






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 TFCamembertModel



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





					"""simple docstring"""






					@slow
					def  _lowercase  (      self  :						Optional[int] ):
						snake_case__				:   List[Any]     =						TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )

						snake_case__				:   List[Any]     =						tf.convert_to_tensor(
						    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]							,	dtype=tf.intaa							,	)  # J'aime le camembert !"

						snake_case__				:   Optional[int]     =						model(__A )["last_hidden_state"]
						snake_case__				:   Optional[Any]     =						tf.TensorShape((1, 1_0, 7_6_8) )
						self.assertEqual(output.shape							,	__A )
						# compare the actual values for a slice.
						snake_case__				:   Optional[int]     =						tf.convert_to_tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]							,	dtype=tf.floataa							,	)
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

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









from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
	from .pipeline_kandinsky import KandinskyPipeline
	from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
	from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
	from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
	from .text_encoder import MultilingualCLIP



