





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast





@require_vision
class        lowercase_				( unittest.TestCase):






							"""simple docstring"""







							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		tempfile.mkdtemp()

													a_					=		CLIPImageProcessor()
													a_					=		BertTokenizer.from_pretrained(
													    """hf-internal-testing/tiny-random-BertModel"""			,    model_input_names=["""input_ids""", """attention_mask"""]		)

													a_					=		GitProcessor(_UpperCAmelCase			,    _UpperCAmelCase		)

													processor.save_pretrained(self.tmpdirname		)


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return AutoProcessor.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		).tokenizer


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return AutoProcessor.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		).image_processor


							def 						lowercase__     (   self		):







													"""simple docstring"""


													shutil.rmtree(self.tmpdirname		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		[np.random.randint(255			,    size=(3, 30, 400)			,    dtype=np.uinta		)]

													a_					=		[Image.fromarray(np.moveaxis(_UpperCAmelCase			,    0			,    -1		)		) for x in image_inputs]

													return image_inputs


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		GitProcessor(tokenizer=self.get_tokenizer()			,    image_processor=self.get_image_processor()		)
													processor.save_pretrained(self.tmpdirname		)

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

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

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

													self.assertEqual(processor.image_processor.to_json_string()			,    image_processor_add_kwargs.to_json_string()		)
													self.assertIsInstance(processor.image_processor			,    _UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.get_image_processor()
													a_					=		self.get_tokenizer()

													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)

													a_					=		self.prepare_image_inputs()

													a_					=		image_processor(_UpperCAmelCase			,    return_tensors="""np"""		)
													a_					=		processor(images=_UpperCAmelCase			,    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 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.get_image_processor()
													a_					=		self.get_tokenizer()

													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)

													a_					=		"""lower newer"""

													a_					=		processor(text=_UpperCAmelCase		)

													a_					=		tokenizer(_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)

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


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.get_image_processor()
													a_					=		self.get_tokenizer()

													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)

													a_					=		"""lower newer"""
													a_					=		self.prepare_image_inputs()

													a_					=		processor(text=_UpperCAmelCase			,    images=_UpperCAmelCase		)

													self.assertListEqual(list(inputs.keys()		)			,    ["""input_ids""", """attention_mask""", """pixel_values"""]		)

													# test if it raises when no input is passed
													with pytest.raises(_UpperCAmelCase		):
																			processor()


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.get_image_processor()
													a_					=		self.get_tokenizer()

													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)

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

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

													self.assertListEqual(_UpperCAmelCase			,    _UpperCAmelCase		)






							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.get_image_processor()
													a_					=		self.get_tokenizer()

													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)

													a_					=		"""lower newer"""
													a_					=		self.prepare_image_inputs()

													a_					=		processor(text=_UpperCAmelCase			,    images=_UpperCAmelCase		)

													# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
													self.assertListEqual(list(inputs.keys()		)			,    ["""input_ids""", """attention_mask""", """pixel_values"""]		)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    :					Optional[Any]             ={
    """configuration_wav2vec2_conformer""": [
        """WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Wav2Vec2ConformerConfig""",
    ],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_    :					Union[str, Any]             =[
	    """WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """Wav2Vec2ConformerForAudioFrameClassification""",
	    """Wav2Vec2ConformerForCTC""",
	    """Wav2Vec2ConformerForPreTraining""",
	    """Wav2Vec2ConformerForSequenceClassification""",
	    """Wav2Vec2ConformerForXVector""",
	    """Wav2Vec2ConformerModel""",
	    """Wav2Vec2ConformerPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_wavaveca_conformer import (
	    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    WavaVecaConformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_wavaveca_conformer import (
		    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    WavaVecaConformerForAudioFrameClassification,
		    WavaVecaConformerForCTC,
		    WavaVecaConformerForPreTraining,
		    WavaVecaConformerForSequenceClassification,
		    WavaVecaConformerForXVector,
		    WavaVecaConformerModel,
		    WavaVecaConformerPreTrainedModel,
		)

else:
	import sys

	A_    :					Union[str, Any]             =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)