






"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFXLMRobertaModel


@require_tf
@require_sentencepiece
@require_tokenizers
class 						__lowerCamelCase							(					unittest.TestCase     ):





		'''simple docstring'''






		@slow
		def   lowerCamelCase			(  self				:      Union[str, Any]      ):
				lowerCAmelCase_  :				List[Any]   							=     TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base"      )

				lowerCAmelCase_  :				Dict   							=     {
				    '''input_ids''': tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]]		,     dtype=tf.intaa      ),  # "My dog is cute"
				    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]		,     dtype=tf.intaa      ),
				}

				lowerCAmelCase_  :				Optional[int]   							=     model(__lowercase      )['''last_hidden_state''']
				lowerCAmelCase_  :				str   							=     tf.TensorShape((1, 6, 7_68)      )
				self.assertEqual(output.shape		,     __lowercase      )
				# compare the actual values for a slice.
				lowerCAmelCase_  :				str   							=     tf.convert_to_tensor(
				    [
				        [
				            [0.0681762, 0.10894451, 0.06772504],
				            [-0.06423668, 0.02366615, 0.04329344],
				            [-0.06057295, 0.09974135, -0.00070584],
				        ]
				    ]		,     dtype=tf.floataa		,     )

				self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,     expected_slice.numpy()		,     atol=1e-4      )      )







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase					      =   {
    'configuration_pix2struct': [
        'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Pix2StructConfig',
        'Pix2StructTextConfig',
        'Pix2StructVisionConfig',
    ],
    'processing_pix2struct': ['Pix2StructProcessor'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   ['Pix2StructImageProcessor']


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_UpperCamelCase					      =   [
							    'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'Pix2StructPreTrainedModel',
							    'Pix2StructForConditionalGeneration',
							    'Pix2StructVisionModel',
							    'Pix2StructTextModel',
							]

if TYPE_CHECKING:
							from .configuration_pixastruct import (
							    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    PixaStructConfig,
							    PixaStructTextConfig,
							    PixaStructVisionConfig,
							)
							from .processing_pixastruct import PixaStructProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_pixastruct import PixaStructImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pixastruct import (
														    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PixaStructForConditionalGeneration,
														    PixaStructPreTrainedModel,
														    PixaStructTextModel,
														    PixaStructVisionModel,
														)

else:
							import sys

							_UpperCamelCase					      =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




