import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      lowerCamelCase_					(       UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						Union[str, Any]      =				None
    a__     :						Dict      =				BloomTokenizerFast
    a__     :						List[str]      =				BloomTokenizerFast
    a__     :						Optional[Any]      =				True
    a__     :						Dict      =				False
    a__     :						int      =				"""tokenizer_file"""
    a__     :						Optional[Any]      =				{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        super().setUp()
        __UpperCamelCase					:Optional[Any]        =		BloomTokenizerFast.from_pretrained('''bigscience/tokenizer''')
        tokenizer.save_pretrained(self.tmpdirname)







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     str:
        kwargs.update(self.special_tokens_map)
        return BloomTokenizerFast.from_pretrained(self.tmpdirname			,			**__lowercase)







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:Any        =		self.get_rust_tokenizer()

        __UpperCamelCase					:List[Any]        =		['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
        __UpperCamelCase					:Dict        =		[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

        __UpperCamelCase					:Optional[int]        =		tokenizer.batch_encode_plus(__lowercase)['''input_ids''']
        self.assertListEqual(__lowercase			,			__lowercase)

        __UpperCamelCase					:Any        =		tokenizer.batch_decode(__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase=6)	->     Optional[Any]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                __UpperCamelCase					:Dict        =		self.rust_tokenizer_class.from_pretrained(__lowercase			,			**__lowercase)
                # tokenizer_r.pad_token = None # Hotfixing padding = None
                # Simple input
                __UpperCamelCase					:Optional[int]        =		'''This is a simple input'''
                __UpperCamelCase					:Optional[int]        =		['''This is a simple input 1''', '''This is a simple input 2''']
                __UpperCamelCase					:str        =		('''This is a simple input''', '''This is a pair''')
                __UpperCamelCase					:List[str]        =		[
                    ('''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
                try:
                    tokenizer_r.encode(__lowercase			,			max_length=__lowercase)
                    tokenizer_r.encode_plus(__lowercase			,			max_length=__lowercase)

                    tokenizer_r.batch_encode_plus(__lowercase			,			max_length=__lowercase)
                    tokenizer_r.encode(__lowercase			,			max_length=__lowercase)
                    tokenizer_r.batch_encode_plus(__lowercase			,			max_length=__lowercase)
                except ValueError:
                    self.fail('''Bloom Tokenizer should be able to deal with padding''')

                __UpperCamelCase					:Optional[Any]        =		None  # Hotfixing padding = None
                self.assertRaises(__lowercase			,			tokenizer_r.encode			,			__lowercase			,			max_length=__lowercase			,			padding='''max_length''')

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

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

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

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

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







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:int        =		self.get_rust_tokenizer()
        __UpperCamelCase					:str        =		load_dataset('''xnli'''			,			'''all_languages'''			,			split='''test'''			,			streaming=__lowercase)

        __UpperCamelCase					:str        =		next(iter(__lowercase))['''premise''']  # pick up one data
        __UpperCamelCase					:Tuple        =		list(sample_data.values())

        __UpperCamelCase					:List[str]        =		list(map(tokenizer.encode			,			__lowercase))
        __UpperCamelCase					:List[str]        =		[tokenizer.decode(__lowercase			,			clean_up_tokenization_spaces=__lowercase) for x in output_tokens]
        self.assertListEqual(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
        # any sequence length constraints. This test of the parent class will fail since it relies on the
        # maximum sequence length of the positoonal embeddings.
        self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)			,			1)
        self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])			,			1)






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


__lowercase										= logging.get_logger(__name__)

__lowercase										= {
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Optional[Any]      =				"""gptj"""
    a__     :						Tuple      =				{
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(     self			,			__lowercase=50_400			,			__lowercase=2_048			,			__lowercase=4_096			,			__lowercase=28			,			__lowercase=16			,			__lowercase=64			,			__lowercase=None			,			__lowercase="gelu_new"			,			__lowercase=0.0			,			__lowercase=0.0			,			__lowercase=0.0			,			__lowercase=1E-5			,			__lowercase=0.02			,			__lowercase=True			,			__lowercase=50_256			,			__lowercase=50_256			,			__lowercase=False			,			**__lowercase			,			)	->     Tuple:
        __UpperCamelCase					:Any        =		vocab_size
        __UpperCamelCase					:Optional[int]        =		n_positions
        __UpperCamelCase					:Tuple        =		n_embd
        __UpperCamelCase					:int        =		n_layer
        __UpperCamelCase					:Any        =		n_head
        __UpperCamelCase					:Any        =		n_inner
        __UpperCamelCase					:Dict        =		rotary_dim
        __UpperCamelCase					:Tuple        =		activation_function
        __UpperCamelCase					:Optional[Any]        =		resid_pdrop
        __UpperCamelCase					:Any        =		embd_pdrop
        __UpperCamelCase					:List[str]        =		attn_pdrop
        __UpperCamelCase					:str        =		layer_norm_epsilon
        __UpperCamelCase					:List[Any]        =		initializer_range
        __UpperCamelCase					:Dict        =		use_cache

        __UpperCamelCase					:List[Any]        =		bos_token_id
        __UpperCamelCase					:Tuple        =		eos_token_id

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


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase = "default"			,			__lowercase = None			,			__lowercase = False			,			)	->     Any:
        super().__init__(__lowercase			,			task=__lowercase			,			patching_specs=__lowercase			,			use_past=__lowercase)
        if not getattr(self._config			,			'''pad_token_id'''			,			__lowercase):
            # TODO: how to do that better?
            __UpperCamelCase					:Tuple        =		0







    @property
    def       UpperCamelCase__       (     self)	->     Mapping[str, Mapping[int, str]]:
        __UpperCamelCase					:Tuple        =		OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}})
        if self.use_past:
            self.fill_with_past_key_values_(__lowercase			,			direction='''inputs''')
            __UpperCamelCase					:str        =		{0: '''batch''', 1: '''past_sequence + sequence'''}
        else:
            __UpperCamelCase					:Any        =		{0: '''batch''', 1: '''sequence'''}

        return common_inputs







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







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







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = -1			,			__lowercase = -1			,			__lowercase = False			,			__lowercase = None			,			)	->     Mapping[str, Any]:
        __UpperCamelCase					:Optional[int]        =		super(__lowercase			,			self).generate_dummy_inputs(
            __lowercase			,			batch_size=__lowercase			,			seq_length=__lowercase			,			is_pair=__lowercase			,			framework=__lowercase)

        # We need to order the input in the way they appears in the forward()
        __UpperCamelCase					:int        =		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  ,	__UpperCamelCase					:str        =		common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                __UpperCamelCase					:List[str]        =		seqlen + 2
                __UpperCamelCase					:Union[str, Any]        =		(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                __UpperCamelCase					:Tuple        =		[
                    (torch.zeros(__lowercase), torch.zeros(__lowercase)) for _ in range(self.num_layers)
                ]

        __UpperCamelCase					:Tuple        =		common_inputs['''attention_mask''']
        if self.use_past:
            __UpperCamelCase					:Tuple        =		ordered_inputs['''attention_mask'''].dtype
            __UpperCamelCase					:Optional[Any]        =		torch.cat(
                [ordered_inputs['''attention_mask'''], torch.ones(__lowercase			,			__lowercase			,			dtype=__lowercase)]			,			dim=1)

        return ordered_inputs







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





