





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					:				Dict				=			logging.get_logger(__name__)


_A					:				List[str]				=			{
    """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 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		List[str]				=	"""codegen"""
    lowerCamelCase__    :		int				=	{
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }



    def __init__(       self   ,				A_=5_04_00   ,				A_=20_48   ,				A_=20_48   ,				A_=40_96   ,				A_=28   ,				A_=16   ,				A_=64   ,				A_=None   ,				A_="gelu_new"   ,				A_=0.0   ,				A_=0.0   ,				A_=0.0   ,				A_=1E-5   ,				A_=0.02   ,				A_=True   ,				A_=5_02_56   ,				A_=5_02_56   ,				A_=False   ,				**A_   ,				):





        '''simple docstring'''

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

        SCREAMING_SNAKE_CASE__											=			bos_token_id
        SCREAMING_SNAKE_CASE__											=			eos_token_id

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





class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_ = "default"   ,				A_ = None   ,				A_ = False   ,				):





        '''simple docstring'''

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



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

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

        return common_inputs



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self._config.n_layer



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self._config.n_head



    def  lowercase_					(       self   ,				A_   ,				A_ = -1   ,				A_ = -1   ,				A_ = False   ,				A_ = None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			super(A_   ,				self     ).generate_dummy_inputs(
            A_   ,				batch_size=A_   ,				seq_length=A_   ,				is_pair=A_   ,				framework=A_     )

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

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

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

        return ordered_inputs




    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return 13







import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	BioGptTokenizer
    lowerCamelCase__    :		List[Any]				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE__											=			[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''w</w>''',
            '''r</w>''',
            '''t</w>''',
            '''lo''',
            '''low''',
            '''er</w>''',
            '''low</w>''',
            '''lowest</w>''',
            '''newer</w>''',
            '''wider</w>''',
            '''<unk>''',
        ]
        SCREAMING_SNAKE_CASE__											=			dict(zip(A_   ,				range(len(A_     )     )     )     )
        SCREAMING_SNAKE_CASE__											=			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']     )
        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file''']     )
        with open(self.vocab_file   ,				'''w'''     ) as fp:
            fp.write(json.dumps(A_     )     )
        with open(self.merges_file   ,				'''w'''     ) as fp:
            fp.write('''\n'''.join(A_     )     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer(self.vocab_file   ,				self.merges_file     )

        SCREAMING_SNAKE_CASE__											=			'''lower'''
        SCREAMING_SNAKE_CASE__											=			['''low''', '''er</w>''']
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + ['''<unk>''']
        SCREAMING_SNAKE_CASE__											=			[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''sequence builders'''   ,				add_special_tokens=A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''multi-sequence build'''   ,				add_special_tokens=A_     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_   ,				A_     )

        self.assertTrue(encoded_sentence == [2] + text     )
        self.assertTrue(encoded_pair == [2] + text + [2] + text_a     )
