






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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				int									=							"perceiver"







			def __init__(     self:  List[str]				,     UpperCAmelCase_:  Dict=256				,     UpperCAmelCase_:  Optional[Any]=1_280				,     UpperCAmelCase_:  List[str]=768				,     UpperCAmelCase_:  Any=1				,     UpperCAmelCase_:  Union[str, Any]=26				,     UpperCAmelCase_:  int=8				,     UpperCAmelCase_:  Optional[int]=8				,     UpperCAmelCase_:  Union[str, Any]=None				,     UpperCAmelCase_:  List[Any]=None				,     UpperCAmelCase_:  Tuple="kv"				,     UpperCAmelCase_:  Union[str, Any]=1				,     UpperCAmelCase_:  List[str]=1				,     UpperCAmelCase_:  Any="gelu"				,     UpperCAmelCase_:  Optional[int]=0.1				,     UpperCAmelCase_:  Tuple=0.02				,     UpperCAmelCase_:  int=1E-12				,     UpperCAmelCase_:  str=True				,     UpperCAmelCase_:  Optional[int]=262				,     UpperCAmelCase_:  str=2_048				,     UpperCAmelCase_:  Tuple=56				,     UpperCAmelCase_:  Dict=[368, 496]				,     UpperCAmelCase_:  Optional[Any]=16				,     UpperCAmelCase_:  List[str]=1_920				,     UpperCAmelCase_:  Tuple=16				,     UpperCAmelCase_:  Union[str, Any]=[1, 16, 224, 224]				,     **UpperCAmelCase_:  Tuple				,     ):







									'''simple docstring'''




									super().__init__(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  num_latents
									_SCREAMING_SNAKE_CASE     				=  d_latents
									_SCREAMING_SNAKE_CASE     				=  d_model
									_SCREAMING_SNAKE_CASE     				=  num_blocks
									_SCREAMING_SNAKE_CASE     				=  num_self_attends_per_block
									_SCREAMING_SNAKE_CASE     				=  num_self_attention_heads
									_SCREAMING_SNAKE_CASE     				=  num_cross_attention_heads
									_SCREAMING_SNAKE_CASE     				=  qk_channels
									_SCREAMING_SNAKE_CASE     				=  v_channels
									_SCREAMING_SNAKE_CASE     				=  cross_attention_shape_for_attention
									_SCREAMING_SNAKE_CASE     				=  self_attention_widening_factor
									_SCREAMING_SNAKE_CASE     				=  cross_attention_widening_factor
									_SCREAMING_SNAKE_CASE     				=  hidden_act
									_SCREAMING_SNAKE_CASE     				=  attention_probs_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  initializer_range
									_SCREAMING_SNAKE_CASE     				=  layer_norm_eps
									_SCREAMING_SNAKE_CASE     				=  use_query_residual
									# masked language modeling attributes
									_SCREAMING_SNAKE_CASE     				=  vocab_size
									_SCREAMING_SNAKE_CASE     				=  max_position_embeddings
									# image classification attributes
									_SCREAMING_SNAKE_CASE     				=  image_size
									# flow attributes
									_SCREAMING_SNAKE_CASE     				=  train_size
									# multimodal autoencoding attributes
									_SCREAMING_SNAKE_CASE     				=  num_frames
									_SCREAMING_SNAKE_CASE     				=  audio_samples_per_frame
									_SCREAMING_SNAKE_CASE     				=  samples_per_patch
									_SCREAMING_SNAKE_CASE     				=  output_shape



class 				__UpperCAmelCase		(_UpperCAmelCase   ):







			@property
			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									if self.task == "multiple-choice":
															_SCREAMING_SNAKE_CASE     				=  {0: """batch""", 1: """choice""", 2: """sequence"""}
									else:
															_SCREAMING_SNAKE_CASE     				=  {0: """batch""", 1: """sequence"""}
									return OrderedDict(
									    [
									        ("""inputs""", dynamic_axis),
									        ("""attention_mask""", dynamic_axis),
									    ]					)







			@property
			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									return 1E-4







			def 							UpperCamelCase				(     self:  Union[str, Any]				,     UpperCAmelCase_:  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]				,     UpperCAmelCase_:  int = -1				,     UpperCAmelCase_:  int = -1				,     UpperCAmelCase_:  int = -1				,     UpperCAmelCase_:  bool = False				,     UpperCAmelCase_:  Optional[TensorType] = None				,     UpperCAmelCase_:  int = 3				,     UpperCAmelCase_:  int = 40				,     UpperCAmelCase_:  int = 40				,     ):







									'''simple docstring'''




									if isinstance(UpperCAmelCase_				,     UpperCAmelCase_					):
															# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
															_SCREAMING_SNAKE_CASE     				=  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
															_SCREAMING_SNAKE_CASE     				=  preprocessor.num_special_tokens_to_add(UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  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
															_SCREAMING_SNAKE_CASE     				=  [""" """.join(["""a"""]					) * seq_length] * batch_size
															_SCREAMING_SNAKE_CASE     				=  dict(preprocessor(UpperCAmelCase_				,     return_tensors=UpperCAmelCase_					)					)
															_SCREAMING_SNAKE_CASE     				=  inputs.pop("""input_ids"""					)
															return inputs
									elif isinstance(UpperCAmelCase_				,     UpperCAmelCase_					) and preprocessor.model_input_names[0] == "pixel_values":
															# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
															_SCREAMING_SNAKE_CASE     				=  compute_effective_axis_dimension(UpperCAmelCase_				,     fixed_dimension=OnnxConfig.default_fixed_batch					)
															_SCREAMING_SNAKE_CASE     				=  self._generate_dummy_images(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  dict(preprocessor(images=UpperCAmelCase_				,     return_tensors=UpperCAmelCase_					)					)
															_SCREAMING_SNAKE_CASE     				=  inputs.pop("""pixel_values"""					)
															return inputs
									else:
															raise ValueError(
															    """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""					)









import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				int									=							TransfoXLTokenizer
			__snake_case    :				Tuple									=							False
			__snake_case    :				List[Any]									=							False







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									super().setUp()

									_SCREAMING_SNAKE_CASE     				=  [
									    """<unk>""",
									    """[CLS]""",
									    """[SEP]""",
									    """want""",
									    """unwanted""",
									    """wa""",
									    """un""",
									    """running""",
									    """,""",
									    """low""",
									    """l""",
									]
									_SCREAMING_SNAKE_CASE     				=  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 							UpperCamelCase				(     self:  Any				,     **UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  True
									return TransfoXLTokenizer.from_pretrained(self.tmpdirname				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """<unk> UNwanted , running"""
									_SCREAMING_SNAKE_CASE     				=  """<unk> unwanted, running"""
									return input_text, output_text







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(vocab_file=self.vocab_file				,     lower_case=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize("""<unk> UNwanted , running"""					)
									self.assertListEqual(UpperCAmelCase_				,     ["""<unk>""", """unwanted""", """,""", """running"""]					)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     [0, 4, 8, 7]					)







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(lower_case=UpperCAmelCase_					)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """					)				,     ["""hello""", """!""", """how""", """are""", """you""", """?"""]					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(lower_case=UpperCAmelCase_					)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """					)				,     ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(lower_case=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  """Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
									_SCREAMING_SNAKE_CASE     				=  [
									    """Hello""",
									    """(""",
									    """bracket""",
									    """)""",
									    """and""",
									    """side""",
									    """@-@""",
									    """scrolled""",
									    """[""",
									    """and""",
									    """]""",
									    """Henry""",
									    """'s""",
									    """$""",
									    """5""",
									    """@,@""",
									    """000""",
									    """with""",
									    """3""",
									    """@.@""",
									    """34""",
									    """m""",
									    """.""",
									    """What""",
									    """'s""",
									    """up""",
									    """!""",
									    """?""",
									]

									self.assertListEqual(tokenizer.tokenize(UpperCAmelCase_					)				,     UpperCAmelCase_					)

									self.assertEqual(tokenizer.convert_tokens_to_string(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_tokenizer()
									_SCREAMING_SNAKE_CASE     				=  len(UpperCAmelCase_					)

									tokenizer.add_tokens(["""new1""", """new2"""]					)
									tokenizer.move_added_token("""new1"""				,     1					)

									# Check that moved token is not copied (duplicate)
									self.assertEqual(len(UpperCAmelCase_					)				,     original_len + 2					)
									# Check that token is moved to specified id
									self.assertEqual(tokenizer.encode("""new1"""					)				,     [1]					)
									self.assertEqual(tokenizer.decode([1]					)				,     """new1"""					)

