





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__:    int  	=	logging.get_logger(__name__)


A__:    List[Any]  	=	{
    '''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        _a    (    __A):

      """simple docstring"""


      UpperCamelCase__  		=       """codegen"""
      UpperCamelCase__  		=       {
          """max_position_embeddings""": """n_positions""",
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }






      def __init__(		self:			Any	,    __lowerCamelCase:			Dict=5_0400	,    __lowerCamelCase:			str=2048	,    __lowerCamelCase:			Any=2048	,    __lowerCamelCase:			Tuple=4096	,    __lowerCamelCase:			List[str]=28	,    __lowerCamelCase:			str=16	,    __lowerCamelCase:			Optional[Any]=64	,    __lowerCamelCase:			str=None	,    __lowerCamelCase:			List[str]="gelu_new"	,    __lowerCamelCase:			Union[str, Any]=0.0	,    __lowerCamelCase:			Optional[int]=0.0	,    __lowerCamelCase:			List[Any]=0.0	,    __lowerCamelCase:			Optional[int]=1e-5	,    __lowerCamelCase:			int=0.02	,    __lowerCamelCase:			Dict=True	,    __lowerCamelCase:			str=5_0256	,    __lowerCamelCase:			int=5_0256	,    __lowerCamelCase:			Any=False	,    **__lowerCamelCase:			List[str]	,    ):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     vocab_size
          UpperCamelCase__:	int					=     n_ctx
          UpperCamelCase__:	Optional[int]					=     n_positions
          UpperCamelCase__:	int					=     n_embd
          UpperCamelCase__:	str					=     n_layer
          UpperCamelCase__:	Dict					=     n_head
          UpperCamelCase__:	List[str]					=     n_inner
          UpperCamelCase__:	List[Any]					=     rotary_dim
          UpperCamelCase__:	Union[str, Any]					=     activation_function
          UpperCamelCase__:	str					=     resid_pdrop
          UpperCamelCase__:	Optional[int]					=     embd_pdrop
          UpperCamelCase__:	Union[str, Any]					=     attn_pdrop
          UpperCamelCase__:	Dict					=     layer_norm_epsilon
          UpperCamelCase__:	Union[str, Any]					=     initializer_range
          UpperCamelCase__:	List[Any]					=     use_cache

          UpperCamelCase__:	Tuple					=     bos_token_id
          UpperCamelCase__:	str					=     eos_token_id

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




class        _a    (    __A):

      """simple docstring"""


      def __init__(		self:			List[str]	,    __lowerCamelCase:			PretrainedConfig	,    __lowerCamelCase:			str = "default"	,    __lowerCamelCase:			List[PatchingSpec] = None	,    __lowerCamelCase:			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?
              UpperCamelCase__:	List[Any]					=     0






      @property
      def 			UpperCAmelCase_       (		self:			int					):
          '''simple docstring'''


          UpperCamelCase__:	str					=     OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
          if self.use_past:
              self.fill_with_past_key_values_(__lowerCamelCase	,    direction="inputs"					)
              UpperCamelCase__:	Tuple					=     {0: '''batch''', 1: '''past_sequence + sequence'''}
          else:
              UpperCamelCase__:	Tuple					=     {0: '''batch''', 1: '''sequence'''}

          return common_inputs






      @property
      def 			UpperCAmelCase_       (		self:			Optional[int]					):
          '''simple docstring'''


          return self._config.n_layer






      @property
      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


          return self._config.n_head






      def 			UpperCAmelCase_       (		self:			List[str]	,    __lowerCamelCase:			PreTrainedTokenizer	,    __lowerCamelCase:			int = -1	,    __lowerCamelCase:			int = -1	,    __lowerCamelCase:			bool = False	,    __lowerCamelCase:			Optional[TensorType] = None	,    ):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     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()
          UpperCamelCase__:	List[str]					=     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

                  UpperCamelCase__:	Any					=     common_inputs['''input_ids'''].shape
                  # Not using the same length for past_key_values
                  UpperCamelCase__:	Tuple					=     seqlen + 2
                  UpperCamelCase__:	int					=     (
                      batch,
                      self.num_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // self.num_attention_heads,
                  )
                  UpperCamelCase__:	str					=     [
                      (torch.zeros(__lowerCamelCase					), torch.zeros(__lowerCamelCase					)) for _ in range(self.num_layers					)
                  ]

          UpperCamelCase__:	List[Any]					=     common_inputs['''attention_mask''']
          if self.use_past:
              UpperCamelCase__:	Any					=     ordered_inputs['''attention_mask'''].dtype
              UpperCamelCase__:	Union[str, Any]					=     torch.cat(
                  [ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase	,    __lowerCamelCase	,    dtype=__lowerCamelCase					)]	,    dim=1					)

          return ordered_inputs






      @property
      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          return 13









'''simple docstring'''



import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class UpperCAmelCase_       (     __A					,							unittest.TestCase					):




      """simple docstring"""




      UpperCamelCase_             =      MvpTokenizer
      UpperCamelCase_             =      MvpTokenizerFast
      UpperCamelCase_             =      True
      UpperCamelCase_             =      filter_roberta_detectors






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


       '''simple docstring'''

       super().setUp()
       lowercase       : Dict						=[
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
       ]
       lowercase       : Tuple						=dict(zip(UpperCAmelCase  ,   range(len(UpperCAmelCase		)		)		)		)
       lowercase       : Optional[Any]						=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       lowercase       : List[Any]						={'''unk_token''': '''<unk>'''}

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






      def 					A__   (			self      :		Union[str, Any]  ,   **UpperCAmelCase      :		Dict		)	->	Tuple:


       '''simple docstring'''

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






      def 					A__   (			self      :		List[str]  ,   **UpperCAmelCase      :		Any		)	->	Tuple:


       '''simple docstring'''

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






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


       '''simple docstring'''

       return "lower newer", "lower newer"






      @cached_property
      def 					A__   (			self      :		Tuple		)	->	List[Any]:


       '''simple docstring'''

       return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''		)






      @cached_property
      def 					A__   (			self      :		Any		)	->	int:


       '''simple docstring'''

       return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''		)






      @require_torch
      def 					A__   (			self      :		Union[str, Any]		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : Optional[int]						=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       lowercase       : List[str]						=[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : Union[str, Any]						=tokenizer(UpperCAmelCase  ,   max_length=len(UpperCAmelCase		)  ,   padding=UpperCAmelCase  ,   return_tensors='''pt'''		)
        self.assertIsInstance(UpperCAmelCase  ,   UpperCAmelCase		)

        self.assertEqual((2, 9)  ,   batch.input_ids.shape		)
        self.assertEqual((2, 9)  ,   batch.attention_mask.shape		)
        lowercase       : Union[str, Any]						=batch.input_ids.tolist()[0]
        self.assertListEqual(UpperCAmelCase  ,   UpperCAmelCase		)
        # Test that special tokens are reset






      @require_torch
      def 					A__   (			self      :		Tuple		)	->	Any:


       '''simple docstring'''

       lowercase       : Any						=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : Dict						=tokenizer(UpperCAmelCase  ,   padding=UpperCAmelCase  ,   return_tensors='''pt'''		)
        # check if input_ids are returned and no labels
        self.assertIn('''input_ids'''  ,   UpperCAmelCase		)
        self.assertIn('''attention_mask'''  ,   UpperCAmelCase		)
        self.assertNotIn('''labels'''  ,   UpperCAmelCase		)
        self.assertNotIn('''decoder_attention_mask'''  ,   UpperCAmelCase		)






      @require_torch
      def 					A__   (			self      :		Dict		)	->	int:


       '''simple docstring'''

       lowercase       : int						=[
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : Optional[Any]						=tokenizer(text_target=UpperCAmelCase  ,   max_length=32  ,   padding='''max_length'''  ,   return_tensors='''pt'''		)
        self.assertEqual(32  ,   targets['''input_ids'''].shape[1]		)






      @require_torch
      def 					A__   (			self      :		Union[str, Any]		)	->	Any:


       '''simple docstring'''

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : Union[str, Any]						=tokenizer(
            ['''I am a small frog''' * 1024, '''I am a small frog''']  ,   padding=UpperCAmelCase  ,   truncation=UpperCAmelCase  ,   return_tensors='''pt'''		)
        self.assertIsInstance(UpperCAmelCase  ,   UpperCAmelCase		)
        self.assertEqual(batch.input_ids.shape  ,   (2, 1024)		)






      @require_torch
      def 					A__   (			self      :		str		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=['''A long paragraph for summarization.''']
       lowercase       : List[Any]						=[
           '''Summary of the text.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowercase       : List[str]						=tokenizer(UpperCAmelCase  ,   text_target=UpperCAmelCase  ,   return_tensors='''pt'''		)
        lowercase       : Optional[int]						=inputs['''input_ids''']
        lowercase       : Optional[Any]						=inputs['''labels''']
        self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
        self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
        self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
        self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)






      def 					A__   (			self      :		List[Any]		)	->	List[Any]:


       '''simple docstring'''

       pass






      def 					A__   (			self      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
         lowercase       : Union[str, Any]						=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase  ,   **UpperCAmelCase		)
         lowercase       : Tuple						=self.tokenizer_class.from_pretrained(UpperCAmelCase  ,   **UpperCAmelCase		)
         lowercase       : Optional[Any]						='''A, <mask> AllenNLP sentence.'''
         lowercase       : int						=tokenizer_r.encode_plus(UpperCAmelCase  ,   add_special_tokens=UpperCAmelCase  ,   return_token_type_ids=UpperCAmelCase		)
         lowercase       : List[Any]						=tokenizer_p.encode_plus(UpperCAmelCase  ,   add_special_tokens=UpperCAmelCase  ,   return_token_type_ids=UpperCAmelCase		)

         # 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''']		)  ,   )

         lowercase       : Any						=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
         lowercase       : str						=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, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)
         self.assertSequenceEqual(tokens_r['''input_ids''']  ,   [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)

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



