



'''simple docstring'''


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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
@require_tokenizers
class 				a_       (    _lowerCAmelCase     ,						unittest.TestCase							):
     __A								=      LEDTokenizer
     __A								=      LEDTokenizerFast
     __A								=      True





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        super().setUp()
        lowercase_      :Union[str, Any]         =			[
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        lowercase_      :List[Any]         =			dict(zip(lowercase     ,				range(len(lowercase						)						)						)						)
        lowercase_      :Union[str, Any]         =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        lowercase_      :Tuple         =			{"unk_token": "<unk>"}

        lowercase_      :int         =			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES["vocab_file"]						)
        lowercase_      :Optional[Any]         =			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(lowercase						) + "\n"						)
        with open(self.merges_file     ,				"w"     ,				encoding="utf-8"						) as fp:
           fp.write("\n".join(lowercase						)						)





     def 							lowercase__				(			self		: Tuple     ,				**lowercase		: int						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Optional[int]     ,				**lowercase		: str						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: str						):





        """simple docstring"""




        return "lower newer", "lower newer"





     @cached_property
     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        return LEDTokenizer.from_pretrained("allenai/led-base-16384"						)





     @cached_property
     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"						)





     @require_torch
     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
        lowercase_      :int         =			[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase_      :Dict         =			tokenizer(lowercase     ,				max_length=len(lowercase						)     ,				padding=lowercase     ,				return_tensors="pt"						)
           self.assertIsInstance(lowercase     ,				lowercase						)

           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(lowercase     ,				lowercase						)





     @require_torch
     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        lowercase_      :int         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase_      :str         =			tokenizer(lowercase     ,				padding=lowercase     ,				return_tensors="pt"						)
           self.assertIn("input_ids"     ,				lowercase						)
           self.assertIn("attention_mask"     ,				lowercase						)
           self.assertNotIn("labels"     ,				lowercase						)
           self.assertNotIn("decoder_attention_mask"     ,				lowercase						)





     @require_torch
     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




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





     @require_torch
     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase_      :Optional[Any]         =			tokenizer(
               ["I am a small frog" * 1_024, "I am a small frog"]     ,				padding=lowercase     ,				truncation=lowercase     ,				return_tensors="pt"						)
           self.assertIsInstance(lowercase     ,				lowercase						)
           self.assertEqual(batch.input_ids.shape     ,				(2, 5_122)						)





     @require_torch
     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      :List[Any]         =			["A long paragraph for summarization."]
        lowercase_      :List[str]         =			[
            "Summary of the text.",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase_      :str         =			tokenizer(lowercase     ,				return_tensors="pt"						)
           lowercase_      :List[Any]         =			tokenizer(text_target=lowercase     ,				return_tensors="pt"						)
           lowercase_      :List[str]         =			inputs["input_ids"]
           lowercase_      :Optional[int]         =			targets["input_ids"]
           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()						)





     @require_torch
     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase_      :List[Any]         =			["Summary of the text.", "Another summary."]
           lowercase_      :List[str]         =			[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

           lowercase_      :Tuple         =			tokenizer(lowercase     ,				padding=lowercase						)
           lowercase_      :Optional[Any]         =			[[0] * len(lowercase						) for x in encoded_output["input_ids"]]
           lowercase_      :Optional[Any]         =			tokenizer.pad(lowercase						)
           self.assertSequenceEqual(outputs["global_attention_mask"]     ,				lowercase						)





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        pass







     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
              lowercase_      :Dict         =			self.rust_tokenizer_class.from_pretrained(lowercase     ,				**lowercase						)
              lowercase_      :List[str]         =			self.tokenizer_class.from_pretrained(lowercase     ,				**lowercase						)
              lowercase_      :Any         =			"A, <mask> AllenNLP sentence."
              lowercase_      :Union[str, Any]         =			tokenizer_r.encode_plus(lowercase     ,				add_special_tokens=lowercase     ,				return_token_type_ids=lowercase						)
              lowercase_      :List[Any]         =			tokenizer_p.encode_plus(lowercase     ,				add_special_tokens=lowercase     ,				return_token_type_ids=lowercase						)
              self.assertEqual(sum(tokens_r["token_type_ids"]						)     ,				sum(tokens_p["token_type_ids"]						)						)
              self.assertEqual(
                  sum(tokens_r["attention_mask"]						) / len(tokens_r["attention_mask"]						)     ,				sum(tokens_p["attention_mask"]						) / len(tokens_p["attention_mask"]						)     ,				)

              lowercase_      :List[str]         =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]						)
              lowercase_      :str         =			tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]						)
              self.assertSequenceEqual(tokens_p["input_ids"]     ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]						)
              self.assertSequenceEqual(tokens_r["input_ids"]     ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]						)

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









'''simple docstring'''


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase :					Union[str, Any] 							=logging.get_logger(__name__)

lowerCAmelCase :					Optional[Any] 							={
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 				a_       (    _lowerCAmelCase							):
     __A								=      "gpt_neo"
     __A								=      ["past_key_values"]
     __A								=      {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





     def __init__(			self		: Union[str, Any]     ,				lowercase		: Tuple=50_257     ,				lowercase		: Optional[Any]=2_048     ,				lowercase		: Union[str, Any]=2_048     ,				lowercase		: int=24     ,				lowercase		: Optional[Any]=[[["global", "local"], 12]]     ,				lowercase		: List[Any]=16     ,				lowercase		: List[str]=None     ,				lowercase		: Union[str, Any]=256     ,				lowercase		: Optional[Any]="gelu_new"     ,				lowercase		: Any=0.0     ,				lowercase		: List[Any]=0.0     ,				lowercase		: Any=0.0     ,				lowercase		: str=0.1     ,				lowercase		: Dict=1e-5     ,				lowercase		: List[str]=0.02     ,				lowercase		: Union[str, Any]=True     ,				lowercase		: int=50_256     ,				lowercase		: Union[str, Any]=50_256     ,				**lowercase		: Dict     ,				):





        """simple docstring"""




        lowercase_      :str         =			vocab_size
        lowercase_      :Tuple         =			max_position_embeddings
        lowercase_      :Tuple         =			hidden_size
        lowercase_      :List[str]         =			num_layers
        lowercase_      :int         =			num_heads
        lowercase_      :Union[str, Any]         =			intermediate_size
        lowercase_      :Tuple         =			window_size
        lowercase_      :Any         =			activation_function
        lowercase_      :Tuple         =			resid_dropout
        lowercase_      :Any         =			embed_dropout
        lowercase_      :str         =			attention_dropout
        lowercase_      :List[str]         =			classifier_dropout
        lowercase_      :List[Any]         =			layer_norm_epsilon
        lowercase_      :List[str]         =			initializer_range
        lowercase_      :int         =			use_cache

        lowercase_      :Tuple         =			bos_token_id
        lowercase_      :Optional[Any]         =			eos_token_id

        lowercase_      :int         =			attention_types
        lowercase_      :Tuple         =			self.expand_attention_types_params(lowercase						)

        if len(self.attention_layers						) != self.num_layers:
           raise ValueError(
               "Configuration for convolutional module is incorrect. "
               "It is required that `len(config.attention_layers)` == `config.num_layers` "
               F'but is `len(config.attention_layers) = {len(self.attention_layers						)}`, '
               F'`config.num_layers = {self.num_layers}`. '
               "`config.attention_layers` is prepared using `config.attention_types`. "
               "Please verify the value of `config.attention_types` argument."						)

        super().__init__(bos_token_id=lowercase     ,				eos_token_id=lowercase     ,				**lowercase						)







     @staticmethod
     def 							lowercase__				(			lowercase		: str						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			[]
        for item in attention_types:
           for _ in range(item[1]						):
              attentions.extend(item[0]						)
        return attentions







def 							UpperCAmelCase_  (						__lowerCamelCase : Union[str, Any]     ,__lowerCamelCase : Optional[int]     ,__lowerCamelCase : Optional[Any]     ,__lowerCamelCase : Dict			):
   import torch

   lowercase_      :List[str]         =			input.size()
   lowercase_      :Union[str, Any]         =			len(__lowerCamelCase			)
   lowercase_      :Any         =			shape[dimension]

   lowercase_      :str         =			torch.arange(0     ,__lowerCamelCase     ,__lowerCamelCase			)
   lowercase_      :Union[str, Any]         =			torch.div(sizedim - size     ,__lowerCamelCase     ,rounding_mode="floor"			) + 1
   lowercase_      :int         =			torch.arange(__lowerCamelCase			) + low_indices[:min_length][:, None]

   lowercase_      :List[Any]         =			[slice(__lowerCamelCase			)] * rank
   lowercase_      :int         =			indices
   lowercase_      :Dict         =			input[s]

   lowercase_      :List[str]         =			list(range(0     ,rank + 1			)			)
   perm.append(perm.pop(dimension + 1			)			)

   return sliced.permute(__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : Any			):
   import torch

   lowercase_      :List[Any]         =			torch.arange(1     ,__lowerCamelCase			)
   lowercase_      :int         =			torch.remainder(__lowerCamelCase     ,__lowerCamelCase			)
   lowercase_      :Optional[int]         =			remainders == 0
   lowercase_      :int         =			candidates[divisor_indices]
   lowercase_      :Tuple         =			torch.max(__lowerCamelCase			)
   return largest_divisor, torch.div(__lowerCamelCase     ,__lowerCamelCase     ,rounding_mode="floor"			)


class 				a_       (    _lowerCAmelCase							):





     @property
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :int         =			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
        if self.use_past:
           self.fill_with_past_key_values_(lowercase     ,				direction="inputs"						)
           lowercase_      :Union[str, Any]         =			{0: "batch", 1: "past_sequence + sequence"}
        else:
           lowercase_      :str         =			{0: "batch", 1: "sequence"}

        return common_inputs





     @property
     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        return self._config.num_heads





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: PreTrainedTokenizer     ,				lowercase		: int = -1     ,				lowercase		: int = -1     ,				lowercase		: bool = False     ,				lowercase		: Optional[TensorType] = None     ,				):





        """simple docstring"""




        lowercase_      :List[str]         =			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()
        lowercase_      :Tuple         =			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

              lowercase_      ,							lowercase_      :Tuple         =			common_inputs["input_ids"].shape
              # Not using the same length for past_key_values
              lowercase_      :Any         =			seqlen + 2
              lowercase_      :List[Any]         =			(
                  batch,
                  self.num_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // self.num_attention_heads,
              )
              lowercase_      :Dict         =			[
                  (torch.zeros(lowercase						), torch.zeros(lowercase						)) for _ in range(self.num_layers						)
              ]

        lowercase_      :Tuple         =			common_inputs["attention_mask"]
        if self.use_past:
           lowercase_      :Optional[int]         =			ordered_inputs["attention_mask"].dtype
           lowercase_      :List[Any]         =			torch.cat(
               [ordered_inputs["attention_mask"], torch.ones(lowercase     ,				lowercase     ,				dtype=lowercase						)]     ,				dim=1						)

        return ordered_inputs







     @property
     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        return 13


