
"""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 			A__	:

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=13	,		_SCREAMING_SNAKE_CASE=7	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=99	,		_SCREAMING_SNAKE_CASE=32	,		_SCREAMING_SNAKE_CASE=2	,		_SCREAMING_SNAKE_CASE=4	,		_SCREAMING_SNAKE_CASE=37	,		_SCREAMING_SNAKE_CASE="gelu"	,		_SCREAMING_SNAKE_CASE=0.1	,		_SCREAMING_SNAKE_CASE=0.1	,		_SCREAMING_SNAKE_CASE=5_12	,		_SCREAMING_SNAKE_CASE=16	,		_SCREAMING_SNAKE_CASE=2	,		_SCREAMING_SNAKE_CASE=0.02	,		_SCREAMING_SNAKE_CASE=3	,		_SCREAMING_SNAKE_CASE=4	,		_SCREAMING_SNAKE_CASE=None	,		):
			__lowerCAmelCase      :						List[Any]						      =				parent
			__lowerCAmelCase      :						str						      =				13
			__lowerCAmelCase      :						Optional[int]						      =				7
			__lowerCAmelCase      :						Union[str, Any]						      =				True
			__lowerCAmelCase      :						Union[str, Any]						      =				True
			__lowerCAmelCase      :						List[str]						      =				True
			__lowerCAmelCase      :						Union[str, Any]						      =				True
			__lowerCAmelCase      :						str						      =				99
			__lowerCAmelCase      :						int						      =				32
			__lowerCAmelCase      :						Dict						      =				2
			__lowerCAmelCase      :						Union[str, Any]						      =				4
			__lowerCAmelCase      :						Dict						      =				37
			__lowerCAmelCase      :						List[str]						      =				"""gelu"""
			__lowerCAmelCase      :						List[str]						      =				0.1
			__lowerCAmelCase      :						Union[str, Any]						      =				0.1
			__lowerCAmelCase      :						List[Any]						      =				5_12
			__lowerCAmelCase      :						Optional[int]						      =				16
			__lowerCAmelCase      :						Dict						      =				2
			__lowerCAmelCase      :						List[str]						      =				0.02
			__lowerCAmelCase      :						List[Any]						      =				3
			__lowerCAmelCase      :						Any						      =				4
			__lowerCAmelCase      :						Union[str, Any]						      =				None

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				ids_tensor([self.batch_size, self.seq_length]	,		self.vocab_size			)

			__lowerCAmelCase      :						Union[str, Any]						      =				None
			if self.use_input_mask:
				__lowerCAmelCase      :						int						      =				random_attention_mask([self.batch_size, self.seq_length]			)

			__lowerCAmelCase      :						Any						      =				None
			if self.use_token_type_ids:
				__lowerCAmelCase      :						Dict						      =				ids_tensor([self.batch_size, self.seq_length]	,		self.type_vocab_size			)

			__lowerCAmelCase      :						Any						      =				None
			__lowerCAmelCase      :						List[Any]						      =				None
			__lowerCAmelCase      :						Union[str, Any]						      =				None
			if self.use_labels:
				__lowerCAmelCase      :						List[Any]						      =				ids_tensor([self.batch_size]	,		self.type_sequence_label_size			)
				__lowerCAmelCase      :						List[Any]						      =				ids_tensor([self.batch_size, self.seq_length]	,		self.num_labels			)
				__lowerCAmelCase      :						Any						      =				ids_tensor([self.batch_size]	,		self.num_choices			)

			__lowerCAmelCase      :						int						      =				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=_lowerCAmelCase	,		)

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

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Optional[int]						      =				TFRoFormerModel(config=_lowerCAmelCase			)
			__lowerCAmelCase      :						List[Any]						      =				{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

			__lowerCAmelCase      :						Union[str, Any]						      =				[input_ids, input_mask]
			__lowerCAmelCase      :						Optional[int]						      =				model(_lowerCAmelCase			)

			__lowerCAmelCase      :						str						      =				model(_lowerCAmelCase			)

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

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[str]						      =				True
			__lowerCAmelCase      :						List[str]						      =				TFRoFormerForCausalLM(config=_lowerCAmelCase			)
			__lowerCAmelCase      :						List[Any]						      =				{
			    """input_ids""": input_ids,
			    """attention_mask""": input_mask,
			    """token_type_ids""": token_type_ids,
			}
			__lowerCAmelCase      :						Union[str, Any]						      =				model(_lowerCAmelCase			)["""logits"""]
			self.parent.assertListEqual(
			    list(prediction_scores.numpy().shape			)	,		[self.batch_size, self.seq_length, self.vocab_size]			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Any						      =				TFRoFormerForMaskedLM(config=_lowerCAmelCase			)
			__lowerCAmelCase      :						Union[str, Any]						      =				{
			    """input_ids""": input_ids,
			    """attention_mask""": input_mask,
			    """token_type_ids""": token_type_ids,
			}
			__lowerCAmelCase      :						str						      =				model(_lowerCAmelCase			)
			self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.seq_length, self.vocab_size)			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[Any]						      =				self.num_labels
			__lowerCAmelCase      :						List[str]						      =				TFRoFormerForSequenceClassification(config=_lowerCAmelCase			)
			__lowerCAmelCase      :						Union[str, Any]						      =				{
			    """input_ids""": input_ids,
			    """attention_mask""": input_mask,
			    """token_type_ids""": token_type_ids,
			}

			__lowerCAmelCase      :						Dict						      =				model(_lowerCAmelCase			)
			self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.num_labels)			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Dict						      =				self.num_choices
			__lowerCAmelCase      :						Optional[Any]						      =				TFRoFormerForMultipleChoice(config=_lowerCAmelCase			)
			__lowerCAmelCase      :						str						      =				tf.tile(tf.expand_dims(_lowerCAmelCase	,		1			)	,		(1, self.num_choices, 1)			)
			__lowerCAmelCase      :						Optional[int]						      =				tf.tile(tf.expand_dims(_lowerCAmelCase	,		1			)	,		(1, self.num_choices, 1)			)
			__lowerCAmelCase      :						List[str]						      =				tf.tile(tf.expand_dims(_lowerCAmelCase	,		1			)	,		(1, self.num_choices, 1)			)
			__lowerCAmelCase      :						List[str]						      =				{
			    """input_ids""": multiple_choice_inputs_ids,
			    """attention_mask""": multiple_choice_input_mask,
			    """token_type_ids""": multiple_choice_token_type_ids,
			}
			__lowerCAmelCase      :						Tuple						      =				model(_lowerCAmelCase			)
			self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.num_choices)			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						str						      =				self.num_labels
			__lowerCAmelCase      :						List[Any]						      =				TFRoFormerForTokenClassification(config=_lowerCAmelCase			)
			__lowerCAmelCase      :						str						      =				{
			    """input_ids""": input_ids,
			    """attention_mask""": input_mask,
			    """token_type_ids""": token_type_ids,
			}
			__lowerCAmelCase      :						Optional[Any]						      =				model(_lowerCAmelCase			)
			self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.seq_length, self.num_labels)			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Optional[Any]						      =				TFRoFormerForQuestionAnswering(config=_lowerCAmelCase			)
			__lowerCAmelCase      :						Optional[int]						      =				{
			    """input_ids""": input_ids,
			    """attention_mask""": input_mask,
			    """token_type_ids""": token_type_ids,
			}

			__lowerCAmelCase      :						List[str]						      =				model(_lowerCAmelCase			)
			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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				self.prepare_config_and_inputs()
			(
			    __lowerCAmelCase				
			)      :						Optional[int]						      =				config_and_inputs
			__lowerCAmelCase      :						List[Any]						      =				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
			return config, inputs_dict




@require_tf
class 			A__	(     __UpperCamelCase	,					__UpperCamelCase	,					unittest.TestCase):
		A_						:	Union[str, Any]           =     (
		    (
		        TFRoFormerModel,
		        TFRoFormerForCausalLM,
		        TFRoFormerForMaskedLM,
		        TFRoFormerForQuestionAnswering,
		        TFRoFormerForSequenceClassification,
		        TFRoFormerForTokenClassification,
		        TFRoFormerForMultipleChoice,
		    )
		    if is_tf_available()
		    else ()
		)
		A_						:	str           =     (
		    {
		        "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 {}
		)

		A_						:	Optional[int]           =     False
		A_						:	Optional[int]           =     False

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			if pipeline_test_casse_name == "TextGenerationPipelineTests":
				return True

			return False

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				TFRoFormerModelTester(self			)
			__lowerCAmelCase      :						Dict						      =				ConfigTester(self	,		config_class=_lowerCAmelCase	,		hidden_size=37			)

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

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_lowerCAmelCase			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[Any]						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_lm_head(*_lowerCAmelCase			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_multiple_choice(*_lowerCAmelCase			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase			)







		@slow
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'			)
			self.assertIsNotNone(_lowerCAmelCase			)




@require_tf
class 			A__	(     unittest.TestCase):

		@slow
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'			)
			__lowerCAmelCase      :						Union[str, Any]						      =				tf.constant([[0, 1, 2, 3, 4, 5]]			)
			__lowerCAmelCase      :						Dict						      =				model(_lowerCAmelCase			)[0]

			# TODO Replace vocab size
			__lowerCAmelCase      :						Any						      =				5_00_00

			__lowerCAmelCase      :						Union[str, Any]						      =				[1, 6, vocab_size]
			self.assertEqual(output.shape	,		_lowerCAmelCase			)

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

			# TODO Replace values below with what was printed above.
			__lowerCAmelCase      :						str						      =				tf.constant(
			    [
			        [
			            [-0.1205_3341, -1.026_4901, 0.2922_1946],
			            [-1.513_3783, 0.19_7433, 0.1519_0607],
			            [-5.013_5403, -3.90_0256, -0.8403_8764],
			        ]
			    ]			)
			tf.debugging.assert_near(output[:, :3, :3]	,		_lowerCAmelCase	,		atol=1E-4			)




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

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				tf.constant([[4, 10]]			)
			__lowerCAmelCase      :						int						      =				TFRoFormerSinusoidalPositionalEmbedding(num_positions=6	,		embedding_dim=6			)

			__lowerCAmelCase      :						Tuple						      =				emba(input_ids.shape			)
			__lowerCAmelCase      :						str						      =				tf.constant(
			    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]			)

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







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				tf.constant(
			    [
			        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
			        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
			        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
			    ]			)
			__lowerCAmelCase      :						List[Any]						      =				TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12	,		embedding_dim=5_12			)
			emba([2, 16, 5_12]			)
			__lowerCAmelCase      :						str						      =				emba.weight[:3, :5]

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




@require_tf
class 			A__	(     unittest.TestCase):
		A_						:	Any           =     1e-4

		def 							__lowerCamelCase							(				self			):
			# 2,12,16,64
			__lowerCAmelCase      :						Dict						      =				tf.reshape(tf.range(2 * 12 * 16 * 64	,		dtype=tf.floataa			)	,		shape=(2, 12, 16, 64)			) / 1_00

			__lowerCAmelCase      :						Optional[Any]						      =				-tf.reshape(tf.range(2 * 12 * 16 * 64	,		dtype=tf.floataa			)	,		shape=(2, 12, 16, 64)			) / 1_00

			__lowerCAmelCase      :						Union[str, Any]						      =				TFRoFormerSinusoidalPositionalEmbedding(num_positions=32	,		embedding_dim=64			)
			__lowerCAmelCase      :						Any						      =				embed_positions([2, 16, 7_68]			)[None, None, :, :]

			__lowerCAmelCase      :						Optional[int]						      =				TFRoFormerSelfAttention.apply_rotary_position_embeddings(
			    _lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase			)

			__lowerCAmelCase      :						List[str]						      =				tf.constant(
			    [
			        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
			        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
			        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
			        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
			        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
			        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
			    ]			)
			__lowerCAmelCase      :						Tuple						      =				tf.constant(
			    [
			        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
			        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
			        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
			        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
			        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
			        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
			    ]			)

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

"""simple docstring"""







from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None		):
	if version.parse(hfh.__version__		).release < version.parse('0.11.0'		).release:
		# old versions of hfh don't url-encode the file path
		__lowerCAmelCase      :						List[str]						      =				quote(_UpperCamelCase		)
	return hfh.hf_hub_url(_UpperCamelCase	,  _UpperCamelCase	,  repo_type='dataset'	,  revision=_UpperCamelCase		)