




import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


lowercase_			:       Optional[int]						       =				'\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = "Text Chunking using Transformation-Based Learning",\n    author = "Ramshaw, Lance  and\n      Marcus, Mitch",\n    booktitle = "Third Workshop on Very Large Corpora",\n    year = "1995",\n    url = "https://www.aclweb.org/anthology/W95-0107",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n'

lowercase_			:       int						       =				'\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n'

lowercase_			:       Union[str, Any]						       =				'\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode="strict". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        "warn". "warn" acts as 0, but the warning is raised.\n\nReturns:\n    \'scores\': dict. Summary of the scores for overall and per type\n        Overall:\n            \'accuracy\': accuracy,\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> seqeval = datasets.load_metric("seqeval")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']\n    >>> print(results["overall_f1"])\n    0.5\n    >>> print(results["PER"]["f1"])\n    1.0\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,	_KWARGS_DESCRIPTION				)
class 					__lowerCAmelCase				( datasets.Metric				):







			def UpperCamelCase    ( self					:    Optional[int]   ):




									"""simple docstring"""


									return datasets.MetricInfo(
									    description=_DESCRIPTION					,					citation=_CITATION					,					homepage="https://github.com/chakki-works/seqeval"					,					inputs_description=_KWARGS_DESCRIPTION					,					features=datasets.Features(
									        {
									            "predictions": datasets.Sequence(datasets.Value("string"					,					id="label"   )					,					id="sequence"   ),
									            "references": datasets.Sequence(datasets.Value("string"					,					id="label"   )					,					id="sequence"   ),
									        }   )					,					codebase_urls=["https://github.com/chakki-works/seqeval"]					,					reference_urls=["https://github.com/chakki-works/seqeval"]					,					)







			def UpperCamelCase    ( self					:    List[str]					,					snake_case__					:    Optional[Any]					,					snake_case__					:    Union[str, Any]					,					snake_case__					:    bool = False					,					snake_case__					:    Optional[str] = None					,					snake_case__					:    Optional[str] = None					,					snake_case__					:    Optional[List[int]] = None					,					snake_case__					:    Union[str, int] = "warn"					,					):




									"""simple docstring"""


									if scheme is not None:
															try:
																					_UpperCAmelCase									=  importlib.import_module("seqeval.scheme"   )
																					_UpperCAmelCase									=  getattr(snake_case__					,					snake_case__   )
															except AttributeError:
																					raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""   )
									_UpperCAmelCase									=  classification_report(
									    y_true=snake_case__					,					y_pred=snake_case__					,					suffix=snake_case__					,					output_dict=snake_case__					,					scheme=snake_case__					,					mode=snake_case__					,					sample_weight=snake_case__					,					zero_division=snake_case__					,					)
									report.pop("macro avg"   )
									report.pop("weighted avg"   )
									_UpperCAmelCase									=  report.pop("micro avg"   )

									_UpperCAmelCase									=  {
									    type_name: {
									        "precision": score["precision"],
									        "recall": score["recall"],
									        "f1": score["f1-score"],
									        "number": score["support"],
									    }
									    for type_name, score in report.items()
									}
									_UpperCAmelCase									=  overall_score["precision"]
									_UpperCAmelCase									=  overall_score["recall"]
									_UpperCAmelCase									=  overall_score["f1-score"]
									_UpperCAmelCase									=  accuracy_score(y_true=snake_case__					,					y_pred=snake_case__   )

									return scores










import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RwkvForCausalLM,
				    RwkvModel,
				)
				from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
				lowercase_			:       str						       =				False






class 					__lowerCAmelCase				:







			def __init__( self					:    Optional[Any]					,					snake_case__					:    Union[str, Any]					,					snake_case__					:    Union[str, Any]=14					,					snake_case__					:    Optional[Any]=7					,					snake_case__					:    int=True					,					snake_case__					:    Optional[int]=False					,					snake_case__					:    Any=True					,					snake_case__					:    Tuple=True					,					snake_case__					:    Union[str, Any]=True					,					snake_case__					:    Optional[int]=99					,					snake_case__					:    List[str]=32					,					snake_case__					:    Union[str, Any]=5					,					snake_case__					:    Dict=37					,					snake_case__					:    int="gelu"					,					snake_case__					:    List[str]=0.1					,					snake_case__					:    Dict=0.1					,					snake_case__					:    str=512					,					snake_case__					:    Any=16					,					snake_case__					:    Dict=2					,					snake_case__					:    int=3					,					snake_case__					:    str=4					,					snake_case__					:    int=None					,					):




									"""simple docstring"""


									_UpperCAmelCase									=  parent
									_UpperCAmelCase									=  batch_size
									_UpperCAmelCase									=  seq_length
									_UpperCAmelCase									=  is_training
									_UpperCAmelCase									=  use_token_type_ids
									_UpperCAmelCase									=  use_input_mask
									_UpperCAmelCase									=  use_labels
									_UpperCAmelCase									=  use_mc_token_ids
									_UpperCAmelCase									=  vocab_size
									_UpperCAmelCase									=  hidden_size
									_UpperCAmelCase									=  num_hidden_layers
									_UpperCAmelCase									=  intermediate_size
									_UpperCAmelCase									=  hidden_act
									_UpperCAmelCase									=  hidden_dropout_prob
									_UpperCAmelCase									=  attention_probs_dropout_prob
									_UpperCAmelCase									=  max_position_embeddings
									_UpperCAmelCase									=  type_vocab_size
									_UpperCAmelCase									=  type_sequence_label_size
									_UpperCAmelCase									=  num_labels
									_UpperCAmelCase									=  num_choices
									_UpperCAmelCase									=  scope
									_UpperCAmelCase									=  vocab_size - 1
									_UpperCAmelCase									=  vocab_size - 1
									_UpperCAmelCase									=  vocab_size - 1







			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"   )







			def UpperCamelCase    ( self					:    List[Any]					,					snake_case__					:    Dict=False					,					snake_case__					:    Union[str, Any]=False					,					snake_case__					:    Any=False   ):




									"""simple docstring"""


									_UpperCAmelCase									=  ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size   )

									_UpperCAmelCase									=  None
									if self.use_input_mask:
															_UpperCAmelCase									=  random_attention_mask([self.batch_size, self.seq_length]   )

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

									_UpperCAmelCase									=  None
									if self.use_mc_token_ids:
															_UpperCAmelCase									=  ids_tensor([self.batch_size, self.num_choices]					,					self.seq_length   )

									_UpperCAmelCase									=  None
									_UpperCAmelCase									=  None
									_UpperCAmelCase									=  None
									if self.use_labels:
															_UpperCAmelCase									=  ids_tensor([self.batch_size]					,					self.type_sequence_label_size   )
															_UpperCAmelCase									=  ids_tensor([self.batch_size, self.seq_length]					,					self.num_labels   )
															_UpperCAmelCase									=  ids_tensor([self.batch_size]					,					self.num_choices   )

									_UpperCAmelCase									=  self.get_config(
									    gradient_checkpointing=snake_case__					,					scale_attn_by_inverse_layer_idx=snake_case__					,					reorder_and_upcast_attn=snake_case__					,					)

									return (
									    config,
									    input_ids,
									    input_mask,
									    None,
									    token_type_ids,
									    mc_token_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									)







			def UpperCamelCase    ( self					:    Optional[Any]					,					snake_case__					:    Dict=False					,					snake_case__					:    Dict=False					,					snake_case__					:    Tuple=False   ):




									"""simple docstring"""


									return RwkvConfig(
									    vocab_size=self.vocab_size					,					hidden_size=self.hidden_size					,					num_hidden_layers=self.num_hidden_layers					,					intermediate_size=self.intermediate_size					,					activation_function=self.hidden_act					,					resid_pdrop=self.hidden_dropout_prob					,					attn_pdrop=self.attention_probs_dropout_prob					,					n_positions=self.max_position_embeddings					,					type_vocab_size=self.type_vocab_size					,					use_cache=snake_case__					,					bos_token_id=self.bos_token_id					,					eos_token_id=self.eos_token_id					,					pad_token_id=self.pad_token_id					,					gradient_checkpointing=snake_case__					,					scale_attn_by_inverse_layer_idx=snake_case__					,					reorder_and_upcast_attn=snake_case__					,					)







			def UpperCamelCase    ( self					:    List[Any]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.get_config()
									_UpperCAmelCase									=  300
									return config







			def UpperCamelCase    ( self					:    Union[str, Any]   ):




									"""simple docstring"""


									(
									    (
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				
									)									=  self.prepare_config_and_inputs()

									_UpperCAmelCase									=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
									_UpperCAmelCase									=  ids_tensor([self.batch_size, self.seq_length]					,					vocab_size=2   )

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									    token_type_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)







			def UpperCamelCase    ( self					:    Optional[Any]					,					snake_case__					:    List[str]					,					snake_case__					:    Optional[int]					,					snake_case__					:    int					,					snake_case__					:    Optional[Any]					,					snake_case__					:    str					,					*snake_case__					:    Optional[int]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  True
									_UpperCAmelCase									=  RwkvModel(config=snake_case__   )
									model.to(snake_case__   )
									model.eval()

									_UpperCAmelCase									=  model(snake_case__   )

									self.parent.assertEqual(result.last_hidden_state.shape					,					(self.batch_size, self.seq_length, self.hidden_size)   )
									self.parent.assertEqual(len(result.hidden_states   )					,					config.num_hidden_layers + 1   )







			def UpperCamelCase    ( self					:    List[str]					,					snake_case__					:    str					,					snake_case__					:    Any					,					snake_case__					:    Any					,					snake_case__					:    Optional[Any]					,					snake_case__					:    List[Any]					,					*snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  RwkvForCausalLM(snake_case__   )
									model.to(snake_case__   )
									model.eval()

									_UpperCAmelCase									=  model(snake_case__					,					labels=snake_case__   )
									self.parent.assertEqual(result.loss.shape					,					()   )
									self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.seq_length, self.vocab_size)   )







			def UpperCamelCase    ( self					:    Optional[Any]					,					snake_case__					:    str					,					snake_case__					:    List[Any]					,					snake_case__					:    Tuple					,					snake_case__					:    List[Any]					,					snake_case__					:    Any					,					*snake_case__					:    Tuple   ):




									"""simple docstring"""


									_UpperCAmelCase									=  RwkvModel(config=snake_case__   )
									model.to(snake_case__   )
									model.eval()

									_UpperCAmelCase									=  model(snake_case__   )
									_UpperCAmelCase									=  outputs.last_hidden_state

									_UpperCAmelCase									=  model(input_ids[:, :2]   )
									_UpperCAmelCase									=  outputs.last_hidden_state

									# Using the state computed on the first inputs, we will get the same output
									_UpperCAmelCase									=  model(input_ids[:, 2:]					,					state=outputs.state   )
									_UpperCAmelCase									=  outputs.last_hidden_state

									self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]					,					dim=1   )					,					snake_case__					,					atol=1e-5   )   )







			def UpperCamelCase    ( self					:    Optional[int]					,					snake_case__					:    List[Any]					,					snake_case__					:    Union[str, Any]					,					snake_case__					:    str					,					snake_case__					:    List[str]					,					snake_case__					:    Union[str, Any]					,					*snake_case__					:    Optional[Any]					,					snake_case__					:    Optional[Any]=False   ):




									"""simple docstring"""


									_UpperCAmelCase									=  RwkvForCausalLM(snake_case__   )
									model.to(snake_case__   )
									if gradient_checkpointing:
															model.gradient_checkpointing_enable()

									_UpperCAmelCase									=  model(snake_case__					,					labels=snake_case__   )
									self.parent.assertEqual(result.loss.shape					,					()   )
									self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.seq_length, self.vocab_size)   )
									result.loss.backward()







			def UpperCamelCase    ( self					:    Dict   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.prepare_config_and_inputs()

									(
									    (
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				(
									    _UpperCAmelCase
									)							,				
									)									=  config_and_inputs

									_UpperCAmelCase									=  {"input_ids": input_ids}

									return config, inputs_dict






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0      ,	reason="See https://github.com/huggingface/transformers/pull/24204"				)
@require_torch
class 					__lowerCAmelCase				( UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	unittest.TestCase				):
			snake_case_   :				List[str]				  =  (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
			snake_case_   :				int				  =  (
			    {"feature-extraction": RwkvModel, "text-generation": RwkvForCausalLM} if is_torch_available() else {}
			)
			# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
			snake_case_   :				Tuple				  =  False
			snake_case_   :				Dict				  =  False
			snake_case_   :				int				  =  False
			snake_case_   :				Dict				  =  False
			snake_case_   :				Any				  =  False  # Rwkv does not support head masking







			def UpperCamelCase    ( self					:    Optional[Any]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  RwkvModelTester(self   )
									_UpperCAmelCase									=  ConfigTester(
									    self					,					config_class=snake_case__					,					n_embd=37					,					common_properties=["hidden_size", "num_hidden_layers"]   )







			def UpperCamelCase    ( self					:    Union[str, Any]					,					snake_case__					:    List[str]					,					snake_case__					:    Optional[int]					,					snake_case__					:    Tuple=None   ):




									"""simple docstring"""


									if isinstance(snake_case__					,					torch.Tensor   ):
															_UpperCAmelCase							,				_UpperCAmelCase									=  member.max().item(), member.min().item()
									elif isinstance(snake_case__					,					snake_case__   ) or isinstance(snake_case__					,					snake_case__   ):
															_UpperCAmelCase							,				_UpperCAmelCase									=  max(snake_case__   ), min(snake_case__   )

									if not isinstance(snake_case__					,					snake_case__   ):
															raise TypeError("container should be a list or tuple"   )
									elif len(snake_case__   ) != 2:
															raise ValueError("container should have 2 elements"   )

									_UpperCAmelCase							,				_UpperCAmelCase									=  container

									_UpperCAmelCase									=  (min_value >= expected_min) and (max_value <= expected_max)

									if not is_inside_interval:
															_UpperCAmelCase									=  "%s not found in %s" % (safe_repr(snake_case__   ), safe_repr(snake_case__   ))
															self.fail(self._formatMessage(snake_case__					,					snake_case__   )   )







			def UpperCamelCase    ( self					:    List[Any]   ):




									"""simple docstring"""


									self.config_tester.run_common_tests()







			def UpperCamelCase    ( self					:    Tuple   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_rwkv_model(*snake_case__   )







			def UpperCamelCase    ( self					:    Dict   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_causl_lm(*snake_case__   )







			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_state_equivalency(*snake_case__   )







			def UpperCamelCase    ( self					:    Union[str, Any]   ):




									"""simple docstring"""


									_UpperCAmelCase							,				_UpperCAmelCase									=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															_UpperCAmelCase									=  model_class(config=snake_case__   )
															for name, param in model.named_parameters():
																					if "time_decay" in name:
																											if param.requires_grad:
																																	self.assertTrue(param.data.max().item() == 3.0   )
																																	self.assertTrue(param.data.min().item() == -5.0   )
																					elif "time_first" in name:
																											if param.requires_grad:
																																	# check if it's a ones like
																																	self.assertTrue(torch.allclose(param.data					,					torch.ones_like(param.data   )					,					atol=1e-5					,					rtol=1e-5   )   )
																					elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]   ):
																											if param.requires_grad:
																																	self.assertInterval(
																																	    param.data					,					[0.0, 1.0]					,					msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,					)
																					elif "time_mix_value" in name:
																											if param.requires_grad:
																																	self.assertInterval(
																																	    param.data					,					[0.0, 1.3]					,					msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,					)







			def UpperCamelCase    ( self					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase							,				_UpperCAmelCase									=  self.model_tester.prepare_config_and_inputs_for_common()
									_UpperCAmelCase									=  True

									_UpperCAmelCase									=  getattr(self.model_tester					,					"seq_length"					,					snake_case__   )

									for model_class in self.all_model_classes:
															_UpperCAmelCase									=  True
															_UpperCAmelCase									=  False
															_UpperCAmelCase									=  True
															_UpperCAmelCase									=  model_class(snake_case__   )
															model.to(snake_case__   )
															model.eval()

															_UpperCAmelCase									=  self._prepare_for_class(snake_case__					,					snake_case__   )
															_UpperCAmelCase									=  inputs["input_ids"].shape[0]
															with torch.no_grad():
																					_UpperCAmelCase									=  model(**snake_case__   )
															_UpperCAmelCase									=  outputs.attentions
															self.assertEqual(len(snake_case__   )					,					self.model_tester.num_hidden_layers   )

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															_UpperCAmelCase									=  True
															_UpperCAmelCase									=  model_class(snake_case__   )
															model.to(snake_case__   )
															model.eval()

															_UpperCAmelCase									=  self._prepare_for_class(snake_case__					,					snake_case__   )
															_UpperCAmelCase									=  inputs["input_ids"].shape[0]
															with torch.no_grad():
																					_UpperCAmelCase									=  model(**snake_case__   )
															_UpperCAmelCase									=  outputs.attentions
															self.assertEqual(len(snake_case__   )					,					self.model_tester.num_hidden_layers   )

															self.assertListEqual(
															    list(attentions[0].shape[-3:]   )					,					[batch_size, seq_len, config.hidden_size]					,					)
															_UpperCAmelCase									=  len(snake_case__   )

															# Check attention is always last and order is fine
															_UpperCAmelCase									=  True
															_UpperCAmelCase									=  True
															_UpperCAmelCase									=  model_class(snake_case__   )
															model.to(snake_case__   )
															model.eval()

															_UpperCAmelCase									=  self._prepare_for_class(snake_case__					,					snake_case__   )
															_UpperCAmelCase									=  inputs["input_ids"].shape[0]
															with torch.no_grad():
																					_UpperCAmelCase									=  model(**snake_case__   )

															_UpperCAmelCase									=  1
															self.assertEqual(out_len + added_hidden_states					,					len(snake_case__   )   )

															_UpperCAmelCase									=  outputs.attentions

															self.assertEqual(len(snake_case__   )					,					self.model_tester.num_hidden_layers   )
															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]   )					,					[batch_size, seq_len, config.hidden_size]					,					)







			@slow
			def UpperCamelCase    ( self					:    int   ):




									"""simple docstring"""


									for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_UpperCAmelCase									=  RwkvModel.from_pretrained(snake_case__   )
															self.assertIsNotNone(snake_case__   )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0      ,	reason="See https://github.com/huggingface/transformers/pull/24204"				)
@slow
class 					__lowerCAmelCase				( unittest.TestCase				):







			def UpperCamelCase    ( self					:    List[str]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  "RWKV/rwkv-4-169m-pile"
									_UpperCAmelCase									=  AutoTokenizer.from_pretrained(self.model_id   )







			def UpperCamelCase    ( self					:    Dict   ):




									"""simple docstring"""


									_UpperCAmelCase									=  "Hello my name is Jasmine and I am a newbie to the"
									_UpperCAmelCase									=  RwkvForCausalLM.from_pretrained(self.model_id   ).to(snake_case__   )

									_UpperCAmelCase									=  self.tokenizer("Hello my name is"					,					return_tensors="pt"   ).input_ids.to(snake_case__   )
									_UpperCAmelCase									=  model.generate(snake_case__					,					max_new_tokens=10   )
									_UpperCAmelCase									=  self.tokenizer.decode(output[0].tolist()   )

									self.assertEqual(snake_case__					,					snake_case__   )







			def UpperCamelCase    ( self					:    str   ):




									"""simple docstring"""


									_UpperCAmelCase									=  "Hello my name is Jasmine and I am a newbie to the"

									_UpperCAmelCase									=  self.tokenizer("Hello my name is"					,					return_tensors="pt"   ).input_ids.to(snake_case__   )
									_UpperCAmelCase									=  RwkvForCausalLM.from_pretrained(self.model_id					,					torch_dtype=torch.bfloataa   ).to(snake_case__   )

									_UpperCAmelCase									=  model.generate(snake_case__					,					max_new_tokens=10   )
									_UpperCAmelCase									=  self.tokenizer.decode(output[0].tolist()   )

									self.assertEqual(snake_case__					,					snake_case__   )




