


from typing import Mapping

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


A							:					Tuple     						=     logging.get_logger(__name__)

A							:					Dict     						=     {
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class     UpperCamelCase(  _UpperCAmelCase   ):
     snake_case_		:				Dict										=	"""umt5"""
     snake_case_		:				Any										=	["""past_key_values"""]


     def __init__(						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Any=2_5_0_1_1_2     ,    SCREAMING_SNAKE_CASE	:		Any=5_1_2     ,    SCREAMING_SNAKE_CASE	:		List[str]=6_4     ,    SCREAMING_SNAKE_CASE	:		Tuple=1_0_2_4     ,    SCREAMING_SNAKE_CASE	:		int=8     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    SCREAMING_SNAKE_CASE	:		Any=6     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=3_2     ,    SCREAMING_SNAKE_CASE	:		str=1_2_8     ,    SCREAMING_SNAKE_CASE	:		List[str]=0.1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1e-6     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1.0     ,    SCREAMING_SNAKE_CASE	:		List[str]="gated-gelu"     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=True     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=True     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]="T5Tokenizer"     ,    SCREAMING_SNAKE_CASE	:		str=True     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=0     ,    SCREAMING_SNAKE_CASE	:		Dict=1     ,    SCREAMING_SNAKE_CASE	:		Tuple=0     ,    **SCREAMING_SNAKE_CASE	:		Any     ,    )			->	Dict:




           '''simple docstring'''

           super().__init__(
               is_encoder_decoder=lowercase__     ,    tokenizer_class=lowercase__     ,    tie_word_embeddings=lowercase__     ,    pad_token_id=lowercase__     ,    eos_token_id=lowercase__     ,    decoder_start_token_id=lowercase__     ,    **lowercase__     ,    )
           __snake_case				=		vocab_size
           __snake_case				=		d_model
           __snake_case				=		d_kv
           __snake_case				=		d_ff
           __snake_case				=		num_layers
           __snake_case				=		(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           __snake_case				=		num_heads
           __snake_case				=		relative_attention_num_buckets
           __snake_case				=		relative_attention_max_distance
           __snake_case				=		dropout_rate
           __snake_case				=		layer_norm_epsilon
           __snake_case				=		initializer_factor
           __snake_case				=		feed_forward_proj
           __snake_case				=		use_cache

           __snake_case				=		self.feed_forward_proj.split("-"						)
           __snake_case				=		act_info[-1]
           __snake_case				=		act_info[0] == """gated"""

           if len(lowercase__						) > 1 and act_info[0] != "gated" or len(lowercase__						) > 2:
                 raise ValueError(
                     f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                     "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                     "'gated-gelu' or 'relu'"						)

           if feed_forward_proj == "gated-gelu":
                 __snake_case				=		"""gelu_new"""


     @property
     def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Union[str, Any]:




           '''simple docstring'''

           return self.d_model


     @property
     def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Union[str, Any]:




           '''simple docstring'''

           return self.num_heads






     @property
     def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	str:




           '''simple docstring'''

           return self.num_layers






class     UpperCamelCase(  _UpperCAmelCase   ):


     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Dict:




           '''simple docstring'''

           __snake_case				=		{
               """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
               """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
           }
           if self.use_past:
                 __snake_case				=		"""past_encoder_sequence + sequence"""
                 __snake_case				=		{0: """batch"""}
                 __snake_case				=		{0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                 __snake_case				=		{0: """batch""", 1: """decoder_sequence"""}
                 __snake_case				=		{0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                 self.fill_with_past_key_values_(lowercase__     ,    direction="inputs"						)

           return common_inputs


     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Optional[int]:




           '''simple docstring'''

           return 1_3






     @property
     def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Union[str, Any]:




           '''simple docstring'''

           return 5e-4









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'''






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






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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

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

											del agent_type

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

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		get_new_path(suffix=".wav"						)
											sf.write(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    1_6_0_0_0						)

											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)

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






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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		torch.randint(0     ,    2_5_6     ,    (6_4, 6_4, 3)						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Any:




											'''simple docstring'''

											__snake_case				=		Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"
											__snake_case				=		Image.open(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)

											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(SCREAMING_SNAKE_CASE						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"
											__snake_case				=		Image.open(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)

											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(SCREAMING_SNAKE_CASE						)						)






class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Any:




											'''simple docstring'''

											__snake_case				=		"Hey!"
											__snake_case				=		AgentText(SCREAMING_SNAKE_CASE						)

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





