import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      "laion/clap-htsat-unfused"
    UpperCamelCase			  =      tempfile.mkdtemp()







   def 						lowerCamelCase_	(     self						:						int   ,   **lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    return RobertaTokenizer.from_pretrained(self.checkpoint   ,   **a_				)







   def 						lowerCamelCase_	(     self						:						str   ,   **lowerCamelCase_						:						Any				):





    """simple docstring"""


    return ClapFeatureExtractor.from_pretrained(self.checkpoint   ,   **a_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    shutil.rmtree(self.tmpdirname				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_tokenizer()
    UpperCamelCase			  =      self.get_feature_extractor()

    UpperCamelCase			  =      ClapProcessor(tokenizer=a_   ,   feature_extractor=a_				)

    processor.save_pretrained(self.tmpdirname				)
    UpperCamelCase			  =      ClapProcessor.from_pretrained(self.tmpdirname				)

    self.assertEqual(processor.tokenizer.get_vocab()   ,   tokenizer.get_vocab()				)
    self.assertIsInstance(processor.tokenizer   ,   a_				)

    self.assertEqual(processor.feature_extractor.to_json_string()   ,   feature_extractor.to_json_string()				)
    self.assertIsInstance(processor.feature_extractor   ,   a_				)







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      ClapProcessor(tokenizer=self.get_tokenizer()   ,   feature_extractor=self.get_feature_extractor()				)
    processor.save_pretrained(self.tmpdirname				)

    UpperCamelCase			  =      self.get_tokenizer(bos_token="""(BOS)"""   ,   eos_token="""(EOS)"""				)
    UpperCamelCase			  =      self.get_feature_extractor(do_normalize=a_   ,   padding_value=1.0				)

    UpperCamelCase			  =      ClapProcessor.from_pretrained(
        self.tmpdirname   ,   bos_token="""(BOS)"""   ,   eos_token="""(EOS)"""   ,   do_normalize=a_   ,   padding_value=1.0				)

    self.assertEqual(processor.tokenizer.get_vocab()   ,   tokenizer_add_kwargs.get_vocab()				)
    self.assertIsInstance(processor.tokenizer   ,   a_				)

    self.assertEqual(processor.feature_extractor.to_json_string()   ,   feature_extractor_add_kwargs.to_json_string()				)
    self.assertIsInstance(processor.feature_extractor   ,   a_				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_feature_extractor()
    UpperCamelCase			  =      self.get_tokenizer()

    UpperCamelCase			  =      ClapProcessor(tokenizer=a_   ,   feature_extractor=a_				)

    UpperCamelCase			  =      floats_list((3, 1000)				)

    UpperCamelCase			  =      feature_extractor(a_   ,   return_tensors="""np"""				)
    UpperCamelCase			  =      processor(audios=a_   ,   return_tensors="""np"""				)

    for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()   ,   input_processor[key].sum()   ,   delta=1E-2				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_feature_extractor()
    UpperCamelCase			  =      self.get_tokenizer()

    UpperCamelCase			  =      ClapProcessor(tokenizer=a_   ,   feature_extractor=a_				)

    UpperCamelCase			  =      "This is a test string"

    UpperCamelCase			  =      processor(text=a_				)

    UpperCamelCase			  =      tokenizer(a_				)

    for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]   ,   encoded_processor[key]				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_feature_extractor()
    UpperCamelCase			  =      self.get_tokenizer()

    UpperCamelCase			  =      ClapProcessor(tokenizer=a_   ,   feature_extractor=a_				)

    UpperCamelCase			  =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    UpperCamelCase			  =      processor.batch_decode(a_				)
    UpperCamelCase			  =      tokenizer.batch_decode(a_				)

    self.assertListEqual(a_   ,   a_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_feature_extractor()
    UpperCamelCase			  =      self.get_tokenizer()

    UpperCamelCase			  =      ClapProcessor(tokenizer=a_   ,   feature_extractor=a_				)

    self.assertListEqual(
        processor.model_input_names[2:]   ,   feature_extractor.model_input_names   ,   msg="""`processor` and `feature_extractor` model input names do not match"""   ,   )




'''simple docstring'''
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   : List[Any]             =				["""text""", """image""", """audio"""]




def 	_A  (       A							)  ->				Dict:
	lowercase						:		str   					=		[]

	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(A							,A							):
			inputs.append(create_inputs(A							)							)
		else:
			raise ValueError(F'''Invalid type requested: {input_type}'''							)

	return inputs




def 	_A  (       A							)  ->				str:
	lowercase						:		Tuple   					=		[]

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

	return output_types






@is_tool_test
class        _UpperCamelCase	:


				'''simple docstring'''


				def 	a__		(     self							)    ->  Optional[Any]:
					self.assertTrue(hasattr(self.tool							,      "inputs"							)							)
					self.assertTrue(hasattr(self.tool							,      "outputs"							)							)

					lowercase						:		Optional[Any]   					=		self.tool.inputs
					for _input in inputs:
						if isinstance(_input							,      a_							):
							for __input in _input:
								self.assertTrue(__input in authorized_types							)
						else:
							self.assertTrue(_input in authorized_types							)

					lowercase						:		Any   					=		self.tool.outputs
					for _output in outputs:
						self.assertTrue(_output in authorized_types							)

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Any   					=		create_inputs(self.tool.inputs							)
					lowercase						:		Tuple   					=		self.tool(*a_							)

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

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

				def 	a__		(     self							)    ->  List[str]:
					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							)    ->  int:
					lowercase						:		str   					=		create_inputs(self.tool.inputs							)
					lowercase						:		str   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		Union[str, Any]   					=		[outputs]

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

					for output, output_type in zip(a_							,      self.tool.outputs							):
						lowercase						:		List[str]   					=		AGENT_TYPE_MAPPING[output_type]
						self.assertTrue(isinstance(a_							,      a_							)							)


				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		int   					=		create_inputs(self.tool.inputs							)

					lowercase						:		str   					=		[]

					for _input, input_type in zip(a_							,      self.tool.inputs							):
						if isinstance(a_							,      a_							):
							_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
					lowercase						:		Optional[int]   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		str   					=		[outputs]

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


