


"""simple docstring"""



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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Tuple													=						'''blenderbot-small'''
   __UpperCAmelCase				: Optional[int]													=						['''past_key_values''']
   __UpperCAmelCase				: int													=						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







   def __init__(			self		,			UpperCamelCase__=5_0265		,			UpperCamelCase__=512		,			UpperCamelCase__=8		,			UpperCamelCase__=2048		,			UpperCamelCase__=16		,			UpperCamelCase__=8		,			UpperCamelCase__=2048		,			UpperCamelCase__=16		,			UpperCamelCase__=0.0		,			UpperCamelCase__=0.0		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=512		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.0		,			UpperCamelCase__=0.0		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1		,			UpperCamelCase__=False		,			UpperCamelCase__=0		,			UpperCamelCase__=1		,			UpperCamelCase__=2		,			UpperCamelCase__=2		,			**UpperCamelCase__		,			)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					vocab_size
       snake_case					:   Optional[Any]         =					max_position_embeddings
       snake_case					:   List[str]         =					d_model
       snake_case					:   str         =					encoder_ffn_dim
       snake_case					:   List[str]         =					encoder_layers
       snake_case					:   Optional[int]         =					encoder_attention_heads
       snake_case					:   Any         =					decoder_ffn_dim
       snake_case					:   Optional[int]         =					decoder_layers
       snake_case					:   Optional[Any]         =					decoder_attention_heads
       snake_case					:   Tuple         =					dropout
       snake_case					:   Tuple         =					attention_dropout
       snake_case					:   List[str]         =					activation_dropout
       snake_case					:   str         =					activation_function
       snake_case					:   Tuple         =					init_std
       snake_case					:   Dict         =					encoder_layerdrop
       snake_case					:   str         =					decoder_layerdrop
       snake_case					:   Any         =					use_cache
       snake_case					:   List[Any]         =					encoder_layers
       snake_case					:   int         =					scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			is_encoder_decoder=UpperCamelCase__		,			decoder_start_token_id=UpperCamelCase__		,			forced_eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		,			)







class _lowerCAmelCase       (   snake_case_			):







   @property
   def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
           snake_case					:   str         =					OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]		)

           if self.use_past:
               snake_case					:   Tuple         =					{0: "batch"}
               snake_case					:   Optional[int]         =					{0: "batch", 1: "past_decoder_sequence + sequence"}
           else:
               snake_case					:   Optional[Any]         =					{0: "batch", 1: "decoder_sequence"}
               snake_case					:   Optional[Any]         =					{0: "batch", 1: "decoder_sequence"}

           if self.use_past:
               self.fill_with_past_key_values_(UpperCamelCase__		,			direction="inputs"		)
       elif self.task == "causal-lm":
           # TODO: figure this case out.
           snake_case					:   Dict         =					OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]		)
           if self.use_past:
               snake_case      ,snake_case					:   str         =					self.num_layers
               for i in range(UpperCamelCase__		):
                   snake_case					:   Tuple         =					{0: "batch", 2: "past_sequence + sequence"}
                   snake_case					:   Optional[int]         =					{0: "batch", 2: "past_sequence + sequence"}
       else:
           snake_case					:   Optional[int]         =					OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                   ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                   ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
               ]		)

       return common_inputs







   @property
   def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
           snake_case					:   List[Any]         =					super().outputs
       else:
           snake_case					:   int         =					super(UpperCamelCase__		,			self		).outputs
           if self.use_past:
               snake_case      ,snake_case					:   Tuple         =					self.num_layers
               for i in range(UpperCamelCase__		):
                   snake_case					:   str         =					{0: "batch", 2: "past_sequence + sequence"}
                   snake_case					:   Union[str, Any]         =					{0: "batch", 2: "past_sequence + sequence"}
       return common_outputs







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = -1		,			UpperCamelCase__ = -1		,			UpperCamelCase__ = False		,			UpperCamelCase__ = None		,			)			->	Mapping[str, Any]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)

       # Generate decoder inputs
       snake_case					:   Any         =					seq_length if not self.use_past else 1
       snake_case					:   List[Any]         =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)
       snake_case					:   Optional[int]         =					{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
       snake_case					:   Tuple         =					dict(**UpperCamelCase__		,			**UpperCamelCase__		)

       if self.use_past:
           if not is_torch_available():
               raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
           else:
               import torch
           snake_case      ,snake_case					:   List[str]         =					common_inputs["input_ids"].shape
           snake_case					:   Dict         =					common_inputs["decoder_input_ids"].shape[1]
           snake_case      ,snake_case					:   int         =					self.num_attention_heads
           snake_case					:   Tuple         =					(
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           snake_case					:   Union[str, Any]         =					decoder_seq_length + 3
           snake_case					:   Optional[int]         =					(
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )

           snake_case					:   Union[str, Any]         =					torch.cat(
               [common_inputs["decoder_attention_mask"], torch.ones(UpperCamelCase__		,			UpperCamelCase__		)]		,			dim=1		)

           snake_case					:   str         =					[]
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           snake_case      ,snake_case					:   List[str]         =					self.num_layers
           snake_case					:   Optional[Any]         =					min(UpperCamelCase__		,			UpperCamelCase__		)
           snake_case					:   str         =					max(UpperCamelCase__		,			UpperCamelCase__		) - min_num_layers
           snake_case					:   Union[str, Any]         =					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

           for _ in range(UpperCamelCase__		):
               common_inputs["past_key_values"].append(
                   (
                       torch.zeros(UpperCamelCase__		),
                       torch.zeros(UpperCamelCase__		),
                       torch.zeros(UpperCamelCase__		),
                       torch.zeros(UpperCamelCase__		),
                   )		)
           # TODO: test this.
           snake_case					:   str         =					encoder_shape if remaining_side_name == "encoder" else decoder_shape
           for _ in range(UpperCamelCase__		,			UpperCamelCase__		):
               common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase__		), torch.zeros(UpperCamelCase__		))		)
       return common_inputs







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = -1		,			UpperCamelCase__ = -1		,			UpperCamelCase__ = False		,			UpperCamelCase__ = None		,			)			->	Mapping[str, Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)

       if self.use_past:
           if not is_torch_available():
               raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
           else:
               import torch
           snake_case      ,snake_case					:   Optional[Any]         =					common_inputs["input_ids"].shape
           # Not using the same length for past_key_values
           snake_case					:   int         =					seqlen + 2
           snake_case      ,snake_case					:   Tuple         =					self.num_layers
           snake_case      ,snake_case					:   Optional[Any]         =					self.num_attention_heads
           snake_case					:   Any         =					(
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           snake_case					:   Dict         =					common_inputs["attention_mask"].dtype
           snake_case					:   Any         =					torch.cat(
               [common_inputs["attention_mask"], torch.ones(UpperCamelCase__		,			UpperCamelCase__		,			dtype=UpperCamelCase__		)]		,			dim=1		)
           snake_case					:   Union[str, Any]         =					[
               (torch.zeros(UpperCamelCase__		), torch.zeros(UpperCamelCase__		)) for _ in range(UpperCamelCase__		)
           ]
       return common_inputs







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = -1		,			UpperCamelCase__ = -1		,			UpperCamelCase__ = False		,			UpperCamelCase__ = None		,			)			->	Mapping[str, Any]:

       '''simple docstring'''




       snake_case					:   Any         =					compute_effective_axis_dimension(
           UpperCamelCase__		,			fixed_dimension=OnnxConfig.default_fixed_batch		,			num_token_to_add=0		)

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       snake_case					:   List[str]         =					tokenizer.num_special_tokens_to_add(UpperCamelCase__		)
       snake_case					:   Union[str, Any]         =					compute_effective_axis_dimension(
           UpperCamelCase__		,			fixed_dimension=OnnxConfig.default_fixed_sequence		,			num_token_to_add=UpperCamelCase__		)

       # Generate dummy inputs according to compute batch and sequence
       snake_case					:   Union[str, Any]         =					[" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
       snake_case					:   List[str]         =					dict(tokenizer(UpperCamelCase__		,			return_tensors=UpperCamelCase__		)		)
       return common_inputs







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = -1		,			UpperCamelCase__ = -1		,			UpperCamelCase__ = False		,			UpperCamelCase__ = None		,			)			->	Mapping[str, Any]:

       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
           snake_case					:   Optional[int]         =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               UpperCamelCase__		,			batch_size=UpperCamelCase__		,			seq_length=UpperCamelCase__		,			is_pair=UpperCamelCase__		,			framework=UpperCamelCase__		)

       elif self.task == "causal-lm":
           snake_case					:   int         =					self._generate_dummy_inputs_for_causal_lm(
               UpperCamelCase__		,			batch_size=UpperCamelCase__		,			seq_length=UpperCamelCase__		,			is_pair=UpperCamelCase__		,			framework=UpperCamelCase__		)
       else:
           snake_case					:   Optional[Any]         =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               UpperCamelCase__		,			batch_size=UpperCamelCase__		,			seq_length=UpperCamelCase__		,			is_pair=UpperCamelCase__		,			framework=UpperCamelCase__		)

       return common_inputs







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)			->	str:

       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
           snake_case					:   Optional[Any]         =					super()._flatten_past_key_values_(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)
       else:
           snake_case					:   Optional[int]         =					super(UpperCamelCase__		,			self		)._flatten_past_key_values_(
               UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)









"""simple docstring"""



import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: List[Any]													=						LayoutLMTokenizer
   __UpperCAmelCase				: List[Any]													=						LayoutLMTokenizerFast
   __UpperCAmelCase				: List[Any]													=						True
   __UpperCAmelCase				: Dict													=						True







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       super().setUp()

       snake_case					:   Dict         =					[
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       snake_case					:   List[str]         =					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 				lowerCamelCase	(			self		,			**UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   str         =					"UNwant\u00E9d,running"
       snake_case					:   Optional[int]         =					"unwanted, running"
       return input_text, output_text







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.tokenizer_class(self.vocab_file		)

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







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       pass





