



"""simple docstring"""







import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 					A_(snake_case_   ,   unittest.TestCase      ):







			"""simple docstring"""



			a_       :			Any												=			None
			a_       :			Dict												=			BloomTokenizerFast
			a_       :			Dict												=			BloomTokenizerFast
			a_       :			Optional[int]												=			True
			a_       :			List[str]												=			False
			a_       :			str												=			"""tokenizer_file"""
			a_       :			Union[str, Any]												=			{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}
			def    _lowerCAmelCase		(	self							):
						super().setUp()
						_lowerCamelCase					:					List[str]	       =    BloomTokenizerFast.from_pretrained('bigscience/tokenizer'							)
						tokenizer.save_pretrained(self.tmpdirname							)
			def    _lowerCAmelCase		(	self				,   **A							):
						kwargs.update(self.special_tokens_map							)
						return BloomTokenizerFast.from_pretrained(self.tmpdirname				,   **A_							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_rust_tokenizer()

						_lowerCamelCase					:					str	       =    ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
						_lowerCamelCase					:					Optional[int]	       =    [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

						_lowerCamelCase					:					List[str]	       =    tokenizer.batch_encode_plus(A_							)['input_ids']
						self.assertListEqual(A_				,   A_							)

						_lowerCamelCase					:					Tuple	       =    tokenizer.batch_decode(A_							)
						self.assertListEqual(A_				,   A_							)
			def    _lowerCAmelCase		(	self				,   A=6							):






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
												_lowerCamelCase					:					Union[str, Any]	       =    self.rust_tokenizer_class.from_pretrained(A_				,   **A_							)
												# tokenizer_r.pad_token = None # Hotfixing padding = None
												# Simple input
												_lowerCamelCase					:					Union[str, Any]	       =    'This is a simple input'
												_lowerCamelCase					:					Any	       =    ['This is a simple input 1', 'This is a simple input 2']
												_lowerCamelCase					:					List[str]	       =    ('This is a simple input', 'This is a pair')
												_lowerCamelCase					:					Any	       =    [
												    ('This is a simple input 1', 'This is a simple input 2'),
												    ('This is a simple pair 1', 'This is a simple pair 2'),
												]

												# Simple input tests
												try:
															tokenizer_r.encode(A_				,   max_length=A_							)
															tokenizer_r.encode_plus(A_				,   max_length=A_							)

															tokenizer_r.batch_encode_plus(A_				,   max_length=A_							)
															tokenizer_r.encode(A_				,   max_length=A_							)
															tokenizer_r.batch_encode_plus(A_				,   max_length=A_							)
												except ValueError:
															self.fail('Bloom Tokenizer should be able to deal with padding'							)

												_lowerCamelCase					:					List[Any]	       =    None  # Hotfixing padding = None
												self.assertRaises(A_				,   tokenizer_r.encode				,   A_				,   max_length=A_				,   padding='max_length'							)

												# Simple input
												self.assertRaises(A_				,   tokenizer_r.encode_plus				,   A_				,   max_length=A_				,   padding='max_length'							)

												# Simple input
												self.assertRaises(
												    A_				,   tokenizer_r.batch_encode_plus				,   A_				,   max_length=A_				,   padding='max_length'				,   )

												# Pair input
												self.assertRaises(A_				,   tokenizer_r.encode				,   A_				,   max_length=A_				,   padding='max_length'							)

												# Pair input
												self.assertRaises(A_				,   tokenizer_r.encode_plus				,   A_				,   max_length=A_				,   padding='max_length'							)

												# Pair input
												self.assertRaises(
												    A_				,   tokenizer_r.batch_encode_plus				,   A_				,   max_length=A_				,   padding='max_length'				,   )
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Tuple	       =    self.get_rust_tokenizer()
						_lowerCamelCase					:					Any	       =    load_dataset('xnli'				,   'all_languages'				,   split='test'				,   streaming=A_							)

						_lowerCamelCase					:					List[str]	       =    next(iter(A_							)							)['premise']  # pick up one data
						_lowerCamelCase					:					Dict	       =    list(sample_data.values()							)

						_lowerCamelCase					:					Optional[int]	       =    list(map(tokenizer.encode				,   A_							)							)
						_lowerCamelCase					:					int	       =    [tokenizer.decode(A_				,   clean_up_tokenization_spaces=A_							) for x in output_tokens]
						self.assertListEqual(A_				,   A_							)

			def    _lowerCAmelCase		(	self							):
						self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map							)				,   1							)
						self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()							)[0]							)				,   1							)







'''simple docstring'''
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					:    Any           = logging.get_logger(__name__)

lowerCAmelCase					:    Optional[int]           = {
    '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 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""perceiver"""







				def __init__(       self						,				A_=256						,				A_=1280						,				A_=768						,				A_=1						,				A_=26						,				A_=8						,				A_=8						,				A_=None						,				A_=None						,				A_="kv"						,				A_=1						,				A_=1						,				A_="gelu"						,				A_=0.1						,				A_=0.02						,				A_=1e-12						,				A_=True						,				A_=262						,				A_=2048						,				A_=56						,				A_=[368, 496]						,				A_=16						,				A_=1920						,				A_=16						,				A_=[1, 16, 224, 224]						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_     )

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


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







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







											'''simple docstring'''






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







				@property
				def 						UpperCAmelCase_    (       self     )->		float:







											'''simple docstring'''






											return 1e-4







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = -1						,				A_ = -1						,				A_ = -1						,				A_ = False						,				A_ = None						,				A_ = 3						,				A_ = 40						,				A_ = 40						,				)->		Mapping[str, Any]:







											'''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
																		UpperCamelCase   			=     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
																		UpperCamelCase   			=     preprocessor.num_special_tokens_to_add(A_     )
																		UpperCamelCase   			=     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
																		UpperCamelCase   			=     [' '.join(['a']     ) * seq_length] * batch_size
																		UpperCamelCase   			=     dict(preprocessor(A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     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
																		UpperCamelCase   			=     compute_effective_axis_dimension(A_						,				fixed_dimension=OnnxConfig.default_fixed_batch     )
																		UpperCamelCase   			=     self._generate_dummy_images(A_						,				A_						,				A_						,				A_     )
																		UpperCamelCase   			=     dict(preprocessor(images=A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     inputs.pop('pixel_values'     )
																		return inputs
											else:
																		raise ValueError(
																		    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'     )




