
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_A   =     logging.get_logger(__name__)


_A   =     {
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}





class 	_lowerCAmelCase					(	__SCREAMING_SNAKE_CASE       ):
				_lowercase					='''codegen'''
				_lowercase					={
				    '''max_position_embeddings''': '''n_positions''',
				    '''hidden_size''': '''n_embd''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__(					self				,				_UpperCamelCase=50_400				,				_UpperCamelCase=2_048				,				_UpperCamelCase=2_048				,				_UpperCamelCase=4_096				,				_UpperCamelCase=28				,				_UpperCamelCase=16				,				_UpperCamelCase=64				,				_UpperCamelCase=None				,				_UpperCamelCase="gelu_new"				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.0				,				_UpperCamelCase=1e-5				,				_UpperCamelCase=0.02				,				_UpperCamelCase=True				,				_UpperCamelCase=50_256				,				_UpperCamelCase=50_256				,				_UpperCamelCase=False				,				**_UpperCamelCase				,				)				->      Union[str, Any]:
							lowerCAmelCase_						=	vocab_size
							lowerCAmelCase_						=	n_ctx
							lowerCAmelCase_						=	n_positions
							lowerCAmelCase_						=	n_embd
							lowerCAmelCase_						=	n_layer
							lowerCAmelCase_						=	n_head
							lowerCAmelCase_						=	n_inner
							lowerCAmelCase_						=	rotary_dim
							lowerCAmelCase_						=	activation_function
							lowerCAmelCase_						=	resid_pdrop
							lowerCAmelCase_						=	embd_pdrop
							lowerCAmelCase_						=	attn_pdrop
							lowerCAmelCase_						=	layer_norm_epsilon
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	use_cache

							lowerCAmelCase_						=	bos_token_id
							lowerCAmelCase_						=	eos_token_id

							super().__init__(
							    bos_token_id=_SCREAMING_SNAKE_CASE				,				eos_token_id=_SCREAMING_SNAKE_CASE				,				tie_word_embeddings=_SCREAMING_SNAKE_CASE				,				**_SCREAMING_SNAKE_CASE     )





class 	_lowerCAmelCase					(	__SCREAMING_SNAKE_CASE       ):




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase = "default"				,				_UpperCamelCase = None				,				_UpperCamelCase = False				,				)				->      Union[str, Any]:
							super().__init__(_SCREAMING_SNAKE_CASE				,				task=_SCREAMING_SNAKE_CASE				,				patching_specs=_SCREAMING_SNAKE_CASE				,				use_past=_SCREAMING_SNAKE_CASE     )
							if not getattr(self._config				,				"pad_token_id"				,				_SCREAMING_SNAKE_CASE     ):
										# TODO: how to do that better?
										lowerCAmelCase_						=	0




				@property
				def      __a						(					self     )				->      Mapping[str, Mapping[int, str]]:
							lowerCAmelCase_						=	OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
							if self.use_past:
										self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE				,				direction="inputs"     )
										lowerCAmelCase_						=	{0: "batch", 1: "past_sequence + sequence"}
							else:
										lowerCAmelCase_						=	{0: "batch", 1: "sequence"}

							return common_inputs




				@property
				def      __a						(					self     )				->      int:
							return self._config.n_layer




				@property
				def      __a						(					self     )				->      int:
							return self._config.n_head




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase = -1				,				_UpperCamelCase = -1				,				_UpperCamelCase = False				,				_UpperCamelCase = None				,				)				->      Mapping[str, Any]:
							lowerCAmelCase_						=	super(_SCREAMING_SNAKE_CASE				,				self     ).generate_dummy_inputs(
							    _SCREAMING_SNAKE_CASE				,				batch_size=_SCREAMING_SNAKE_CASE				,				seq_length=_SCREAMING_SNAKE_CASE				,				is_pair=_SCREAMING_SNAKE_CASE				,				framework=_SCREAMING_SNAKE_CASE     )

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase_						=	OrderedDict({"input_ids": common_inputs["input_ids"]}     )

							# Need to add the past_keys
							if self.use_past:
										if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."     )
										else:
													import torch

													lowerCAmelCase_						=	common_inputs["input_ids"].shape
													# Not using the same length for past_key_values
													lowerCAmelCase_						=	seqlen + 2
													lowerCAmelCase_						=	(
													    batch,
													    self.num_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // self.num_attention_heads,
													)
													lowerCAmelCase_						=	[
													    (torch.zeros(_SCREAMING_SNAKE_CASE     ), torch.zeros(_SCREAMING_SNAKE_CASE     )) for _ in range(self.num_layers     )
													]

							lowerCAmelCase_						=	common_inputs["attention_mask"]
							if self.use_past:
										lowerCAmelCase_						=	ordered_inputs["attention_mask"].dtype
										lowerCAmelCase_						=	torch.cat(
										    [ordered_inputs["attention_mask"], torch.ones(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE				,				dtype=_SCREAMING_SNAKE_CASE     )]				,				dim=1     )

							return ordered_inputs




				@property
				def      __a						(					self     )				->      int:
							return 13








"""simple docstring"""






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 			A__   (  __SCREAMING_SNAKE_CASE  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	FunnelTokenizer
	SCREAMING_SNAKE_CASE          =	FunnelTokenizerFast
	SCREAMING_SNAKE_CASE          =	True
	SCREAMING_SNAKE_CASE          =	True

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Optional[int]:

				"""simple docstring"""

				super().setUp()

				__lowerCAmelCase      :  str												=       [
				    "<unk>",
				    "<cls>",
				    "<sep>",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				__lowerCAmelCase      :  int												=       os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["vocab_file"])
				with open(self.vocab_file       ,			"w"       ,			encoding="utf-8") as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			**_SCREAMING_SNAKE_CASE:		Union[str, Any])				->       int:

				"""simple docstring"""

				return FunnelTokenizer.from_pretrained(self.tmpdirname       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			**_SCREAMING_SNAKE_CASE:		Any)				->       str:

				"""simple docstring"""

				return FunnelTokenizerFast.from_pretrained(self.tmpdirname       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		str)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       "UNwant\u00E9d,running"
				__lowerCAmelCase      :  str												=       "unwanted, running"
				return input_text, output_text

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.tokenizer_class(self.vocab_file)

				__lowerCAmelCase      :  Any												=       tokenizer.tokenize("UNwant\u00E9d,running")
				self.assertListEqual(_SCREAMING_SNAKE_CASE       ,			["un", "##want", "##ed", ",", "runn", "##ing"])
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE)       ,			[7, 4, 5, 10, 8, 9])







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE)
				for tokenizer in tokenizers:
							__lowerCAmelCase      :  List[str]												=       tokenizer("UNwant\u00E9d,running")
							__lowerCAmelCase      :  Optional[int]												=       len(inputs["input_ids"]) - 1
							self.assertListEqual(inputs["token_type_ids"]       ,			[2] + [0] * sentence_len)

							__lowerCAmelCase      :  List[str]												=       tokenizer("UNwant\u00E9d,running"       ,			"UNwant\u00E9d,running")
							self.assertListEqual(inputs["token_type_ids"]       ,			[2] + [0] * sentence_len + [1] * sentence_len)