
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    :    Union[str, Any]=""							):




			"""simple docstring"""


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





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




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      torch.rand(12							,							dtype=torch.floataa	) - 0.5
							lowerCAmelCase_         =      AgentAudio(_UpperCamelCase	)
							lowerCAmelCase_         =      str(agent_type.to_string()	)

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

							del agent_type

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

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




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      torch.rand(12							,							dtype=torch.floataa	) - 0.5
							lowerCAmelCase_         =      get_new_path(suffix=".wav"	)
							sf.write(_UpperCamelCase							,							_UpperCamelCase							,							16_000	)

							lowerCAmelCase_         =      AgentAudio(_UpperCamelCase	)

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





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




				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      torch.randint(0							,							256							,							(64, 64, 3)	)
							lowerCAmelCase_         =      AgentImage(_UpperCamelCase	)
							lowerCAmelCase_         =      str(agent_type.to_string()	)

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




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"
							lowerCAmelCase_         =      Image.open(_UpperCamelCase	)
							lowerCAmelCase_         =      AgentImage(_UpperCamelCase	)

							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(_UpperCamelCase	)	)




				def 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"
							lowerCAmelCase_         =      Image.open(_UpperCamelCase	)
							lowerCAmelCase_         =      AgentImage(_UpperCamelCase	)

							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(_UpperCamelCase	)	)





class       _lowerCAmelCase   (      unittest.TestCase			):




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      "Hey!"
							lowerCAmelCase_         =      AgentText(_UpperCamelCase	)

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





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

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


if TYPE_CHECKING:
			from ... import FeatureExtractionMixin, TensorType

_A  =	logging.get_logger(__name__)

_A  =	{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''imagegpt'''
				_lowercase     						=['''past_key_values''']
				_lowercase     						={
				    '''hidden_size''': '''n_embd''',
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__(   self							,							_UpperCamelCase=512 + 1							,							_UpperCamelCase=32 * 32							,							_UpperCamelCase=512							,							_UpperCamelCase=24							,							_UpperCamelCase=8							,							_UpperCamelCase=None							,							_UpperCamelCase="quick_gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=1e-5							,							_UpperCamelCase=0.02							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							**_UpperCamelCase							,							)							-> Tuple:
							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      n_positions
							lowerCAmelCase_         =      n_embd
							lowerCAmelCase_         =      n_layer
							lowerCAmelCase_         =      n_head
							lowerCAmelCase_         =      n_inner
							lowerCAmelCase_         =      activation_function
							lowerCAmelCase_         =      resid_pdrop
							lowerCAmelCase_         =      embd_pdrop
							lowerCAmelCase_         =      attn_pdrop
							lowerCAmelCase_         =      layer_norm_epsilon
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      scale_attn_weights
							lowerCAmelCase_         =      use_cache
							lowerCAmelCase_         =      scale_attn_by_inverse_layer_idx
							lowerCAmelCase_         =      reorder_and_upcast_attn
							lowerCAmelCase_         =      tie_word_embeddings

							super().__init__(tie_word_embeddings=_UpperCamelCase							,							**_UpperCamelCase	)





class       _lowerCAmelCase   (      __a			):




				@property
				def 	__a		(   self	)							-> Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ("input_ids", {0: "batch", 1: "sequence"}),
							    ]	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = 1							,							_UpperCamelCase = -1							,							_UpperCamelCase = False							,							_UpperCamelCase = None							,							_UpperCamelCase = 3							,							_UpperCamelCase = 32							,							_UpperCamelCase = 32							,							)							-> Mapping[str, Any]:
							lowerCAmelCase_         =      self._generate_dummy_images(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)
							lowerCAmelCase_         =      dict(preprocessor(images=_UpperCamelCase							,							return_tensors=_UpperCamelCase	)	)

							return inputs



