



import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					CodeGenTokenizer
			UpperCamelCase        =					CodeGenTokenizerFast
			UpperCamelCase        =					True
			UpperCamelCase        =					{'''add_prefix_space''': True}
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  Dict)       ->   Tuple:



						"""simple docstring"""






						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						_UpperCAmelCase			=       [
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    '\u0120',
						    '\u0120l',
						    '\u0120n',
						    '\u0120lo',
						    '\u0120low',
						    'er',
						    '\u0120lowest',
						    '\u0120newer',
						    '\u0120wider',
						    '<unk>',
						    '<|endoftext|>',
						]
						_UpperCAmelCase			=       dict(zip(A			,		range(len(A))))
						_UpperCAmelCase			=       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
						_UpperCAmelCase			=       {'unk_token': '<unk>'}

						_UpperCAmelCase			=       os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file'])
						_UpperCAmelCase			=       os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['merges_file'])
						with open(self.vocab_file			,		'w'			,		encoding='utf-8') as fp:
									fp.write(json.dumps(A) + '\n')
						with open(self.merges_file			,		'w'			,		encoding='utf-8') as fp:
									fp.write('\n'.join(A))



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



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return CodeGenTokenizer.from_pretrained(self.tmpdirname			,		**A)



			def    _lowerCamelCase	(						self       :  Tuple			,		**A       :  Any)       ->   List[str]:



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return CodeGenTokenizerFast.from_pretrained(self.tmpdirname			,		**A)



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



						"""simple docstring"""






						_UpperCAmelCase			=       'lower newer'
						_UpperCAmelCase			=       'lower newer'
						return input_text, output_text



			def    _lowerCamelCase	(						self       :  Dict)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       CodeGenTokenizer(self.vocab_file			,		self.merges_file			,		**self.special_tokens_map)
						_UpperCAmelCase			=       'lower newer'
						_UpperCAmelCase			=       ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
						_UpperCAmelCase			=       tokenizer.tokenize(A			,		add_prefix_space=A)
						self.assertListEqual(A			,		A)

						_UpperCAmelCase			=       tokens + [tokenizer.unk_token]
						_UpperCAmelCase			=       [14, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(A)			,		A)



			def    _lowerCamelCase	(						self       :  int)       ->   List[Any]:



						"""simple docstring"""






						if not self.test_rust_tokenizer:
									return

						_UpperCAmelCase			=       self.get_tokenizer()
						_UpperCAmelCase			=       self.get_rust_tokenizer(add_prefix_space=A)

						_UpperCAmelCase			=       'lower newer'

						# Testing tokenization
						_UpperCAmelCase			=       tokenizer.tokenize(A			,		add_prefix_space=A)
						_UpperCAmelCase			=       rust_tokenizer.tokenize(A)
						self.assertListEqual(A			,		A)

						# Testing conversion to ids without special tokens
						_UpperCAmelCase			=       tokenizer.encode(A			,		add_special_tokens=A			,		add_prefix_space=A)
						_UpperCAmelCase			=       rust_tokenizer.encode(A			,		add_special_tokens=A)
						self.assertListEqual(A			,		A)

						# Testing conversion to ids with special tokens
						_UpperCAmelCase			=       self.get_rust_tokenizer(add_prefix_space=A)
						_UpperCAmelCase			=       tokenizer.encode(A			,		add_prefix_space=A)
						_UpperCAmelCase			=       rust_tokenizer.encode(A)
						self.assertListEqual(A			,		A)

						# Testing the unknown token
						_UpperCAmelCase			=       tokens + [rust_tokenizer.unk_token]
						_UpperCAmelCase			=       [14, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A)			,		A)



			def    _lowerCamelCase	(						self       :  int			,		*A       :  int			,		**A       :  List[str])       ->   List[str]:



						"""simple docstring"""






						pass



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  Optional[Any]=15)       ->   int:



						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase			=       self.rust_tokenizer_class.from_pretrained(A			,		**A)

												# Simple input
												_UpperCAmelCase			=       'This is a simple input'
												_UpperCAmelCase			=       ['This is a simple input 1', 'This is a simple input 2']
												_UpperCAmelCase			=       ('This is a simple input', 'This is a pair')
												_UpperCAmelCase			=       [
												    ('This is a simple input 1', 'This is a simple input 2'),
												    ('This is a simple pair 1', 'This is a simple pair 2'),
												]

												# Simple input tests
												self.assertRaises(A			,		tokenizer_r.encode			,		A			,		max_length=A			,		padding='max_length')

												# Simple input
												self.assertRaises(A			,		tokenizer_r.encode_plus			,		A			,		max_length=A			,		padding='max_length')

												# Simple input
												self.assertRaises(
												    A			,		tokenizer_r.batch_encode_plus			,		A			,		max_length=A			,		padding='max_length'			,		)

												# Pair input
												self.assertRaises(A			,		tokenizer_r.encode			,		A			,		max_length=A			,		padding='max_length')

												# Pair input
												self.assertRaises(A			,		tokenizer_r.encode_plus			,		A			,		max_length=A			,		padding='max_length')

												# Pair input
												self.assertRaises(
												    A			,		tokenizer_r.batch_encode_plus			,		A			,		max_length=A			,		padding='max_length'			,		)



			def    _lowerCamelCase	(						self       :  Dict)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       CodeGenTokenizer.from_pretrained(self.tmpdirname			,		pad_token='<pad>')

						# Simple input
						_UpperCAmelCase			=       'This is a simple input'
						_UpperCAmelCase			=       ['This is a simple input looooooooong', 'This is a simple input']
						_UpperCAmelCase			=       ('This is a simple input', 'This is a pair')
						_UpperCAmelCase			=       [
						    ('This is a simple input loooooong', 'This is a simple input'),
						    ('This is a simple pair loooooong', 'This is a simple pair'),
						]

						_UpperCAmelCase			=       tokenizer.pad_token_id

						_UpperCAmelCase			=       tokenizer(A			,		padding='max_length'			,		max_length=30			,		return_tensors='np')
						_UpperCAmelCase			=       tokenizer(A			,		padding=A			,		truncate=A			,		return_tensors='np')
						_UpperCAmelCase			=       tokenizer(*A			,		padding='max_length'			,		max_length=60			,		return_tensors='np')
						_UpperCAmelCase			=       tokenizer(A			,		padding=A			,		truncate=A			,		return_tensors='np')

						# s
						# test single string max_length padding
						self.assertEqual(out_s['input_ids'].shape[-1]			,		30)
						self.assertTrue(pad_token_id in out_s['input_ids'])
						self.assertTrue(0 in out_s['attention_mask'])

						# s2
						# test automatic padding
						self.assertEqual(out_sa['input_ids'].shape[-1]			,		33)
						# long slice doesn't have padding
						self.assertFalse(pad_token_id in out_sa['input_ids'][0])
						self.assertFalse(0 in out_sa['attention_mask'][0])
						# short slice does have padding
						self.assertTrue(pad_token_id in out_sa['input_ids'][1])
						self.assertTrue(0 in out_sa['attention_mask'][1])

						# p
						# test single pair max_length padding
						self.assertEqual(out_p['input_ids'].shape[-1]			,		60)
						self.assertTrue(pad_token_id in out_p['input_ids'])
						self.assertTrue(0 in out_p['attention_mask'])

						# p2
						# test automatic padding pair
						self.assertEqual(out_pa['input_ids'].shape[-1]			,		52)
						# long slice pair doesn't have padding
						self.assertFalse(pad_token_id in out_pa['input_ids'][0])
						self.assertFalse(0 in out_pa['attention_mask'][0])
						# short slice pair does have padding
						self.assertTrue(pad_token_id in out_pa['input_ids'][1])
						self.assertTrue(0 in out_pa['attention_mask'][1])



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



						"""simple docstring"""






						_UpperCAmelCase			=       '$$$'
						_UpperCAmelCase			=       CodeGenTokenizer.from_pretrained(self.tmpdirname			,		bos_token=A			,		add_bos_token=A)

						_UpperCAmelCase			=       'This is a simple input'
						_UpperCAmelCase			=       ['This is a simple input 1', 'This is a simple input 2']

						_UpperCAmelCase			=       tokenizer.bos_token_id

						_UpperCAmelCase			=       tokenizer(A)
						_UpperCAmelCase			=       tokenizer(A)

						self.assertEqual(out_s.input_ids[0]			,		A)
						self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))

						_UpperCAmelCase			=       tokenizer.decode(out_s.input_ids)
						_UpperCAmelCase			=       tokenizer.batch_decode(out_sa.input_ids)

						self.assertEqual(decode_s.split()[0]			,		A)
						self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))



			@slow
			def    _lowerCamelCase	(						self       :  int)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono')

						_UpperCAmelCase			=       '\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
						_UpperCAmelCase			=       '\nif len_a > len_b:      result = a\nelse:      result = b'

						_UpperCAmelCase			=       tokenizer.encode(A)
						_UpperCAmelCase			=       ['^#', re.escape('<|endoftext|>'), '^\'\'\'', '^"""', '\n\n\n']
						_UpperCAmelCase			=       tokenizer.decode(A			,		truncate_before_pattern=A)
						self.assertEqual(A			,		A)



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[Any]:



						"""simple docstring"""






						pass





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


UpperCAmelCase__       =	r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"







@add_start_docstrings(A						)
class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''rag'''
			UpperCamelCase        =					True



			def __init__(						self       :  str			,		A       :  int=None			,		A       :  Dict=True			,		A       :  int=None			,		A       :  Any=None			,		A       :  Any=None			,		A       :  Any=None			,		A       :  int=None			,		A       :  str=" / "			,		A       :  int=" // "			,		A       :  Any=5			,		A       :  Optional[Any]=3_00			,		A       :  Optional[Any]=7_68			,		A       :  Optional[int]=8			,		A       :  Union[str, Any]="wiki_dpr"			,		A       :  int="train"			,		A       :  Tuple="compressed"			,		A       :  str=None			,		A       :  Any=None			,		A       :  Tuple=False			,		A       :  List[str]=False			,		A       :  Tuple=0.0			,		A       :  Any=True			,		A       :  int=False			,		A       :  Dict=False			,		A       :  List[str]=False			,		A       :  Optional[Any]=True			,		A       :  Tuple=None			,		**A       :  Optional[Any]			,		)       ->   Optional[Any]:



						"""simple docstring"""






						super().__init__(
						    bos_token_id=A			,		pad_token_id=A			,		eos_token_id=A			,		decoder_start_token_id=A			,		forced_eos_token_id=A			,		is_encoder_decoder=A			,		prefix=A			,		vocab_size=A			,		**A			,		)
						assert (
						    "question_encoder" in kwargs and "generator" in kwargs
						), "Config has to be initialized with question_encoder and generator config"
						_UpperCAmelCase			=       kwargs.pop('question_encoder')
						_UpperCAmelCase			=       question_encoder_config.pop('model_type')
						_UpperCAmelCase			=       kwargs.pop('generator')
						_UpperCAmelCase			=       decoder_config.pop('model_type')

						from ..auto.configuration_auto import AutoConfig

						_UpperCAmelCase			=       AutoConfig.for_model(A			,		**A)
						_UpperCAmelCase			=       AutoConfig.for_model(A			,		**A)

						_UpperCAmelCase			=       reduce_loss
						_UpperCAmelCase			=       label_smoothing
						_UpperCAmelCase			=       exclude_bos_score
						_UpperCAmelCase			=       do_marginalize

						_UpperCAmelCase			=       title_sep
						_UpperCAmelCase			=       doc_sep
						_UpperCAmelCase			=       n_docs
						_UpperCAmelCase			=       max_combined_length

						_UpperCAmelCase			=       dataset
						_UpperCAmelCase			=       dataset_split
						_UpperCAmelCase			=       index_name

						_UpperCAmelCase			=       retrieval_vector_size
						_UpperCAmelCase			=       retrieval_batch_size
						_UpperCAmelCase			=       passages_path
						_UpperCAmelCase			=       index_path
						_UpperCAmelCase			=       use_dummy_dataset

						_UpperCAmelCase			=       output_retrieved

						_UpperCAmelCase			=       do_deduplication

						_UpperCAmelCase			=       use_cache

						if self.forced_eos_token_id is None:
									_UpperCAmelCase			=       getattr(self.generator			,		'forced_eos_token_id'			,		A)



			@classmethod
			def    _lowerCamelCase	(						cls       :  Union[str, Any]			,		A       :  PretrainedConfig			,		A       :  PretrainedConfig			,		**A       :  Optional[Any])       ->   PretrainedConfig:



						"""simple docstring"""






						return cls(question_encoder=question_encoder_config.to_dict()			,		generator=generator_config.to_dict()			,		**A)



			def    _lowerCamelCase	(						self       :  Dict)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       copy.deepcopy(self.__dict__)
						_UpperCAmelCase			=       self.question_encoder.to_dict()
						_UpperCAmelCase			=       self.generator.to_dict()
						_UpperCAmelCase			=       self.__class__.model_type
						return output
