




import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase       :       Optional[Any]        =    get_tests_dir('fixtures/spiece.model')




@require_sentencepiece
@require_tokenizers
class 						__lowercase		(A__     ,		unittest.TestCase							):




      """simple docstring"""





      _snake_case							     =						AlbertTokenizer
      _snake_case							     =						AlbertTokenizerFast
      _snake_case							     =						True
      _snake_case							     =						True
      _snake_case							     =						True

      def 	UpperCAmelCase      (      self	)		->				str:
         super().setUp()

         # We have a SentencePiece fixture for testing
         snake_case      :				Optional[int]				=       AlbertTokenizer(_A	)
         tokenizer.save_pretrained(self.tmpdirname	)

      def 	UpperCAmelCase      (      self				,      A	)		->				Optional[int]:
         snake_case      :				Optional[Any]				=       "this is a test"
         snake_case      :				Optional[Any]				=       "this is a test"
         return input_text, output_text

      def 	UpperCAmelCase      (      self	)		->				Tuple:
         snake_case      :				Tuple				=       "<pad>"
         snake_case      :				str				=       0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A	)				,      _A	)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A	)				,      _A	)

      def 	UpperCAmelCase      (      self	)		->				Union[str, Any]:
         snake_case      :				Tuple				=       list(self.get_tokenizer().get_vocab().keys()	)

         self.assertEqual(vocab_keys[0]				,      """<pad>"""	)
         self.assertEqual(vocab_keys[1]				,      """<unk>"""	)
         self.assertEqual(vocab_keys[-1]				,      """▁eloquent"""	)
         self.assertEqual(len(_A	)				,      3_0_0_0_0	)

      def 	UpperCAmelCase      (      self	)		->				List[Any]:
         self.assertEqual(self.get_tokenizer().vocab_size				,      3_0_0_0_0	)

      def 	UpperCAmelCase      (      self	)		->				List[Any]:






         if not self.test_rust_tokenizer:
            return

         snake_case      :				List[str]				=       self.get_tokenizer()
         snake_case      :				Tuple				=       self.get_rust_tokenizer()

         snake_case      :				List[str]				=       "I was born in 92000, and this is falsé."

         snake_case      :				str				=       tokenizer.tokenize(_A	)
         snake_case      :				List[Any]				=       rust_tokenizer.tokenize(_A	)
         self.assertListEqual(_A				,      _A	)

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

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

      def 	UpperCAmelCase      (      self	)		->				Optional[Any]:
         snake_case      :				Any				=       AlbertTokenizer(_A				,      keep_accents=_A	)

         snake_case      :				List[Any]				=       tokenizer.tokenize("""This is a test"""	)
         self.assertListEqual(_A				,      ["""▁this""", """▁is""", """▁a""", """▁test"""]	)

         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A	)				,      [4_8, 2_5, 2_1, 1_2_8_9]	)

         snake_case      :				int				=       tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
         self.assertListEqual(
             _A				,      ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]	)
         snake_case      :				int				=       tokenizer.convert_tokens_to_ids(_A	)
         self.assertListEqual(_A				,      [3_1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]	)

         snake_case      :				List[str]				=       tokenizer.convert_ids_to_tokens(_A	)
         self.assertListEqual(
             _A				,      ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]				,      )

      def 	UpperCAmelCase      (      self	)		->				List[Any]:
         snake_case      :				List[str]				=       AlbertTokenizer(_A	)

         snake_case      :				List[str]				=       tokenizer.encode("""sequence builders"""	)
         snake_case      :				Dict				=       tokenizer.encode("""multi-sequence build"""	)

         snake_case      :				Tuple				=       tokenizer.build_inputs_with_special_tokens(_A	)
         snake_case      :				Union[str, Any]				=       tokenizer.build_inputs_with_special_tokens(_A				,      _A	)

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]

      @slow
      def 	UpperCAmelCase      (      self	)		->				List[str]:
         snake_case      :				List[str]				=       {"attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "input_ids": [[2, 2_1_9_7_0, 1_3, 5, 6_0_9_2, 1_6_7, 2_8, 7_1_0_3, 2_1_5_3, 6_7_3, 8, 7_0_2_8, 1_2_0_5_1, 1_8, 1_7, 7_1_0_3, 2_1_5_3, 6_7_3, 8, 3_5_1_5, 1_8_6_8_4, 8, 4_4_6_1, 6, 1_9_2_7, 2_9_7, 8, 1_2_0_6_0, 2_6_0_7, 1_8, 1_3, 5, 4_4_6_1, 1_5, 1_0_5_3_8, 3_8, 8, 1_3_5, 1_5, 8_2_2, 5_8, 1_5, 9_9_3, 1_0_3_6_3, 1_5, 1_4_6_0, 8_0_0_5, 4_4_6_1, 1_5, 9_9_3, 2_5_5, 2_3_2_8, 9, 9, 9, 6, 2_6, 1_1_1_2, 8_1_6, 3_2_6_0, 1_3, 5, 1_0_3, 2_3_7_7, 6, 1_7, 1_1_1_2, 8_1_6, 2_7_8_2, 1_3, 5, 1_0_3, 1_0_6_4_1, 6, 2_9, 8_4, 2_5_1_2, 2_4_3_0, 7_8_2, 1_8_6_8_4, 2_7_6_1, 1_9, 8_0_8, 2_4_3_0, 2_5_5_6, 1_7, 8_5_5, 1_4_8_0, 9_4_7_7, 4_0_9_1, 1_2_8, 1_1_7_1_2, 1_5, 7_1_0_3, 2_1_5_3, 6_7_3, 1_7, 2_4_8_8_3, 9_9_9_0, 9, 3], [2, 1_1_5_0_2, 2_5, 1_0_0_6, 2_0, 7_8_2, 8, 1_1_8_0_9, 8_5_5, 1_7_3_2, 1_9_3_9_3, 1_8_6_6_7, 3_7, 3_6_7, 2_1_0_1_8, 6_9, 1_8_5_4, 3_4, 1_1_8_6_0, 1_9_1_2_4, 2_7, 1_5_6, 2_2_5, 1_7, 1_9_3, 4_1_4_1, 1_9, 6_5, 9_1_2_4, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 1_4, 2_2_3_1, 8_8_6, 2_3_8_5, 1_7_6_5_9, 8_4, 1_4, 1_6_7_9_2, 1_9_5_2, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=_A				,      model_name="""albert-base-v2"""				,      revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"""				,      )






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


__lowerCamelCase					:	Optional[int]       =  logging.get_logger(__name__)

__lowerCamelCase					:	Tuple       =  {
    '''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 		a__						(					A__					):
					A	       =							'perceiver'

					def __init__(    self      :       List[Any],_A      :       Tuple=256,_A      :       str=1280,_A      :       List[Any]=768,_A      :       Union[str, Any]=1,_A      :       Union[str, Any]=26,_A      :       List[str]=8,_A      :       List[Any]=8,_A      :       List[Any]=None,_A      :       List[Any]=None,_A      :       Union[str, Any]="kv",_A      :       Any=1,_A      :       int=1,_A      :       Dict="gelu",_A      :       Any=0.1,_A      :       int=0.02,_A      :       int=1E-12,_A      :       Any=True,_A      :       Optional[Any]=262,_A      :       List[Any]=2048,_A      :       str=56,_A      :       Optional[int]=[368, 496],_A      :       Dict=16,_A      :       Tuple=1920,_A      :       List[Any]=16,_A      :       str=[1, 16, 224, 224],**_A      :       Optional[Any],):







							"""simple docstring"""






							super().__init__(**_A    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    num_latents
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    d_latents
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    d_model
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    num_blocks
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    num_self_attends_per_block
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_self_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    num_cross_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    qk_channels
							SCREAMING_SNAKE_CASE_  :		Any				      =    v_channels
							SCREAMING_SNAKE_CASE_  :		Any				      =    cross_attention_shape_for_attention
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self_attention_widening_factor
							SCREAMING_SNAKE_CASE_  :		Any				      =    cross_attention_widening_factor
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Any				      =    initializer_range
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    layer_norm_eps
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    use_query_residual
							# masked language modeling attributes
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    max_position_embeddings
							# image classification attributes
							SCREAMING_SNAKE_CASE_  :		Dict				      =    image_size
							# flow attributes
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    train_size
							# multimodal autoencoding attributes
							SCREAMING_SNAKE_CASE_  :		str				      =    num_frames
							SCREAMING_SNAKE_CASE_  :		Any				      =    audio_samples_per_frame
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    samples_per_patch
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    output_shape







class 		a__						(					A__					):

					@property
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






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

					@property
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return 1E-4






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







							"""simple docstring"""






							if isinstance(_A,_A    ):
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    compute_effective_axis_dimension(
									    _A,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_  :		Tuple				      =    preprocessor.num_special_tokens_to_add(_A    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    compute_effective_axis_dimension(
									    _A,fixed_dimension=OnnxConfig.default_fixed_sequence,num_token_to_add=_A    )
									# Generate dummy inputs according to compute batch and sequence
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [" ".join(["a"]    ) * seq_length] * batch_size
									SCREAMING_SNAKE_CASE_  :		str				      =    dict(preprocessor(_A,return_tensors=_A    )    )
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    inputs.pop("input_ids"    )
									return inputs
							elif isinstance(_A,_A    ) 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_  :		Any				      =    compute_effective_axis_dimension(_A,fixed_dimension=OnnxConfig.default_fixed_batch    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self._generate_dummy_images(_A,_A,_A,_A    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    dict(preprocessor(images=_A,return_tensors=_A    )    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    inputs.pop("pixel_values"    )
									return inputs
							else:
									raise ValueError(
									    "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."    )
