






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						(						__lowerCamelCase=""       ) ->			str:

     '''simple docstring'''
     UpperCAmelCase__      :  Any				      =			tempfile.mkdtemp()
     return os.path.join(__lowerCamelCase ,							str(uuid.uuida()       ) + suffix       )







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




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Any				      =			torch.rand(12	,						dtype=torch.floataa		) - 0.5
          UpperCAmelCase__      :  Any				      =			AgentAudio(_lowerCAmelCase		)
          UpperCAmelCase__      :  str				      =			str(agent_type.to_string()		)

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

          del agent_type

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

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




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Tuple				      =			torch.rand(12	,						dtype=torch.floataa		) - 0.5
          UpperCAmelCase__      :  Optional[Any]				      =			get_new_path(suffix=""".wav"""		)
          sf.write(_lowerCAmelCase	,						_lowerCAmelCase	,						16000		)

          UpperCAmelCase__      :  int				      =			AgentAudio(_lowerCAmelCase		)

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







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




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Dict				      =			torch.randint(0	,						256	,						(64, 64, 3)		)
          UpperCAmelCase__      :  Any				      =			AgentImage(_lowerCAmelCase		)
          UpperCAmelCase__      :  str				      =			str(agent_type.to_string()		)

          # Ensure that the tensor and the agent_type's tensor are the same
          self.assertTrue(torch.allclose(_lowerCAmelCase	,						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(_lowerCAmelCase		)		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[int]				      =			Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""
          UpperCAmelCase__      :  List[Any]				      =			Image.open(_lowerCAmelCase		)
          UpperCAmelCase__      :  str				      =			AgentImage(_lowerCAmelCase		)

          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(_lowerCAmelCase		)		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  List[Any]				      =			Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""
          UpperCAmelCase__      :  Union[str, Any]				      =			Image.open(_lowerCAmelCase		)
          UpperCAmelCase__      :  List[str]				      =			AgentImage(_lowerCAmelCase		)

          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(_lowerCAmelCase		)		)







class 				UpperCAmelCase_  (		unittest.TestCase		):




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  int				      =			"""Hey!"""
          UpperCAmelCase__      :  Optional[Any]				      =			AgentText(_lowerCAmelCase		)

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












'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


_snake_case : Optional[int]  				=						logging.get_logger(__name__)

_snake_case : Any  				=						{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class      A       (						_a       ):
							lowercase_							     =							'roformer'







							def __init__(    self			:    str				,     lowerCAmelCase_			:    int=5_00_00				,     lowerCAmelCase_			:    Any=None				,     lowerCAmelCase_			:    int=7_68				,     lowerCAmelCase_			:    Tuple=12				,     lowerCAmelCase_			:    Any=12				,     lowerCAmelCase_			:    List[str]=30_72				,     lowerCAmelCase_			:    Dict="gelu"				,     lowerCAmelCase_			:    Optional[int]=0.1				,     lowerCAmelCase_			:    List[Any]=0.1				,     lowerCAmelCase_			:    int=15_36				,     lowerCAmelCase_			:    Optional[Any]=2				,     lowerCAmelCase_			:    int=0.0_2				,     lowerCAmelCase_			:    Dict=1e-12				,     lowerCAmelCase_			:    Any=0				,     lowerCAmelCase_			:    Optional[Any]=False				,     lowerCAmelCase_			:    Tuple=True				,     **lowerCAmelCase_			:    Optional[int]				,     )     ->				str:


														"""simple docstring"""


														super().__init__(pad_token_id=lowerCAmelCase_				,     **lowerCAmelCase_   )

														_a      	=							vocab_size
														_a      	=							hidden_size if embedding_size is None else embedding_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      	=							rotary_value
														_a      	=							use_cache



class      A       (						_a       ):







							@property
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


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

