

"""simple docstring"""

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


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """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 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						'gptj'
       __UpperCamelCase					:      List[str]    =						{
           'max_position_embeddings': 'n_positions',
           'hidden_size': 'n_embd',
           'num_attention_heads': 'n_head',
           'num_hidden_layers': 'n_layer',
       }






       def __init__(   self			,					snake_case=50_400			,					snake_case=2_048			,					snake_case=4_096			,					snake_case=28			,					snake_case=16			,					snake_case=64			,					snake_case=None			,					snake_case="gelu_new"			,					snake_case=0.0			,					snake_case=0.0			,					snake_case=0.0			,					snake_case=1e-5			,					snake_case=0.02			,					snake_case=True			,					snake_case=50_256			,					snake_case=50_256			,					snake_case=False			,					**snake_case			,					):





        """simple docstring"""

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

        lowerCAmelCase__   :				Dict 			=				bos_token_id
        lowerCAmelCase__   :				Tuple 			=				eos_token_id

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






class 			__a					(						__magic_name__       ):





       """simple docstring"""


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





        """simple docstring"""

        super().__init__(snake_case			,					task=snake_case			,					patching_specs=snake_case			,					use_past=snake_case   )
        if not getattr(self._config			,					"pad_token_id"			,					snake_case   ):
         # TODO: how to do that better?
         lowerCAmelCase__   :				str 			=				0






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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

        return common_inputs






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self._config.n_layer






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self._config.n_head






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = -1			,					snake_case = -1			,					snake_case = False			,					snake_case = None			,					):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				super(snake_case			,					self   ).generate_dummy_inputs(
            snake_case			,					batch_size=snake_case			,					seq_length=snake_case			,					is_pair=snake_case			,					framework=snake_case   )

        # We need to order the input in the way they appears in the forward()
        lowerCAmelCase__   :				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

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

        lowerCAmelCase__   :				List[str] 			=				common_inputs["attention_mask"]
        if self.use_past:
         lowerCAmelCase__   :				Optional[int] 			=				ordered_inputs["attention_mask"].dtype
         lowerCAmelCase__   :				int 			=				torch.cat(
             [ordered_inputs["attention_mask"], torch.ones(snake_case			,					snake_case			,					dtype=snake_case   )]			,					dim=1   )

        return ordered_inputs






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return 13









"""simple docstring"""

import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__a					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizer
       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizerFast
       __UpperCamelCase					:      str    =						True
       __UpperCamelCase					:      Any    =						True






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        super().setUp()

        lowerCAmelCase__   :				Tuple 			=				[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        lowerCAmelCase__   :				Dict 			=				os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES["vocab_file"]   )
        with open(self.vocab_file			,					"w"			,					encoding="utf-8"   ) as vocab_writer:
         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				"UNwant\u00E9d,running"
        lowerCAmelCase__   :				Union[str, Any] 			=				"unwanted, running"
        return input_text, output_text






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.tokenizer_class(self.vocab_file   )

        lowerCAmelCase__   :				List[Any] 			=				tokenizer.tokenize("UNwant\u00E9d,running"   )
        self.assertListEqual(snake_case			,					["un", "##want", "##ed", ",", "runn", "##ing"]   )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case   )			,					[7, 4, 5, 10, 8, 9]   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        if not self.test_rust_tokenizer:
         return

        lowerCAmelCase__   :				int 			=				self.get_tokenizer()
        lowerCAmelCase__   :				List[str] 			=				self.get_rust_tokenizer()

        lowerCAmelCase__   :				Optional[Any] 			=				"I was born in 92000, and this is falsé."

        lowerCAmelCase__   :				int 			=				tokenizer.tokenize(snake_case   )
        lowerCAmelCase__   :				int 			=				rust_tokenizer.tokenize(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				str 			=				tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        lowerCAmelCase__   :				List[str] 			=				rust_tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				Tuple 			=				self.get_rust_tokenizer()
        lowerCAmelCase__   :				Tuple 			=				tokenizer.encode(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				rust_tokenizer.encode(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )






