




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
					if is_torch_available():
										import torch
					if is_tf_available():
										import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


lowercase_			:       List[str]						       =				logging.get_logger(__name__)

lowercase_			:       List[str]						       =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowercase_			:       int						       =				{
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
}

lowercase_			:       Any						       =				{
    'Salesforce/codegen-350M-mono': 20_48,
}


@lru_cache()
def 							__SCREAMING_SNAKE_CASE						(    ):

						'''simple docstring'''


						_UpperCAmelCase									=  (
						    list(range(ord("!"		)			,					ord("~"		) + 1		)		) + list(range(ord("¡"		)			,					ord("¬"		) + 1		)		) + list(range(ord("®"		)			,					ord("ÿ"		) + 1		)		)
						)
						_UpperCAmelCase									=  bs[:]
						_UpperCAmelCase									=  0
						for b in range(2**8		):
												if b not in bs:
																		bs.append(snake_case_		)
																		cs.append(2**8 + n		)
																		n += 1
						_UpperCAmelCase									=  [chr(snake_case_		) for n in cs]
						return dict(zip(snake_case_			,					snake_case_		)		)


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  set()
						_UpperCAmelCase									=  word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)		)
												_UpperCAmelCase									=  char
						return pairs






class 					__lowerCAmelCase				( UpperCAmelCase__				):
			snake_case_   :				int				  =  VOCAB_FILES_NAMES
			snake_case_   :				str				  =  PRETRAINED_VOCAB_FILES_MAP
			snake_case_   :				List[str]				  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_   :				Tuple				  =  ["input_ids", "attention_mask"]







			def __init__( self					:    List[str]					,					snake_case__					:    Any					,					snake_case__					:    Union[str, Any]					,					snake_case__					:    str="replace"					,					snake_case__					:    List[str]="<|endoftext|>"					,					snake_case__					:    Optional[int]="<|endoftext|>"					,					snake_case__					:    Tuple="<|endoftext|>"					,					snake_case__					:    Any=None					,					snake_case__					:    Tuple=False					,					snake_case__					:    List[Any]=False					,					**snake_case__					:    Dict					,					):




									"""simple docstring"""


									_UpperCAmelCase									=  AddedToken(snake_case__					,					lstrip=snake_case__					,					rstrip=snake_case__   ) if isinstance(snake_case__					,					snake_case__   ) else bos_token
									_UpperCAmelCase									=  AddedToken(snake_case__					,					lstrip=snake_case__					,					rstrip=snake_case__   ) if isinstance(snake_case__					,					snake_case__   ) else eos_token
									_UpperCAmelCase									=  AddedToken(snake_case__					,					lstrip=snake_case__					,					rstrip=snake_case__   ) if isinstance(snake_case__					,					snake_case__   ) else unk_token
									_UpperCAmelCase									=  AddedToken(snake_case__					,					lstrip=snake_case__					,					rstrip=snake_case__   ) if isinstance(snake_case__					,					snake_case__   ) else pad_token
									super().__init__(
									    errors=snake_case__					,					unk_token=snake_case__					,					bos_token=snake_case__					,					eos_token=snake_case__					,					pad_token=snake_case__					,					add_prefix_space=snake_case__					,					add_bos_token=snake_case__					,					**snake_case__					,					)
									_UpperCAmelCase									=  add_bos_token

									with open(snake_case__					,					encoding="utf-8"   ) as vocab_handle:
															_UpperCAmelCase									=  json.load(snake_case__   )
									_UpperCAmelCase									=  {v: k for k, v in self.encoder.items()}
									_UpperCAmelCase									=  errors  # how to handle errors in decoding
									_UpperCAmelCase									=  bytes_to_unicode()
									_UpperCAmelCase									=  {v: k for k, v in self.byte_encoder.items()}
									with open(snake_case__					,					encoding="utf-8"   ) as merges_handle:
															_UpperCAmelCase									=  merges_handle.read().split("\n"   )[1:-1]
									_UpperCAmelCase									=  [tuple(merge.split()   ) for merge in bpe_merges]
									_UpperCAmelCase									=  dict(zip(snake_case__					,					range(len(snake_case__   )   )   )   )
									_UpperCAmelCase									=  {}
									_UpperCAmelCase									=  add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									_UpperCAmelCase									=  re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"   )







			@property
			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									return len(self.encoder   )







			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									return dict(self.encoder					,					**self.added_tokens_encoder   )







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




									"""simple docstring"""


									if token in self.cache:
															return self.cache[token]
									_UpperCAmelCase									=  tuple(snake_case__   )
									_UpperCAmelCase									=  get_pairs(snake_case__   )

									if not pairs:
															return token

									while True:
															_UpperCAmelCase									=  min(snake_case__					,					key=lambda snake_case__   : self.bpe_ranks.get(snake_case__					,					float("inf"   )   )   )
															if bigram not in self.bpe_ranks:
																					break
															_UpperCAmelCase							,				_UpperCAmelCase									=  bigram
															_UpperCAmelCase									=  []
															_UpperCAmelCase									=  0
															while i < len(snake_case__   ):
																					try:
																											_UpperCAmelCase									=  word.index(snake_case__					,					snake_case__   )
																					except ValueError:
																											new_word.extend(word[i:]   )
																											break
																					else:
																											new_word.extend(word[i:j]   )
																											_UpperCAmelCase									=  j

																					if word[i] == first and i < len(snake_case__   ) - 1 and word[i + 1] == second:
																											new_word.append(first + second   )
																											i += 2
																					else:
																											new_word.append(word[i]   )
																											i += 1
															_UpperCAmelCase									=  tuple(snake_case__   )
															_UpperCAmelCase									=  new_word
															if len(snake_case__   ) == 1:
																					break
															else:
																					_UpperCAmelCase									=  get_pairs(snake_case__   )
									_UpperCAmelCase									=  " ".join(snake_case__   )
									_UpperCAmelCase									=  word
									return word







			def UpperCamelCase    ( self					:    Optional[Any]					,					snake_case__					:    int					,					snake_case__					:    Any=None   ):




									"""simple docstring"""


									if self.add_bos_token:
															_UpperCAmelCase									=  [self.bos_token_id]
									else:
															_UpperCAmelCase									=  []

									_UpperCAmelCase									=  bos_token_ids + token_ids_a

									if token_ids_a is None:
															return output

									return output + bos_token_ids + token_ids_a







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




									"""simple docstring"""


									_UpperCAmelCase									=  []
									for token in re.findall(self.pat					,					snake_case__   ):
															_UpperCAmelCase									=  "".join(
															    self.byte_encoder[b] for b in token.encode("utf-8"   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case__   ).split(" "   )   )
									return bpe_tokens







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




									"""simple docstring"""


									return self.encoder.get(snake_case__					,					self.encoder.get(self.unk_token   )   )







			def UpperCamelCase    ( self					:    List[str]					,					snake_case__					:    List[str]   ):




									"""simple docstring"""


									return self.decoder.get(snake_case__   )







			def UpperCamelCase    ( self					:    Tuple					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  "".join(snake_case__   )
									_UpperCAmelCase									=  bytearray([self.byte_decoder[c] for c in text]   ).decode("utf-8"					,					errors=self.errors   )
									return text







			def UpperCamelCase    ( self					:    int					,					snake_case__					:    str					,					snake_case__					:    Optional[str] = None   ):




									"""simple docstring"""


									if not os.path.isdir(snake_case__   ):
															logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
															return
									_UpperCAmelCase									=  os.path.join(
									    snake_case__					,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
									_UpperCAmelCase									=  os.path.join(
									    snake_case__					,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]   )

									with open(snake_case__					,					"w"					,					encoding="utf-8"   ) as f:
															f.write(json.dumps(self.encoder					,					indent=2					,					sort_keys=snake_case__					,					ensure_ascii=snake_case__   ) + "\n"   )

									_UpperCAmelCase									=  0
									with open(snake_case__					,					"w"					,					encoding="utf-8"   ) as writer:
															writer.write("#version: 0.2\n"   )
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,					key=lambda snake_case__   : kv[1]   ):
																					if index != token_index:
																											logger.warning(
																											    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																											    " Please check that the tokenizer is not corrupted!"   )
																											_UpperCAmelCase									=  token_index
																					writer.write(" ".join(snake_case__   ) + "\n"   )
																					index += 1

									return vocab_file, merge_file







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




									"""simple docstring"""


									_UpperCAmelCase									=  kwargs.pop("add_prefix_space"					,					self.add_prefix_space   )
									if is_split_into_words or add_prefix_space:
															_UpperCAmelCase									=  " " + text
									return (text, kwargs)







			def UpperCamelCase    ( self					:    List[str]					,					snake_case__					:    Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]					,					snake_case__					:    bool = False					,					snake_case__					:    bool = None					,					snake_case__					:    Optional[List[str]] = None					,					**snake_case__					:    str					,					):




									"""simple docstring"""


									_UpperCAmelCase									=  super()._decode(
									    token_ids=snake_case__					,					skip_special_tokens=snake_case__					,					clean_up_tokenization_spaces=snake_case__					,					**snake_case__					,					)

									if truncate_before_pattern is not None and len(snake_case__   ) > 0:
															_UpperCAmelCase									=  self.truncate(snake_case__					,					snake_case__   )

									return decoded_text







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




									"""simple docstring"""


									def find_re(snake_case__					:    Union[str, Any]					,					snake_case__					:    Optional[int]					,					snake_case__					:    Any   ):
															_UpperCAmelCase									=  pattern.search(snake_case__					,					snake_case__   )
															return m.start() if m else -1

									_UpperCAmelCase									=  [re.compile(snake_case__					,					re.MULTILINE   ) for pattern in truncate_before_pattern]

									_UpperCAmelCase									=  list(re.finditer("^print"					,					snake_case__					,					re.MULTILINE   )   )

									if len(snake_case__   ) > 1:
															_UpperCAmelCase									=  completion[: prints[1].start()]

									_UpperCAmelCase									=  list(re.finditer("^def"					,					snake_case__					,					re.MULTILINE   )   )

									if len(snake_case__   ) > 1:
															_UpperCAmelCase									=  completion[: defs[1].start()]

									_UpperCAmelCase									=  0

									_UpperCAmelCase									=  [
									    pos for pos in [find_re(snake_case__					,					snake_case__					,					snake_case__   ) for terminal in terminals] if pos != -1
									]

									if len(snake_case__   ) > 0:
															return completion[: min(snake_case__   )]
									else:
															return completion










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__   )




