
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


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

snake_case :		str							      =  {
    "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       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'perceiver'







			def __init__(				self						,   _a=256						,   _a=1_280						,   _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=2_048						,   _a=56						,   _a=[368, 496]						,   _a=16						,   _a=1_920						,   _a=16						,   _a=[1, 16, 224, 224]						,   **_a						,   ):
				super().__init__(**_a		)

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







class       _snake_case					(     snake_case				):







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







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return 1e-4







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = -1						,   _a = -1						,   _a = -1						,   _a = False						,   _a = None						,   _a = 3						,   _a = 40						,   _a = 40						,   ):
				# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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


snake_case :		Optional[int]							      =  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"






def   lowerCAmelCase_						(					_snake_case      :  bytes   )				->		bytes:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Tuple	   =     F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
		raise TypeError(_snake_case   )

	__magic_name__		:		Optional[int]	   =     "".join(bin(_snake_case   )[2:].zfill(8   ) for byte in data   )

	__magic_name__		:		List[Any]	   =     len(_snake_case   ) % 6 != 0

	if padding_needed:
		# The padding that will be added later
		__magic_name__		:		List[str]	   =     B"=" * ((6 - len(_snake_case   ) % 6) // 2)

		# Append binary_stream with arbitrary binary digits (0's by default) to make its
		# length a multiple of 6.
		binary_stream += "0" * (6 - len(_snake_case   ) % 6)
	else:
		__magic_name__		:		List[str]	   =     B""

	# Encode every 6 binary digits to their corresponding Base64 character
	return (
	    "".join(
	        B64_CHARSET[int(binary_stream[index : index + 6]      ,   2   )]
	        for index in range(0      ,   len(_snake_case   )      ,   6   )   ).encode()
	    + padding
	)






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		bytes:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   _snake_case   ) and not isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		List[str]	   =     (
		    "argument should be a bytes-like object or ASCII string, "
		    F'''not \'{encoded_data.__class__.__name__}\''''
		)
		raise TypeError(_snake_case   )

	# In case encoded_data is a bytes-like object, make sure it contains only
	# ASCII characters so we convert it to a string object
	if isinstance(_snake_case      ,   _snake_case   ):
		try:
			__magic_name__		:		List[Any]	   =     encoded_data.decode("utf-8"   )
		except UnicodeDecodeError:
			raise ValueError("base64 encoded data should only contain ASCII characters"   )

	__magic_name__		:		List[str]	   =     encoded_data.count("="   )

	# Check if the encoded string contains non base64 characters
	if padding:
		assert all(
		    char in B64_CHARSET for char in encoded_data[:-padding]   ), "Invalid base64 character(s) found."
	else:
		assert all(
		    char in B64_CHARSET for char in encoded_data   ), "Invalid base64 character(s) found."

	# Check the padding
	assert len(_snake_case   ) % 4 == 0 and padding < 3, "Incorrect padding"

	if padding:
		# Remove padding if there is one
		__magic_name__		:		Optional[int]	   =     encoded_data[:-padding]

		__magic_name__		:		Dict	   =     "".join(
		    bin(B64_CHARSET.index(_snake_case   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
	else:
		__magic_name__		:		Union[str, Any]	   =     "".join(
		    bin(B64_CHARSET.index(_snake_case   )   )[2:].zfill(6   ) for char in encoded_data   )

	__magic_name__		:		List[Any]	   =     [
	    int(binary_stream[index : index + 8]      ,   2   )
	    for index in range(0      ,   len(_snake_case   )      ,   8   )
	]

	return bytes(_snake_case   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
