def collate_fn(snake_case__     :   Optional[int]  ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(snake_case__	,padding="""max_length"""	,max_length=128	,return_tensors="""pt"""  )
          return tokenizer.pad(snake_case__	,padding="""longest"""	,return_tensors="""pt"""  )

     # Instantiate dataloaders.
     __snake_case			:List[Any]          =						DataLoader(tokenized_datasets["""train"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=2  )
     __snake_case			:str          =						DataLoader(tokenized_datasets["""validation"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=1  )

     return train_dataloader, eval_dataloader


