






"""simple docstring"""
import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     UpperCamelCase_ (lowercase__       ,		unittest.TestCase  ):
	__magic_name__			   =       FunnelTokenizer
	__magic_name__			   =       FunnelTokenizerFast
	__magic_name__			   =       True
	__magic_name__			   =       True





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Optional[int]:
				super().setUp()

				UpperCAmelCase_  :				int      =      [
				    "<unk>",
				    "<cls>",
				    "<sep>",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				UpperCAmelCase_  :				Any      =      os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES["vocab_file"]		)
				with open(self.vocab_file			,			"w"			,			encoding="utf-8"		) as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			**lowerCAmelCase_      :				Tuple		)		->    Tuple:
				return FunnelTokenizer.from_pretrained(self.tmpdirname			,			**__lowerCamelCase		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			**lowerCAmelCase_      :				Optional[int]		)		->    Optional[Any]:
				return FunnelTokenizerFast.from_pretrained(self.tmpdirname			,			**__lowerCamelCase		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				Union[str, Any]		)		->    int:
				UpperCAmelCase_  :				int      =      "UNwant\u00E9d,running"
				UpperCAmelCase_  :				Optional[Any]      =      "unwanted, running"
				return input_text, output_text





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[Any]      =      self.tokenizer_class(self.vocab_file		)

				UpperCAmelCase_  :				List[str]      =      tokenizer.tokenize("UNwant\u00E9d,running"		)
				self.assertListEqual(__lowerCamelCase			,			["un", "##want", "##ed", ",", "runn", "##ing"]		)
				self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase		)			,			[7, 4, 5, 10, 8, 9]		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Dict:
				UpperCAmelCase_  :				Optional[int]      =      self.get_tokenizers(do_lower_case=__lowerCamelCase		)
				for tokenizer in tokenizers:
							UpperCAmelCase_  :				Dict      =      tokenizer("UNwant\u00E9d,running"		)
							UpperCAmelCase_  :				int      =      len(inputs["input_ids"]		) - 1
							self.assertListEqual(inputs["token_type_ids"]			,			[2] + [0] * sentence_len		)

							UpperCAmelCase_  :				Union[str, Any]      =      tokenizer("UNwant\u00E9d,running"			,			"UNwant\u00E9d,running"		)
							self.assertListEqual(inputs["token_type_ids"]			,			[2] + [0] * sentence_len + [1] * sentence_len		)












"""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 TFCamembertModel







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





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[Any]      =      TFCamembertModel.from_pretrained("jplu/tf-camembert-base"		)

				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]			,			dtype=tf.intaa			,			)  # J'aime le camembert !"

				UpperCAmelCase_  :				Any      =      model(lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_  :				str      =      tf.TensorShape((1, 10, 768)		)
				self.assertEqual(output.shape			,			lowerCAmelCase_		)
				# compare the actual values for a slice.
				UpperCAmelCase_  :				Tuple      =      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		)		)



