
"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
lowerCAmelCase__				=					'\nHuman: <<task>>\n\nAssistant: '


lowerCAmelCase__				=					'huggingface-tools/default-prompts'
lowerCAmelCase__				=					{'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}



def 							_lowerCamelCase			(			__a,				__a,				__a="run"     ):
		if prompt_or_repo_id is None:
				SCREAMING_SNAKE_CASE_    			=			DEFAULT_PROMPTS_REPO

		# prompt is considered a repo ID when it does not contain any kind of space
		if re.search('''\\s''',				__a     ) is not None:
				return prompt_or_repo_id

		SCREAMING_SNAKE_CASE_    			=			cached_file(
		    __a,				PROMPT_FILES[mode],				repo_type='''dataset''',				user_agent={'''agent''': agent_name}     )
		with open(__a,				'''r''',				encoding='''utf-8'''     ) as f:
				return f.read()

"""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 			snake_case  (				unittest.TestCase ):

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''     )

									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]							,							dtype=tf.intaa							,							)  # J'aime le camembert !"

									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_     )['''last_hidden_state''']
									SCREAMING_SNAKE_CASE_    			=			tf.TensorShape((1, 10, 7_68)     )
									self.assertEqual(output.shape							,							SCREAMING_SNAKE_CASE_     )
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]							,							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     )     )