






"""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


A__	:    int			 = logging.get_logger(__name__)

A__	:    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        lowercase__      (							_UpperCamelCase       ):
   _UpperCAmelCase		:Dict             =			'perceiver'


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

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







class        lowercase__      (							_UpperCamelCase       ):


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


   @property
   def      UpperCAmelCase__						(				self					:  Optional[Any] ):
          return 1E-4


   def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  List[Any]       ,	snake_case__					:  str = -1       ,	snake_case__					:  List[Any] = -1       ,	snake_case__					:  Any = -1       ,	snake_case__					:  Any = False       ,	snake_case__					:  Tuple = None       ,	snake_case__					:  Optional[Any] = 3       ,	snake_case__					:  List[Any] = 40       ,	snake_case__					:  Any = 40       ,	):
          # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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











"""simple docstring"""



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# 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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser

def 			_snake_case    (					)    ->     Tuple:
							lowerCamelCase_	:							Optional[int]									=ArgumentParser("Accelerate CLI tool"    ,						usage="accelerate <command> [<args>]"    ,						allow_abbrev=lowerCamelCase__  )
							lowerCamelCase_	:							Optional[int]									=parser.add_subparsers(help="accelerate command helpers"  )

							# Register commands
							get_config_parser(subparsers=lowerCamelCase__  )
							env_command_parser(subparsers=lowerCamelCase__  )
							launch_command_parser(subparsers=lowerCamelCase__  )
							tpu_command_parser(subparsers=lowerCamelCase__  )
							test_command_parser(subparsers=lowerCamelCase__  )

							# Let's go
							lowerCamelCase_	:							int									=parser.parse_args()

							if not hasattr(lowerCamelCase__    ,						"func"  ):
														parser.print_help()
														exit(1  )

							# Run
							args.func(lowerCamelCase__  )


if __name__ == "__main__":
						main()



