

from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image


lowerCAmelCase__								=		["""text""", """image""", """audio"""]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]	)						->				Optional[int]:




 '''simple docstring'''




 _UpperCamelCase   :		Dict			=						[]

 for input_type in input_types:
  if input_type == "text":
   inputs.append('Text input'	)
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	).resize((5_1_2, 5_1_2)	)	)
  elif input_type == "audio":
   inputs.append(torch.ones(3_0_0_0	)	)
  elif isinstance(UpperCAmelCase_							,       UpperCAmelCase_	):
   inputs.append(create_inputs(UpperCAmelCase_	)	)
  else:
   raise ValueError(F'''Invalid type requested: {input_type}'''	)

 return inputs






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List	)						->				str:




 '''simple docstring'''




 _UpperCamelCase   :		Optional[Any]			=						[]

 for output in outputs:
  if isinstance(UpperCAmelCase_							,       (str, AgentText)	):
   output_types.append('text'	)
  elif isinstance(UpperCAmelCase_							,       (Image.Image, AgentImage)	):
   output_types.append('image'	)
  elif isinstance(UpperCAmelCase_							,       (torch.Tensor, AgentAudio)	):
   output_types.append('audio'	)
  else:
   raise ValueError(F'''Invalid output: {output}'''	)

 return output_types







@is_tool_test
class 	lowercase    :


  """simple docstring"""




  def 		A__					(				self):
   self.assertTrue(hasattr(self.tool	,       'inputs'))
   self.assertTrue(hasattr(self.tool	,       'outputs'))

   _UpperCamelCase   :		str			=						self.tool.inputs
   for _input in inputs:
    if isinstance(_input	,       __snake_case):
     for __input in _input:
      self.assertTrue(__input in authorized_types)
    else:
     self.assertTrue(_input in authorized_types)

   _UpperCamelCase   :		Optional[int]			=						self.tool.outputs
   for _output in outputs:
    self.assertTrue(_output in authorized_types)


  def 		A__					(				self):
   _UpperCamelCase   :		int			=						create_inputs(self.tool.inputs)
   _UpperCamelCase   :		Any			=						self.tool(*__snake_case)

   # There is a single output
   if len(self.tool.outputs) == 1:
    _UpperCamelCase   :		Tuple			=						[outputs]

   self.assertListEqual(output_types(__snake_case)	,       self.tool.outputs)


  def 		A__					(				self):
   self.assertTrue(hasattr(self.tool	,       'description'))
   self.assertTrue(hasattr(self.tool	,       'default_checkpoint'))
   self.assertTrue(self.tool.description.startswith('This is a tool that'))


  def 		A__					(				self):
   _UpperCamelCase   :		str			=						create_inputs(self.tool.inputs)
   _UpperCamelCase   :		int			=						self.tool(*__snake_case)

   if not isinstance(__snake_case	,       __snake_case):
    _UpperCamelCase   :		Dict			=						[outputs]

   self.assertEqual(len(__snake_case)	,       len(self.tool.outputs))

   for output, output_type in zip(__snake_case	,       self.tool.outputs):
    _UpperCamelCase   :		Any			=						AGENT_TYPE_MAPPING[output_type]
    self.assertTrue(isinstance(__snake_case	,       __snake_case))






  def 		A__					(				self):
   _UpperCamelCase   :		List[Any]			=						create_inputs(self.tool.inputs)

   _UpperCamelCase   :		List[str]			=						[]

   for _input, input_type in zip(__snake_case	,       self.tool.inputs):
    if isinstance(__snake_case	,       __snake_case):
     _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input) for _input_type in input_type])
    else:
     _inputs.append(AGENT_TYPE_MAPPING[input_type](_input))

        # Should not raise an error
   _UpperCamelCase   :		Optional[int]			=						self.tool(*__snake_case)

   if not isinstance(__snake_case	,       __snake_case):
    _UpperCamelCase   :		str			=						[outputs]

   self.assertEqual(len(__snake_case)	,       len(self.tool.outputs))







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="absolute"	,       __snake_case=True	,       __snake_case=None	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		int			=						vocab_size
			_UpperCamelCase   :		Optional[Any]			=						hidden_size
			_UpperCamelCase   :		Optional[Any]			=						num_hidden_layers
			_UpperCamelCase   :		List[str]			=						num_attention_heads
			_UpperCamelCase   :		int			=						hidden_act
			_UpperCamelCase   :		Optional[Any]			=						intermediate_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_dropout_prob
			_UpperCamelCase   :		Tuple			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Optional[int]			=						max_position_embeddings
			_UpperCamelCase   :		str			=						type_vocab_size
			_UpperCamelCase   :		Optional[Any]			=						initializer_range
			_UpperCamelCase   :		List[str]			=						layer_norm_eps
			_UpperCamelCase   :		Any			=						position_embedding_type
			_UpperCamelCase   :		Any			=						use_cache
			_UpperCamelCase   :		Any			=						classifier_dropout







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




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




