



import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
					import os

					from flax.core.frozen_dict import unfreeze
					from flax.traverse_util import flatten_dict

					from transformers import FlaxBertModel

					lowerCAmelCase_           = '0.12'  # assumed parallelism: 8
@require_flax
@is_staging_test
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		@classmethod
		def       A(  cls		:       int		)    ->   int:


						'''simple docstring'''
						A	 =      TOKEN
						HfFolder.save_token(_SCREAMING_SNAKE_CASE		)
		@classmethod
		def       A(  cls		:       str		)    ->   int:


						'''simple docstring'''
						try:
										delete_repo(token=cls._token						,repo_id='test-model-flax'		)
						except HTTPError:
										pass

						try:
										delete_repo(token=cls._token						,repo_id='valid_org/test-model-flax-org'		)
						except HTTPError:
										pass
		def       A(  self		:       Union[str, Any]		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      BertConfig(
						    vocab_size=9_9						,hidden_size=3_2						,num_hidden_layers=5						,num_attention_heads=4						,intermediate_size=3_7		)
						A	 =      FlaxBertModel(_SCREAMING_SNAKE_CASE		)
						model.push_to_hub('test-model-flax'						,use_auth_token=self._token		)

						A	 =      FlaxBertModel.from_pretrained(f'{USER}/test-model-flax'		)

						A	 =      flatten_dict(unfreeze(model.params		)		)
						A	 =      flatten_dict(unfreeze(new_model.params		)		)

						for key in base_params.keys():
										A	 =      (base_params[key] - new_params[key]).sum().item()
										self.assertLessEqual(_SCREAMING_SNAKE_CASE						,1E-3						,msg=f'{key} not identical'		)

						# Reset repo
						delete_repo(token=self._token						,repo_id='test-model-flax'		)

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
										model.save_pretrained(_SCREAMING_SNAKE_CASE						,repo_id='test-model-flax'						,push_to_hub=_SCREAMING_SNAKE_CASE						,use_auth_token=self._token		)

						A	 =      FlaxBertModel.from_pretrained(f'{USER}/test-model-flax'		)

						A	 =      flatten_dict(unfreeze(model.params		)		)
						A	 =      flatten_dict(unfreeze(new_model.params		)		)

						for key in base_params.keys():
										A	 =      (base_params[key] - new_params[key]).sum().item()
										self.assertLessEqual(_SCREAMING_SNAKE_CASE						,1E-3						,msg=f'{key} not identical'		)







		def       A(  self		:       str		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      BertConfig(
						    vocab_size=9_9						,hidden_size=3_2						,num_hidden_layers=5						,num_attention_heads=4						,intermediate_size=3_7		)
						A	 =      FlaxBertModel(_SCREAMING_SNAKE_CASE		)
						model.push_to_hub('valid_org/test-model-flax-org'						,use_auth_token=self._token		)

						A	 =      FlaxBertModel.from_pretrained('valid_org/test-model-flax-org'		)

						A	 =      flatten_dict(unfreeze(model.params		)		)
						A	 =      flatten_dict(unfreeze(new_model.params		)		)

						for key in base_params.keys():
										A	 =      (base_params[key] - new_params[key]).sum().item()
										self.assertLessEqual(_SCREAMING_SNAKE_CASE						,1E-3						,msg=f'{key} not identical'		)

						# Reset repo
						delete_repo(token=self._token						,repo_id='valid_org/test-model-flax-org'		)

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
										model.save_pretrained(
										    _SCREAMING_SNAKE_CASE						,repo_id='valid_org/test-model-flax-org'						,push_to_hub=_SCREAMING_SNAKE_CASE						,use_auth_token=self._token		)

						A	 =      FlaxBertModel.from_pretrained('valid_org/test-model-flax-org'		)

						A	 =      flatten_dict(unfreeze(model.params		)		)
						A	 =      flatten_dict(unfreeze(new_model.params		)		)

						for key in base_params.keys():
										A	 =      (base_params[key] - new_params[key]).sum().item()
										self.assertLessEqual(_SCREAMING_SNAKE_CASE						,1E-3						,msg=f'{key} not identical'		)







def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[str]   ):
				A	 =      True
				A	 =      flatten_dict(modela.params   )
				A	 =      flatten_dict(modela.params   )
				for key in flat_params_a.keys():
								if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]   )   ) > 1E-4:
												A	 =      False

				return models_are_equal



@require_flax
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		def       A(  self		:       Union[str, Any]		)    ->   Tuple:


						'''simple docstring'''
						A	 =      BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only'		)
						A	 =      FlaxBertModel(_SCREAMING_SNAKE_CASE		)

						A	 =      'bert'
						with tempfile.TemporaryDirectory() as tmp_dir:
										model.save_pretrained(os.path.join(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)		)

										with self.assertRaises(_SCREAMING_SNAKE_CASE		):
														A	 =      FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE		)

										A	 =      FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE						,subfolder=_SCREAMING_SNAKE_CASE		)

						self.assertTrue(check_models_equal(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)		)
		def       A(  self		:       List[str]		)    ->   Dict:


						'''simple docstring'''
						A	 =      BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only'		)
						A	 =      FlaxBertModel(_SCREAMING_SNAKE_CASE		)

						A	 =      'bert'
						with tempfile.TemporaryDirectory() as tmp_dir:
										model.save_pretrained(os.path.join(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)						,max_shard_size='10KB'		)

										with self.assertRaises(_SCREAMING_SNAKE_CASE		):
														A	 =      FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE		)

										A	 =      FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE						,subfolder=_SCREAMING_SNAKE_CASE		)

						self.assertTrue(check_models_equal(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)		)
		def       A(  self		:       Optional[Any]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      'bert'
						A	 =      'hf-internal-testing/tiny-random-bert-subfolder'

						with self.assertRaises(_SCREAMING_SNAKE_CASE		):
										A	 =      FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE		)

						A	 =      FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE						,subfolder=_SCREAMING_SNAKE_CASE		)

						self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)







		def       A(  self		:       str		)    ->   Dict:


						'''simple docstring'''
						A	 =      'bert'
						A	 =      'hf-internal-testing/tiny-random-bert-sharded-subfolder'
						with self.assertRaises(_SCREAMING_SNAKE_CASE		):
										A	 =      FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE		)

						A	 =      FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE						,subfolder=_SCREAMING_SNAKE_CASE		)

						self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)













import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


lowerCAmelCase_           = logging.get_logger(__name__)

lowerCAmelCase_           = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_           = {
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_           = {
    'gpt-neox-20b': 2_048,
}
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   VOCAB_FILES_NAMES
		snake_case    =   PRETRAINED_VOCAB_FILES_MAP
		snake_case    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case    =   ["input_ids", "attention_mask"]
		def __init__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Dict="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       int="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       Optional[int]="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       List[Any]=False						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,)    ->   int:


						'''simple docstring'''
						super().__init__(
						    _SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,tokenizer_file=_SCREAMING_SNAKE_CASE						,unk_token=_SCREAMING_SNAKE_CASE						,bos_token=_SCREAMING_SNAKE_CASE						,eos_token=_SCREAMING_SNAKE_CASE						,add_prefix_space=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)

						A	 =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
						if pre_tok_state.get('add_prefix_space'						,_SCREAMING_SNAKE_CASE		) != add_prefix_space:
										A	 =      getattr(_SCREAMING_SNAKE_CASE						,pre_tok_state.pop('type'		)		)
										A	 =      add_prefix_space
										A	 =      pre_tok_class(**_SCREAMING_SNAKE_CASE		)

						A	 =      add_prefix_space
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[str] = None		)    ->   Tuple[str]:


						'''simple docstring'''
						A	 =      self._tokenizer.model.save(_SCREAMING_SNAKE_CASE						,name=_SCREAMING_SNAKE_CASE		)
						return tuple(_SCREAMING_SNAKE_CASE		)







		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       "Conversation"		)    ->   List[int]:


						'''simple docstring'''
						A	 =      []
						for is_user, text in conversation.iter_texts():
										input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE		) + [self.eos_token_id]		)

						if len(_SCREAMING_SNAKE_CASE		) > self.model_max_length:
										A	 =      input_ids[-self.model_max_length :]
						return input_ids





