




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[Any]									=     logging.get_logger(__name__)

UpperCAmelCase_		:				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      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""perceiver"""

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

										super().__init__(**__snake_case )

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




class 		__A      (			UpperCamelCase__			):

						@property
						def     A__						(		self   :Tuple ):
										'''simple docstring'''

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

						@property
						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										return 1E-4


						def     A__						(		self   :int    , __snake_case   :Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]    , __snake_case   :int = -1    , __snake_case   :int = -1    , __snake_case   :int = -1    , __snake_case   :bool = False    , __snake_case   :Optional[TensorType] = None    , __snake_case   :int = 3    , __snake_case   :int = 40    , __snake_case   :int = 40    , ):
										'''simple docstring'''

										if isinstance(__snake_case    , __snake_case ):
														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														__magic_name__	:	str								=compute_effective_axis_dimension(
														    __snake_case    , 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__	:	Optional[Any]								=preprocessor.num_special_tokens_to_add(__snake_case )
														__magic_name__	:	str								=compute_effective_axis_dimension(
														    __snake_case    , fixed_dimension=OnnxConfig.default_fixed_sequence    , num_token_to_add=__snake_case )
														# Generate dummy inputs according to compute batch and sequence
														__magic_name__	:	Union[str, Any]								=[""" """.join(["""a"""] ) * seq_length] * batch_size
														__magic_name__	:	Optional[Any]								=dict(preprocessor(__snake_case    , return_tensors=__snake_case ) )
														__magic_name__	:	Union[str, Any]								=inputs.pop("""input_ids""" )
														return inputs
										elif isinstance(__snake_case    , __snake_case ) 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__	:	Union[str, Any]								=compute_effective_axis_dimension(__snake_case    , fixed_dimension=OnnxConfig.default_fixed_batch )
														__magic_name__	:	Dict								=self._generate_dummy_images(__snake_case    , __snake_case    , __snake_case    , __snake_case )
														__magic_name__	:	Union[str, Any]								=dict(preprocessor(images=__snake_case    , return_tensors=__snake_case ) )
														__magic_name__	:	Optional[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.""" )










# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCAmelCase_		:				str									=     "3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
							import torch

							print("Torch version:", torch.__version__)
							print("Cuda available:", torch.cuda.is_available())
							print("Cuda version:", torch.version.cuda)
							print("CuDNN version:", torch.backends.cudnn.version())
							print("Number of GPUs available:", torch.cuda.device_count())
							print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
							print("Torch version:", None)

try:
							import deepspeed

							print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
							print("DeepSpeed version:", None)

try:
							import tensorflow as tf

							print("TensorFlow version:", tf.__version__)
							print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
							print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
							print("TensorFlow version:", None)




