





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__   : List[str]					  = logging.get_logger(__name__)

UpperCamelCase__   : Union[str, Any]					  = {
    "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  ):







					'''simple docstring'''

					lowerCamelCase     :     Tuple			    =       'perceiver'





					def __init__(						self    :  str ,					__lowercase    :  Union[str, Any]=2_56 ,					__lowercase    :  Dict=12_80 ,					__lowercase    :  List[str]=7_68 ,					__lowercase    :  int=1 ,					__lowercase    :  Any=26 ,					__lowercase    :  Tuple=8 ,					__lowercase    :  Any=8 ,					__lowercase    :  int=None ,					__lowercase    :  Tuple=None ,					__lowercase    :  Union[str, Any]="kv" ,					__lowercase    :  Optional[int]=1 ,					__lowercase    :  str=1 ,					__lowercase    :  Tuple="gelu" ,					__lowercase    :  Optional[int]=0.1 ,					__lowercase    :  Dict=0.02 ,					__lowercase    :  Optional[int]=1e-12 ,					__lowercase    :  List[Any]=True ,					__lowercase    :  List[str]=2_62 ,					__lowercase    :  Tuple=20_48 ,					__lowercase    :  int=56 ,					__lowercase    :  int=[3_68, 4_96] ,					__lowercase    :  Tuple=16 ,					__lowercase    :  List[Any]=19_20 ,					__lowercase    :  Optional[Any]=16 ,					__lowercase    :  Tuple=[1, 16, 2_24, 2_24] ,					**__lowercase    :  Tuple ,					):






						'''simple docstring'''







						super().__init__(**lowercase_		)

						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   _UpperCamelCase  (       _UpperCAmelCase  ):







					'''simple docstring'''

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






						'''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        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						return 1e-4





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






						'''simple docstring'''







						if isinstance(lowercase_ ,					lowercase_		):
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							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_								=     preprocessor.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(["""a"""]		) * seq_length] * batch_size
							UpperCAmelCase_								=     dict(preprocessor(lowercase_ ,					return_tensors=lowercase_		)		)
							UpperCAmelCase_								=     inputs.pop("""input_ids"""		)
							return inputs
						elif isinstance(lowercase_ ,					lowercase_		) 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(lowercase_ ,					fixed_dimension=OnnxConfig.default_fixed_batch		)
							UpperCAmelCase_								=     self._generate_dummy_images(lowercase_ ,					lowercase_ ,					lowercase_ ,					lowercase_		)
							UpperCAmelCase_								=     dict(preprocessor(images=lowercase_ ,					return_tensors=lowercase_		)		)
							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."""		)









import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def    A_(			A				,      A   ):
	assert isinstance(A				,      A   )
	assert dataset.num_rows == 4
	assert dataset.num_columns == 1
	assert dataset.column_names == ["text"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""				,      [False, True]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A				,      keep_in_memory=A   ).read()
	_check_text_dataset(A				,      A   )




@pytest.mark.parametrize(
    """features"""				,      [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,      )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     features.copy() if features else default_expected_features
	UpperCAmelCase_								=     (
	    Features({feature: Value(A   ) for feature, dtype in features.items()}   ) if features is not None else None
	)
	UpperCAmelCase_								=     TextDatasetReader(A				,      features=A				,      cache_dir=A   ).read()
	_check_text_dataset(A				,      A   )




@pytest.mark.parametrize("""split"""				,      [None, NamedSplit("""train"""   ), """train""", """test"""]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A				,      split=A   ).read()
	_check_text_dataset(A				,      A   )
	assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""				,      [str, list]   )
def    A_(			A				,      A				,      A   ):
	if issubclass(A				,      A   ):
		UpperCAmelCase_								=     text_path
	elif issubclass(A				,      A   ):
		UpperCAmelCase_								=     [text_path]
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A   ).read()
	_check_text_dataset(A				,      A   )




def    A_(			A				,      A				,      A=("train",)   ):
	assert isinstance(A				,      A   )
	for split in splits:
		UpperCAmelCase_								=     dataset_dict[split]
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
			assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""				,      [False, True]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase_								=     TextDatasetReader({"""train""": text_path}				,      cache_dir=A				,      keep_in_memory=A   ).read()
	_check_text_datasetdict(A				,      A   )




@pytest.mark.parametrize(
    """features"""				,      [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,      )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     features.copy() if features else default_expected_features
	UpperCAmelCase_								=     (
	    Features({feature: Value(A   ) for feature, dtype in features.items()}   ) if features is not None else None
	)
	UpperCAmelCase_								=     TextDatasetReader({"""train""": text_path}				,      features=A				,      cache_dir=A   ).read()
	_check_text_datasetdict(A				,      A   )




@pytest.mark.parametrize("""split"""				,      [None, NamedSplit("""train"""   ), """train""", """test"""]   )
def    A_(			A				,      A				,      A   ):
	if split:
		UpperCAmelCase_								=     {split: text_path}
	else:
		UpperCAmelCase_								=     """train"""
		UpperCAmelCase_								=     {"""train""": text_path, """test""": text_path}
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A   ).read()
	_check_text_datasetdict(A				,      A				,      splits=list(path.keys()   )   )
	assert all(dataset[split].split == split for split in path.keys()   )


