




'''simple docstring'''







import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
   A     ='pt'
elif is_tf_available():
   A     ='tf'
else:
   A     ='jax'


class 			_a  (	__a							,							unittest.TestCase ):
       __a				:     Optional[Any]   				=					PerceiverTokenizer
       __a				:     str   				=					False
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           super().setUp()
           UpperCAmelCase			       =		PerceiverTokenizer()
           tokenizer.save_pretrained(self.tmpdirname				)
       @cached_property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''				)
       def     A      (	self						:	Union[str, Any]   ,				**lowercase						:	int				):



           '''simple docstring'''

           return self.tokenizer_class.from_pretrained(self.tmpdirname   ,				**lowercase				)
       def     A      (	self						:	Tuple   ,				lowercase						:	str   ,				lowercase						:	List[str]=False   ,				lowercase						:	Union[str, Any]=20   ,				lowercase						:	Union[str, Any]=5				):



           '''simple docstring'''

           UpperCAmelCase			       =		[]
           for i in range(len(lowercase				)				):
               try:
                   UpperCAmelCase			       =		tokenizer.decode([i]   ,				clean_up_tokenization_spaces=lowercase				)
               except UnicodeDecodeError:
                   pass
               toks.append((i, tok)				)

           UpperCAmelCase			       =		list(filter(lambda lowercase				: re.match(R'''^[ a-zA-Z]+$'''   ,				t[1]				)   ,				lowercase				)				)
           UpperCAmelCase			       =		list(filter(lambda lowercase				: [t[0]] == tokenizer.encode(t[1]   ,				add_special_tokens=lowercase				)   ,				lowercase				)				)
           if max_length is not None and len(lowercase				) > max_length:
               UpperCAmelCase			       =		toks[:max_length]
           if min_length is not None and len(lowercase				) < min_length and len(lowercase				) > 0:
               while len(lowercase				) < min_length:
                   UpperCAmelCase			       =		toks + toks
        # toks_str = [t[1] for t in toks]
           UpperCAmelCase			       =		[t[0] for t in toks]

           # Ensure consistency
           UpperCAmelCase			       =		tokenizer.decode(lowercase   ,				clean_up_tokenization_spaces=lowercase				)
           if " " not in output_txt and len(lowercase				) > 1:
               UpperCAmelCase			       =		(
                   tokenizer.decode([toks_ids[0]]   ,				clean_up_tokenization_spaces=lowercase				)
                   + ''' '''
                   + tokenizer.decode(toks_ids[1:]   ,				clean_up_tokenization_spaces=lowercase				)
               )
           if with_prefix_space:
               UpperCAmelCase			       =		''' ''' + output_txt
           UpperCAmelCase			       =		tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
           return output_txt, output_ids
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		'''Unicode €.'''
           UpperCAmelCase			       =		tokenizer(lowercase				)
           UpperCAmelCase			       =		[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
           self.assertEqual(encoded['''input_ids''']   ,				lowercase				)

           # decoding
           UpperCAmelCase			       =		tokenizer.decode(lowercase				)
           self.assertEqual(lowercase   ,				'''[CLS]Unicode €.[SEP]'''				)

           UpperCAmelCase			       =		tokenizer('''e è é ê ë'''				)
           UpperCAmelCase			       =		[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
           self.assertEqual(encoded['''input_ids''']   ,				lowercase				)
           # decoding
           UpperCAmelCase			       =		tokenizer.decode(lowercase				)
           self.assertEqual(lowercase   ,				'''[CLS]e è é ê ë[SEP]'''				)

           # encode/decode, but with `encode` instead of `__call__`
           self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''				)				)   ,				'''[CLS]e è é ê ë[SEP]'''				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           # fmt: off
           UpperCAmelCase			       =		[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
           # fmt: on
           UpperCAmelCase			       =		tokenizer(lowercase   ,				padding=lowercase   ,				return_tensors=lowercase				)
           self.assertIsInstance(lowercase   ,				lowercase				)

           if FRAMEWORK != "jax":
               UpperCAmelCase			       =		list(batch.input_ids.numpy()[0]				)
           else:
               UpperCAmelCase			       =		list(batch.input_ids.tolist()[0]				)

           self.assertListEqual(lowercase   ,				lowercase				)

           self.assertEqual((2, 38)   ,				batch.input_ids.shape				)
           self.assertEqual((2, 38)   ,				batch.attention_mask.shape				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           UpperCAmelCase			       =		tokenizer(lowercase   ,				padding=lowercase   ,				return_tensors=lowercase				)
           # check if input_ids are returned and no decoder_input_ids
           self.assertIn('''input_ids'''   ,				lowercase				)
           self.assertIn('''attention_mask'''   ,				lowercase				)
           self.assertNotIn('''decoder_input_ids'''   ,				lowercase				)
           self.assertNotIn('''decoder_attention_mask'''   ,				lowercase				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		[
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           UpperCAmelCase			       =		tokenizer(
               text_target=lowercase   ,				max_length=32   ,				padding='''max_length'''   ,				truncation=lowercase   ,				return_tensors=lowercase				)
           self.assertEqual(32   ,				targets['''input_ids'''].shape[1]				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"				):
                   self.assertNotEqual(tokenizer.model_max_length   ,				42				)

        # Now let's start the test
           UpperCAmelCase			       =		self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"				):
                   # Isolate this from the other tests because we save additional tokens/etc
                   UpperCAmelCase			       =		tempfile.mkdtemp()

                   UpperCAmelCase			       =		''' He is very happy, UNwant\u00E9d,running'''
                   UpperCAmelCase			       =		tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
                   tokenizer.save_pretrained(lowercase				)

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase				)
                   UpperCAmelCase			       =		after_tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
                   self.assertListEqual(lowercase   ,				lowercase				)

                   shutil.rmtree(lowercase				)

           UpperCAmelCase			       =		self.get_tokenizers(model_max_length=42				)
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"				):
                   # Isolate this from the other tests because we save additional tokens/etc
                   UpperCAmelCase			       =		tempfile.mkdtemp()

                   UpperCAmelCase			       =		''' He is very happy, UNwant\u00E9d,running'''
                   tokenizer.add_tokens(['''bim''', '''bambam''']				)
                   UpperCAmelCase			       =		tokenizer.additional_special_tokens
                   additional_special_tokens.append('''new_additional_special_token'''				)
                   tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}				)
                   UpperCAmelCase			       =		tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
                   tokenizer.save_pretrained(lowercase				)

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase				)
                   UpperCAmelCase			       =		after_tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
                   self.assertListEqual(lowercase   ,				lowercase				)
                   self.assertIn('''new_additional_special_token'''   ,				after_tokenizer.additional_special_tokens				)
                   self.assertEqual(after_tokenizer.model_max_length   ,				42				)

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase   ,				model_max_length=43				)
                   self.assertEqual(tokenizer.model_max_length   ,				43				)

                   shutil.rmtree(lowercase				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		[]
           if self.test_slow_tokenizer:
               tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())				)

           if self.test_rust_tokenizer:
               tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())				)

           for tokenizer_class, tokenizer_utils in tokenizer_list:
               with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer_utils.save_pretrained(lowercase				)

                   with open(os.path.join(lowercase   ,				'''special_tokens_map.json'''				)   ,				encoding='''utf-8'''				) as json_file:
                       UpperCAmelCase			       =		json.load(lowercase				)

                   with open(os.path.join(lowercase   ,				'''tokenizer_config.json'''				)   ,				encoding='''utf-8'''				) as json_file:
                       UpperCAmelCase			       =		json.load(lowercase				)

                   UpperCAmelCase			       =		[f"<extra_id_{i}>" for i in range(125				)]

                   UpperCAmelCase			       =		added_tokens_extra_ids + [
                       '''an_additional_special_token'''
                   ]
                   UpperCAmelCase			       =		added_tokens_extra_ids + [
                       '''an_additional_special_token'''
                   ]

                   with open(os.path.join(lowercase   ,				'''special_tokens_map.json'''				)   ,				'''w'''   ,				encoding='''utf-8'''				) as outfile:
                       json.dump(lowercase   ,				lowercase				)
                   with open(os.path.join(lowercase   ,				'''tokenizer_config.json'''				)   ,				'''w'''   ,				encoding='''utf-8'''				) as outfile:
                       json.dump(lowercase   ,				lowercase				)

                   # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                   # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                   # "special_tokens_map.json" files
                   UpperCAmelCase			       =		tokenizer_class.from_pretrained(
                       lowercase   ,				)
                   self.assertIn(
                       '''an_additional_special_token'''   ,				tokenizer_without_change_in_init.additional_special_tokens				)
                   self.assertEqual(
                       ['''an_additional_special_token''']   ,				tokenizer_without_change_in_init.convert_ids_to_tokens(
                           tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']				)				)   ,				)

                   # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                   UpperCAmelCase			       =		added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''   ,				lstrip=lowercase				)]
                   UpperCAmelCase			       =		tokenizer_class.from_pretrained(
                       lowercase   ,				additional_special_tokens=lowercase   ,				)

                   self.assertIn('''a_new_additional_special_token'''   ,				tokenizer.additional_special_tokens				)
                   self.assertEqual(
                       ['''a_new_additional_special_token''']   ,				tokenizer.convert_ids_to_tokens(
                           tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']				)				)   ,				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           self.assertEqual(tokenizer.decode([178]				)   ,				'''�'''				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Any				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           pass
       def     A      (	self						:	str				):



           '''simple docstring'''

           pass







       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_tokenizers(fast=lowercase   ,				do_lower_case=lowercase				)
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"				):
                   UpperCAmelCase			       =		['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                   UpperCAmelCase			       =		tokenizer.convert_tokens_to_string(lowercase				)

                   self.assertIsInstance(lowercase   ,				lowercase				)










'''simple docstring'''







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

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


A     =logging.get_logger(__name__)

A     ={
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}


class 			_a  (	__a ):
       __a				:     List[Any]   				=					"""marian"""
       __a				:     Union[str, Any]   				=					["""past_key_values"""]
       __a				:     List[str]   				=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
       def __init__(	self						:	List[Any]   ,				lowercase						:	Union[str, Any]=58_101   ,				lowercase						:	Tuple=None   ,				lowercase						:	str=1_024   ,				lowercase						:	Optional[int]=12   ,				lowercase						:	Optional[int]=4_096   ,				lowercase						:	int=16   ,				lowercase						:	List[Any]=12   ,				lowercase						:	int=4_096   ,				lowercase						:	Optional[int]=16   ,				lowercase						:	int=0.0   ,				lowercase						:	Tuple=0.0   ,				lowercase						:	Tuple=True   ,				lowercase						:	Union[str, Any]=True   ,				lowercase						:	List[Any]="gelu"   ,				lowercase						:	Tuple=1_024   ,				lowercase						:	str=0.1   ,				lowercase						:	str=0.0   ,				lowercase						:	Optional[int]=0.0   ,				lowercase						:	Dict=0.02   ,				lowercase						:	Union[str, Any]=58_100   ,				lowercase						:	List[str]=False   ,				lowercase						:	str=58_100   ,				lowercase						:	Any=0   ,				lowercase						:	Optional[Any]=0   ,				lowercase						:	Tuple=True   ,				**lowercase						:	Optional[int]   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		vocab_size
           UpperCAmelCase			       =		decoder_vocab_size or vocab_size
           UpperCAmelCase			       =		max_position_embeddings
           UpperCAmelCase			       =		d_model
           UpperCAmelCase			       =		encoder_ffn_dim
           UpperCAmelCase			       =		encoder_layers
           UpperCAmelCase			       =		encoder_attention_heads
           UpperCAmelCase			       =		decoder_ffn_dim
           UpperCAmelCase			       =		decoder_layers
           UpperCAmelCase			       =		decoder_attention_heads
           UpperCAmelCase			       =		dropout
           UpperCAmelCase			       =		attention_dropout
           UpperCAmelCase			       =		activation_dropout
           UpperCAmelCase			       =		activation_function
           UpperCAmelCase			       =		init_std
           UpperCAmelCase			       =		encoder_layerdrop
           UpperCAmelCase			       =		decoder_layerdrop
           UpperCAmelCase			       =		use_cache
           UpperCAmelCase			       =		encoder_layers
           UpperCAmelCase			       =		scale_embedding  # scale factor will be sqrt(d_model) if True
           UpperCAmelCase			       =		share_encoder_decoder_embeddings
           super().__init__(
               pad_token_id=lowercase   ,				eos_token_id=lowercase   ,				is_encoder_decoder=lowercase   ,				decoder_start_token_id=lowercase   ,				forced_eos_token_id=lowercase   ,				**lowercase   ,				)






class 			_a  (	__a ):
       @property
       # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
       def     A      (	self						:	int				):



           '''simple docstring'''

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

               if self.use_past:
                   UpperCAmelCase			       =		{0: '''batch'''}
                   UpperCAmelCase			       =		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
               else:
                   UpperCAmelCase			       =		{0: '''batch''', 1: '''decoder_sequence'''}
                   UpperCAmelCase			       =		{0: '''batch''', 1: '''decoder_sequence'''}

               if self.use_past:
                   self.fill_with_past_key_values_(lowercase   ,				direction='''inputs'''				)
           elif self.task == "causal-lm":
               # TODO: figure this case out.
               UpperCAmelCase			       =		OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ]				)
               if self.use_past:
                   UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_layers
                   for i in range(lowercase				):
                       UpperCAmelCase			       =		{0: '''batch''', 2: '''past_sequence + sequence'''}
                       UpperCAmelCase			       =		{0: '''batch''', 2: '''past_sequence + sequence'''}
           else:
               UpperCAmelCase			       =		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
       # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
       def     A      (	self						:	Any				):



           '''simple docstring'''

           if self.task in ["default", "seq2seq-lm"]:
               UpperCAmelCase			       =		super().outputs
           else:
               UpperCAmelCase			       =		super(lowercase   ,				self				).outputs
               if self.use_past:
                   UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_layers
                   for i in range(lowercase				):
                       UpperCAmelCase			       =		{0: '''batch''', 2: '''past_sequence + sequence'''}
                       UpperCAmelCase			       =		{0: '''batch''', 2: '''past_sequence + sequence'''}
           return common_outputs
       def     A      (	self						:	Dict   ,				lowercase						:	PreTrainedTokenizer   ,				lowercase						:	int = -1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional[TensorType] = None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		self._generate_dummy_inputs_for_encoder_and_decoder(
               lowercase   ,				lowercase   ,				lowercase   ,				lowercase   ,				lowercase				)

           # Generate decoder inputs
           UpperCAmelCase			       =		seq_length if not self.use_past else 1
           UpperCAmelCase			       =		self._generate_dummy_inputs_for_encoder_and_decoder(
               lowercase   ,				lowercase   ,				lowercase   ,				lowercase   ,				lowercase				)
           UpperCAmelCase			       =		{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
           UpperCAmelCase			       =		dict(**lowercase   ,				**lowercase				)

           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			       =		common_inputs['''input_ids'''].shape
               UpperCAmelCase			       =		common_inputs['''decoder_input_ids'''].shape[1]
               UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_attention_heads
               UpperCAmelCase			       =		(
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               UpperCAmelCase			       =		decoder_seq_length + 3
               UpperCAmelCase			       =		(
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               UpperCAmelCase			       =		torch.cat(
                   [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase   ,				lowercase				)]   ,				dim=1				)

               UpperCAmelCase			       =		[]
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_layers
               UpperCAmelCase			       =		min(lowercase   ,				lowercase				)
               UpperCAmelCase			       =		max(lowercase   ,				lowercase				) - min_num_layers
               UpperCAmelCase			       =		'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

               for _ in range(lowercase				):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(lowercase				),
                           torch.zeros(lowercase				),
                           torch.zeros(lowercase				),
                           torch.zeros(lowercase				),
                       )				)
               # TODO: test this.
               UpperCAmelCase			       =		encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
               for _ in range(lowercase   ,				lowercase				):
                   common_inputs["past_key_values"].append((torch.zeros(lowercase				), torch.zeros(lowercase				))				)
           return common_inputs
       def     A      (	self						:	int   ,				lowercase						:	PreTrainedTokenizer   ,				lowercase						:	int = -1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional[TensorType] = None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		self._generate_dummy_inputs_for_encoder_and_decoder(
               lowercase   ,				lowercase   ,				lowercase   ,				lowercase   ,				lowercase				)

           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			       =		common_inputs['''input_ids'''].shape
               # Not using the same length for past_key_values
               UpperCAmelCase			       =		seqlen + 2
               UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_layers
               UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_attention_heads
               UpperCAmelCase			       =		(
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               UpperCAmelCase			       =		common_inputs['''attention_mask'''].dtype
               UpperCAmelCase			       =		torch.cat(
                   [common_inputs['''attention_mask'''], torch.ones(lowercase   ,				lowercase   ,				dtype=lowercase				)]   ,				dim=1				)
               UpperCAmelCase			       =		[
                   (torch.zeros(lowercase				), torch.zeros(lowercase				)) for _ in range(lowercase				)
               ]
           return common_inputs
       def     A      (	self						:	str   ,				lowercase						:	PreTrainedTokenizer   ,				lowercase						:	int = -1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional[TensorType] = None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		compute_effective_axis_dimension(
               lowercase   ,				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
           UpperCAmelCase			       =		tokenizer.num_special_tokens_to_add(lowercase				)
           UpperCAmelCase			       =		compute_effective_axis_dimension(
               lowercase   ,				fixed_dimension=OnnxConfig.default_fixed_sequence   ,				num_token_to_add=lowercase				)

           # Generate dummy inputs according to compute batch and sequence
           UpperCAmelCase			       =		[''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
           UpperCAmelCase			       =		dict(tokenizer(lowercase   ,				return_tensors=lowercase				)				)
           return common_inputs
       def     A      (	self						:	List[str]   ,				lowercase						:	PreTrainedTokenizer   ,				lowercase						:	int = -1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional[TensorType] = None   ,				):



           '''simple docstring'''

           if self.task in ["default", "seq2seq-lm"]:
               UpperCAmelCase			       =		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   lowercase   ,				batch_size=lowercase   ,				seq_length=lowercase   ,				is_pair=lowercase   ,				framework=lowercase				)

           else:
               UpperCAmelCase			       =		self._generate_dummy_inputs_for_causal_lm(
                   lowercase   ,				batch_size=lowercase   ,				seq_length=lowercase   ,				is_pair=lowercase   ,				framework=lowercase				)

           return common_inputs
       def     A      (	self						:	List[Any]   ,				lowercase						:	Any   ,				lowercase						:	Tuple   ,				lowercase						:	Any   ,				lowercase						:	Any				):



           '''simple docstring'''

           if self.task in ["default", "seq2seq-lm"]:
               UpperCAmelCase			       =		super()._flatten_past_key_values_(lowercase   ,				lowercase   ,				lowercase   ,				lowercase				)
           else:
               UpperCAmelCase			       =		super(lowercase   ,				self				)._flatten_past_key_values_(
                   lowercase   ,				lowercase   ,				lowercase   ,				lowercase				)







       @property
       def     A      (	self						:	Any				):



           '''simple docstring'''

           return 1E-4




