
'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFRoFormerForCausalLM,
					    TFRoFormerForMaskedLM,
					    TFRoFormerForMultipleChoice,
					    TFRoFormerForQuestionAnswering,
					    TFRoFormerForSequenceClassification,
					    TFRoFormerForTokenClassification,
					    TFRoFormerModel,
					)
					from transformers.models.roformer.modeling_tf_roformer import (
					    TFRoFormerSelfAttention,
					    TFRoFormerSinusoidalPositionalEmbedding,
					)

class 						__lowercase					:

			def __init__(self      , A      , A=1_3      , A=7      , A=True      , A=True      , A=True      , A=True      , A=9_9      , A=3_2      , A=2      , A=4      , A=3_7      , A="gelu"      , A=0.1      , A=0.1      , A=5_1_2      , A=1_6      , A=2      , A=0.02      , A=3      , A=4      , A=None      , ):
								lowerCamelCase_       :   Optional[Any]  =		parent
								lowerCamelCase_       :   int  =		1_3
								lowerCamelCase_       :   Dict  =		7
								lowerCamelCase_       :   Dict  =		True
								lowerCamelCase_       :   Union[str, Any]  =		True
								lowerCamelCase_       :   int  =		True
								lowerCamelCase_       :   Union[str, Any]  =		True
								lowerCamelCase_       :   Tuple  =		9_9
								lowerCamelCase_       :   int  =		3_2
								lowerCamelCase_       :   Dict  =		2
								lowerCamelCase_       :   Tuple  =		4
								lowerCamelCase_       :   List[str]  =		3_7
								lowerCamelCase_       :   Optional[Any]  =		"gelu"
								lowerCamelCase_       :   List[str]  =		0.1
								lowerCamelCase_       :   Dict  =		0.1
								lowerCamelCase_       :   List[str]  =		5_1_2
								lowerCamelCase_       :   Any  =		1_6
								lowerCamelCase_       :   Dict  =		2
								lowerCamelCase_       :   Dict  =		0.02
								lowerCamelCase_       :   Union[str, Any]  =		3
								lowerCamelCase_       :   str  =		4
								lowerCamelCase_       :   Tuple  =		None

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Union[str, Any]  =		ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size   )

								lowerCamelCase_       :   Dict  =		None
								if self.use_input_mask:
													lowerCamelCase_       :   str  =		random_attention_mask([self.batch_size, self.seq_length]   )

								lowerCamelCase_       :   Tuple  =		None
								if self.use_token_type_ids:
													lowerCamelCase_       :   List[str]  =		ids_tensor([self.batch_size, self.seq_length]      , self.type_vocab_size   )

								lowerCamelCase_       :   Tuple  =		None
								lowerCamelCase_       :   str  =		None
								lowerCamelCase_       :   Dict  =		None
								if self.use_labels:
													lowerCamelCase_       :   Any  =		ids_tensor([self.batch_size]      , self.type_sequence_label_size   )
													lowerCamelCase_       :   int  =		ids_tensor([self.batch_size, self.seq_length]      , self.num_labels   )
													lowerCamelCase_       :   Any  =		ids_tensor([self.batch_size]      , self.num_choices   )

								lowerCamelCase_       :   List[str]  =		RoFormerConfig(
								    vocab_size=self.vocab_size      , hidden_size=self.hidden_size      , num_hidden_layers=self.num_hidden_layers      , num_attention_heads=self.num_attention_heads      , intermediate_size=self.intermediate_size      , hidden_act=self.hidden_act      , hidden_dropout_prob=self.hidden_dropout_prob      , attention_probs_dropout_prob=self.attention_probs_dropout_prob      , max_position_embeddings=self.max_position_embeddings      , type_vocab_size=self.type_vocab_size      , initializer_range=self.initializer_range      , return_dict=_lowercase      , )

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

			def 							UpperCAmelCase__			(self      , A      , A      , A      , A      , A      , A      , A   ):
								lowerCamelCase_       :   int  =		TFRoFormerModel(config=_lowercase   )
								lowerCamelCase_       :   Optional[Any]  =		{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

								lowerCamelCase_       :   Union[str, Any]  =		[input_ids, input_mask]
								lowerCamelCase_       :   Dict  =		model(_lowercase   )

								lowerCamelCase_       :   Any  =		model(_lowercase   )

								self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )

			def 							UpperCAmelCase__			(self      , A      , A      , A      , A      , A      , A      , A   ):
								lowerCamelCase_       :   Optional[int]  =		True
								lowerCamelCase_       :   List[Any]  =		TFRoFormerForCausalLM(config=_lowercase   )
								lowerCamelCase_       :   List[Any]  =		{
								    "input_ids": input_ids,
								    "attention_mask": input_mask,
								    "token_type_ids": token_type_ids,
								}
								lowerCamelCase_       :   Tuple  =		model(_lowercase   )["logits"]
								self.parent.assertListEqual(
								    list(prediction_scores.numpy().shape   )      , [self.batch_size, self.seq_length, self.vocab_size]   )

			def 							UpperCAmelCase__			(self      , A      , A      , A      , A      , A      , A      , A   ):
								lowerCamelCase_       :   List[str]  =		TFRoFormerForMaskedLM(config=_lowercase   )
								lowerCamelCase_       :   Optional[Any]  =		{
								    "input_ids": input_ids,
								    "attention_mask": input_mask,
								    "token_type_ids": token_type_ids,
								}
								lowerCamelCase_       :   str  =		model(_lowercase   )
								self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.vocab_size)   )

			def 							UpperCAmelCase__			(self      , A      , A      , A      , A      , A      , A      , A   ):
								lowerCamelCase_       :   Optional[Any]  =		self.num_labels
								lowerCamelCase_       :   Dict  =		TFRoFormerForSequenceClassification(config=_lowercase   )
								lowerCamelCase_       :   Optional[int]  =		{
								    "input_ids": input_ids,
								    "attention_mask": input_mask,
								    "token_type_ids": token_type_ids,
								}

								lowerCamelCase_       :   List[str]  =		model(_lowercase   )
								self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)   )

			def 							UpperCAmelCase__			(self      , A      , A      , A      , A      , A      , A      , A   ):
								lowerCamelCase_       :   List[Any]  =		self.num_choices
								lowerCamelCase_       :   List[Any]  =		TFRoFormerForMultipleChoice(config=_lowercase   )
								lowerCamelCase_       :   Union[str, Any]  =		tf.tile(tf.expand_dims(_lowercase      , 1   )      , (1, self.num_choices, 1)   )
								lowerCamelCase_       :   Optional[Any]  =		tf.tile(tf.expand_dims(_lowercase      , 1   )      , (1, self.num_choices, 1)   )
								lowerCamelCase_       :   List[Any]  =		tf.tile(tf.expand_dims(_lowercase      , 1   )      , (1, self.num_choices, 1)   )
								lowerCamelCase_       :   Dict  =		{
								    "input_ids": multiple_choice_inputs_ids,
								    "attention_mask": multiple_choice_input_mask,
								    "token_type_ids": multiple_choice_token_type_ids,
								}
								lowerCamelCase_       :   Any  =		model(_lowercase   )
								self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_choices)   )

			def 							UpperCAmelCase__			(self      , A      , A      , A      , A      , A      , A      , A   ):
								lowerCamelCase_       :   Any  =		self.num_labels
								lowerCamelCase_       :   str  =		TFRoFormerForTokenClassification(config=_lowercase   )
								lowerCamelCase_       :   Optional[int]  =		{
								    "input_ids": input_ids,
								    "attention_mask": input_mask,
								    "token_type_ids": token_type_ids,
								}
								lowerCamelCase_       :   Optional[int]  =		model(_lowercase   )
								self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.num_labels)   )

			def 							UpperCAmelCase__			(self      , A      , A      , A      , A      , A      , A      , A   ):
								lowerCamelCase_       :   int  =		TFRoFormerForQuestionAnswering(config=_lowercase   )
								lowerCamelCase_       :   Optional[int]  =		{
								    "input_ids": input_ids,
								    "attention_mask": input_mask,
								    "token_type_ids": token_type_ids,
								}

								lowerCamelCase_       :   List[str]  =		model(_lowercase   )
								self.parent.assertEqual(result.start_logits.shape      , (self.batch_size, self.seq_length)   )
								self.parent.assertEqual(result.end_logits.shape      , (self.batch_size, self.seq_length)   )






			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Tuple  =		self.prepare_config_and_inputs()
								(
								    lowerCamelCase_			
								)       :   List[Any]  =		config_and_inputs
								lowerCamelCase_       :   Optional[Any]  =		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
								return config, inputs_dict

@require_tf
class 						__lowercase					(	SCREAMING_SNAKE_CASE__   ,  SCREAMING_SNAKE_CASE__   ,  unittest.TestCase     ):
			lowerCamelCase      :		List[str]						   =      (
			    (
			        TFRoFormerModel,
			        TFRoFormerForCausalLM,
			        TFRoFormerForMaskedLM,
			        TFRoFormerForQuestionAnswering,
			        TFRoFormerForSequenceClassification,
			        TFRoFormerForTokenClassification,
			        TFRoFormerForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCamelCase      :		List[Any]						   =      (
			    {
			        "feature-extraction": TFRoFormerModel,
			        "fill-mask": TFRoFormerForMaskedLM,
			        "question-answering": TFRoFormerForQuestionAnswering,
			        "text-classification": TFRoFormerForSequenceClassification,
			        "text-generation": TFRoFormerForCausalLM,
			        "token-classification": TFRoFormerForTokenClassification,
			        "zero-shot": TFRoFormerForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase      :		Tuple						   =      False
			lowerCamelCase      :		Dict						   =      False

			def 							UpperCAmelCase__			(self      , A      , A      , A      , A      , A   ):
								if pipeline_test_casse_name == "TextGenerationPipelineTests":
													return True

								return False

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   int  =		TFRoFormerModelTester(self   )
								lowerCamelCase_       :   Optional[Any]  =		ConfigTester(self      , config_class=_lowercase      , hidden_size=3_7   )

			def 							UpperCAmelCase__			(self   ):
								self.config_tester.run_common_tests()

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Optional[Any]  =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase   )

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Any  =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_lowercase   )

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Optional[Any]  =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head(*_lowercase   )

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Any  =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*_lowercase   )

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Any  =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*_lowercase   )

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Optional[Any]  =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*_lowercase   )

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Dict  =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*_lowercase   )






			@slow
			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Dict  =		TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''   )
								self.assertIsNotNone(_lowercase   )

@require_tf
class 						__lowercase					(	unittest.TestCase     ):

			@slow
			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Dict  =		TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''   )
								lowerCamelCase_       :   List[str]  =		tf.constant([[0, 1, 2, 3, 4, 5]]   )
								lowerCamelCase_       :   int  =		model(_lowercase   )[0]

								# TODO Replace vocab size
								lowerCamelCase_       :   Union[str, Any]  =		5_0_0_0_0

								lowerCamelCase_       :   Optional[int]  =		[1, 6, vocab_size]
								self.assertEqual(output.shape      , _lowercase   )

								print(output[:, :3, :3]   )

								# TODO Replace values below with what was printed above.
								lowerCamelCase_       :   Any  =		tf.constant(
								    [
								        [
								            [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
								            [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
								            [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
								        ]
								    ]   )
								tf.debugging.assert_near(output[:, :3, :3]      , _lowercase      , atol=1E-4   )

@require_tf
class 						__lowercase					(	unittest.TestCase     ):
			lowerCamelCase      :		str						   =      1e-4

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   str  =		tf.constant([[4, 1_0]]   )
								lowerCamelCase_       :   List[Any]  =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=6      , embedding_dim=6   )

								lowerCamelCase_       :   List[str]  =		emba(input_ids.shape   )
								lowerCamelCase_       :   Optional[Any]  =		tf.constant(
								    [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]]   )

								tf.debugging.assert_near(_lowercase      , _lowercase      , atol=self.tolerance   )






			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Dict  =		tf.constant(
								    [
								        [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
								        [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
								        [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
								    ]   )
								lowerCamelCase_       :   Optional[int]  =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2      , embedding_dim=5_1_2   )
								emba([2, 1_6, 5_1_2]   )
								lowerCamelCase_       :   Any  =		emba.weight[:3, :5]

								tf.debugging.assert_near(_lowercase      , _lowercase      , atol=self.tolerance   )

@require_tf
class 						__lowercase					(	unittest.TestCase     ):
			lowerCamelCase      :		List[str]						   =      1e-4

			def 							UpperCAmelCase__			(self   ):
								# 2,12,16,64
								lowerCamelCase_       :   Union[str, Any]  =		tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4      , dtype=tf.floataa   )      , shape=(2, 1_2, 1_6, 6_4)   ) / 1_0_0

								lowerCamelCase_       :   Dict  =		-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4      , dtype=tf.floataa   )      , shape=(2, 1_2, 1_6, 6_4)   ) / 1_0_0

								lowerCamelCase_       :   Union[str, Any]  =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2      , embedding_dim=6_4   )
								lowerCamelCase_       :   Optional[int]  =		embed_positions([2, 1_6, 7_6_8]   )[None, None, :, :]

								lowerCamelCase_       :   Optional[Any]  =		TFRoFormerSelfAttention.apply_rotary_position_embeddings(
								    _lowercase      , _lowercase      , _lowercase   )

								lowerCamelCase_       :   Dict  =		tf.constant(
								    [
								        [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
								        [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
								        [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
								        [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
								        [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
								        [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
								    ]   )
								lowerCamelCase_       :   Any  =		tf.constant(
								    [
								        [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
								        [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
								        [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
								        [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
								        [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
								        [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
								    ]   )

								tf.debugging.assert_near(query_layer[0, 0, :6, :8]      , _lowercase      , atol=self.tolerance   )
								tf.debugging.assert_near(key_layer[0, 0, :6, :8]      , _lowercase      , atol=self.tolerance   )







"""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
A		=						'\nHuman: <<task>>\n\nAssistant: '


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

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							Tuple="run" ):
						"""simple docstring"""






						if prompt_or_repo_id is None:
												snake_case   :			Any						    = DEFAULT_PROMPTS_REPO

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

						snake_case   :			Optional[int]						    = cached_file(
						    lowerCamelCase_     ,		PROMPT_FILES[mode]     ,		repo_type="dataset"     ,		user_agent={"agent": agent_name} )
						with open(lowerCamelCase_     ,		"r"     ,		encoding="utf-8" ) as f:
												return f.read()



