



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


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__       =      {
    "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     (		__UpperCAmelCase      ):
     """simple docstring"""






     A__				:		List[Any]			       =    "codegen"
     A__				:		Any			       =    {
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }



     def __init__(     self					:       List[str]				,					_snake_case					:       Optional[int]=5_04_00				,					_snake_case					:       Optional[Any]=20_48				,					_snake_case					:       Union[str, Any]=20_48				,					_snake_case					:       Union[str, Any]=40_96				,					_snake_case					:       List[Any]=28				,					_snake_case					:       str=16				,					_snake_case					:       Optional[Any]=64				,					_snake_case					:       str=None				,					_snake_case					:       Tuple="gelu_new"				,					_snake_case					:       List[str]=0.0				,					_snake_case					:       Dict=0.0				,					_snake_case					:       Dict=0.0				,					_snake_case					:       List[Any]=1E-5				,					_snake_case					:       str=0.02				,					_snake_case					:       Optional[int]=True				,					_snake_case					:       Any=5_02_56				,					_snake_case					:       Optional[Any]=5_02_56				,					_snake_case					:       Tuple=False				,					**_snake_case					:       List[Any]				,					):






        """simple docstring"""


        A__       =    vocab_size
        A__       =    n_ctx
        A__       =    n_positions
        A__       =    n_embd
        A__       =    n_layer
        A__       =    n_head
        A__       =    n_inner
        A__       =    rotary_dim
        A__       =    activation_function
        A__       =    resid_pdrop
        A__       =    embd_pdrop
        A__       =    attn_pdrop
        A__       =    layer_norm_epsilon
        A__       =    initializer_range
        A__       =    use_cache

        A__       =    bos_token_id
        A__       =    eos_token_id

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







class      __lowerCAmelCase     (		__UpperCAmelCase      ):
     """simple docstring"""






     def __init__(     self					:       int				,					_snake_case					:       PretrainedConfig				,					_snake_case					:       str = "default"				,					_snake_case					:       List[PatchingSpec] = None				,					_snake_case					:       bool = False				,					):






        """simple docstring"""


        super().__init__(lowerCAmelCase_				,					task=lowerCAmelCase_				,					patching_specs=lowerCAmelCase_				,					use_past=lowerCAmelCase_					)
        if not getattr(self._config				,					'pad_token_id'				,					lowerCAmelCase_					):
           # TODO: how to do that better?
           A__       =    0



     @property
     def     _a							(     self					:       List[str]					):






        """simple docstring"""


        A__       =    OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}					)
        if self.use_past:
           self.fill_with_past_key_values_(lowerCAmelCase_				,					direction='inputs'					)
           A__       =    {0: 'batch', 1: 'past_sequence + sequence'}
        else:
           A__       =    {0: 'batch', 1: 'sequence'}

        return common_inputs



     @property
     def     _a							(     self					:       List[Any]					):






        """simple docstring"""


        return self._config.n_layer



     @property
     def     _a							(     self					:       Dict					):






        """simple docstring"""


        return self._config.n_head



     def     _a							(     self					:       Any				,					_snake_case					:       PreTrainedTokenizer				,					_snake_case					:       int = -1				,					_snake_case					:       int = -1				,					_snake_case					:       bool = False				,					_snake_case					:       Optional[TensorType] = None				,					):






        """simple docstring"""


        A__       =    super(lowerCAmelCase_				,					self					).generate_dummy_inputs(
            lowerCAmelCase_				,					batch_size=lowerCAmelCase_				,					seq_length=lowerCAmelCase_				,					is_pair=lowerCAmelCase_				,					framework=lowerCAmelCase_					)

        # We need to order the input in the way they appears in the forward()
        A__       =    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

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

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

        return ordered_inputs



     @property
     def     _a							(     self					:       List[Any]					):






        """simple docstring"""


        return 13









import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      __lowerCAmelCase     (		UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		int			       =    LongformerTokenizer
					A__				:		Optional[int]			       =    True
					A__				:		Any			       =    LongformerTokenizerFast
					A__				:		Dict			       =    True



					def     _a							(     self					:       int					):






								"""simple docstring"""


								super().setUp()

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

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



					def     _a							(     self					:       int				,					**_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								kwargs.update(self.special_tokens_map					)
								return self.tokenizer_class.from_pretrained(self.tmpdirname				,					**_snake_case					)



					def     _a							(     self					:       Optional[int]				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


								kwargs.update(self.special_tokens_map					)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,					**_snake_case					)



					def     _a							(     self					:       Any				,					_snake_case					:       Optional[Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.tokenizer_class(self.vocab_file				,					self.merges_file				,					**self.special_tokens_map					)
								A__       =    'lower newer'
								A__       =    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
								A__       =    tokenizer.tokenize(_snake_case					)  # , add_prefix_space=True)
								self.assertListEqual(_snake_case				,					_snake_case					)

								A__       =    tokens + [tokenizer.unk_token]
								A__       =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case					)				,					_snake_case					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    self.get_tokenizer()

								self.assertListEqual(tokenizer.encode('Hello world!'				,					add_special_tokens=_snake_case					)				,					[0, 3_14_14, 2_32, 3_28, 2]					)
								self.assertListEqual(
								    tokenizer.encode('Hello world! cécé herlolip 418'				,					add_special_tokens=_snake_case					)				,					[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]				,					)



					@slow
					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'					)

								A__       =    tokenizer.encode('sequence builders'				,					add_special_tokens=_snake_case					)
								A__       =    tokenizer.encode('multi-sequence build'				,					add_special_tokens=_snake_case					)

								A__       =    tokenizer.encode(
								    'sequence builders'				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)
								A__       =    tokenizer.encode(
								    'sequence builders'				,					'multi-sequence build'				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)

								A__       =    tokenizer.build_inputs_with_special_tokens(_snake_case					)
								A__       =    tokenizer.build_inputs_with_special_tokens(_snake_case				,					_snake_case					)

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    self.get_tokenizer()

								A__       =    'Encode this sequence.'
								A__       =    tokenizer.byte_encoder[' '.encode('utf-8'					)[0]]

								# Testing encoder arguments
								A__       =    tokenizer.encode(_snake_case				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
								self.assertNotEqual(_snake_case				,					_snake_case					)

								A__       =    tokenizer.encode(_snake_case				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
								self.assertEqual(_snake_case				,					_snake_case					)

								tokenizer.add_special_tokens({'bos_token': '<s>'}					)
								A__       =    tokenizer.encode(_snake_case				,					add_special_tokens=_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
								self.assertNotEqual(_snake_case				,					_snake_case					)

								# Testing spaces after special tokens
								A__       =    '<mask>'
								tokenizer.add_special_tokens(
								    {'mask_token': AddedToken(_snake_case				,					lstrip=_snake_case				,					rstrip=_snake_case					)}					)  # mask token has a left space
								A__       =    tokenizer.convert_tokens_to_ids(_snake_case					)

								A__       =    'Encode <mask> sequence'
								A__       =    'Encode <mask>sequence'

								A__       =    tokenizer.encode(_snake_case					)
								A__       =    encoded.index(_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
								self.assertEqual(_snake_case				,					_snake_case					)

								A__       =    tokenizer.encode(_snake_case					)
								A__       =    encoded.index(_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
								self.assertNotEqual(_snake_case				,					_snake_case					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								pass



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






								"""simple docstring"""


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
														A__       =    self.rust_tokenizer_class.from_pretrained(_snake_case				,					**_snake_case					)
														A__       =    self.tokenizer_class.from_pretrained(_snake_case				,					**_snake_case					)
														A__       =    'A, <mask> AllenNLP sentence.'
														A__       =    tokenizer_r.encode_plus(_snake_case				,					add_special_tokens=_snake_case				,					return_token_type_ids=_snake_case					)
														A__       =    tokenizer_p.encode_plus(_snake_case				,					add_special_tokens=_snake_case				,					return_token_type_ids=_snake_case					)

														# token_type_ids should put 0 everywhere
														self.assertEqual(sum(tokens_r['token_type_ids']					)				,					sum(tokens_p['token_type_ids']					)					)

														# attention_mask should put 1 everywhere, so sum over length should be 1
														self.assertEqual(
														    sum(tokens_r['attention_mask']					) / len(tokens_r['attention_mask']					)				,					sum(tokens_p['attention_mask']					) / len(tokens_p['attention_mask']					)				,					)

														A__       =    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
														A__       =    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']					)

														# Rust correctly handles the space before the mask while python doesnt
														self.assertSequenceEqual(tokens_p['input_ids']				,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)
														self.assertSequenceEqual(tokens_r['input_ids']				,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)

														self.assertSequenceEqual(
														    _snake_case				,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
														self.assertSequenceEqual(
														    _snake_case				,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								for trim_offsets, add_prefix_space in itertools.product([True, False]				,					repeat=2					):
											A__       =    self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)

											A__       =    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
											A__       =    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

											self.assertEqual(pre_tokenizer_state['add_prefix_space']				,					_snake_case					)

											self.assertEqual(post_processor_state['add_prefix_space']				,					_snake_case					)
											self.assertEqual(post_processor_state['trim_offsets']				,					_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
														A__       =    'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
														A__       =    F'''{text_of_1_token} {text_of_1_token}'''

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					) + 1, len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					) + 1, len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					), len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					), len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    F''' {text}'''

														# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
														#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
														# )
														# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
														# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
														# self.assertEqual(
														#     encoding.offset_mapping[1],
														#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
														# )

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(1, 1 + len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(1 + len(_snake_case					) + 1, 1 + len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, 1 + len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(1 + len(_snake_case					), 1 + len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, 1 + len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(1 + len(_snake_case					), 1 + len(_snake_case					) + 1 + len(_snake_case					))				,					)




