



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 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[int]=""						)->	str:
    A__					=       tempfile.mkdtemp()
    return os.path.join(UpperCamelCase_			,							str(uuid.uuida()						) + suffix						)



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




     def 		snake_case_		(					self):
         A__					=       torch.rand(1_2			,    dtype=torch.floataa) - 0.5
         A__					=       AgentAudio(a__)
         A__					=       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
         A__			,						A__					=       sf.read(a__)
         self.assertTrue(torch.allclose(a__			,    torch.tensor(a__)			,    atol=1e-4))




     def 		snake_case_		(					self):
         A__					=       torch.rand(1_2			,    dtype=torch.floataa) - 0.5
         A__					=       get_new_path(suffix='''.wav''')
         sf.write(a__			,    a__			,    1_6_0_0_0)

         A__					=       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      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         A__					=       torch.randint(0			,    2_5_6			,    (6_4, 6_4, 3))
         A__					=       AgentImage(a__)
         A__					=       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 		snake_case_		(					self):
         A__					=       Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png'''
         A__					=       Image.open(a__)
         A__					=       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 		snake_case_		(					self):
         A__					=       Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png'''
         A__					=       Image.open(a__)
         A__					=       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      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         A__					=       '''Hey!'''
         A__					=       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 Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''xlm-roberta'''




     def __init__(					self			,    a__=3_0_5_2_2			,    a__=7_6_8			,    a__=1_2			,    a__=1_2			,    a__=3_0_7_2			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=2			,    a__=0.0_2			,    a__=1e-12			,    a__=1			,    a__=0			,    a__=2			,    a__="absolute"			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(pad_token_id=a__			,    bos_token_id=a__			,    eos_token_id=a__			,    **a__)

         A__					=       vocab_size
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       num_attention_heads
         A__					=       hidden_act
         A__					=       intermediate_size
         A__					=       hidden_dropout_prob
         A__					=       attention_probs_dropout_prob
         A__					=       max_position_embeddings
         A__					=       type_vocab_size
         A__					=       initializer_range
         A__					=       layer_norm_eps
         A__					=       position_embedding_type
         A__					=       use_cache
         A__					=       classifier_dropout







class      _UpperCAmelCase      (							A__  ):




     @property
     def 		snake_case_		(					self):
         if self.task == "multiple-choice":
             A__					=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A__					=       {0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ])
