

"""simple docstring"""




import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
	import tensorflow as tf

	tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
	import torch

if is_datasets_available():
	from datasets import load_dataset


__A     :    int    =		5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					return PTtoTFCommand(
					    args.model_name			,							args.local_dir			,							args.max_error			,							args.new_weights			,							args.no_pr			,							args.push			,							args.extra_commit_description			,							args.override_model_class			,							)





class 			__UpperCamelCase (   _A						):


		@staticmethod
		def 					SCREAMING_SNAKE_CASE__   (__SCREAMING_SNAKE_CASE :		ArgumentParser):
							A			=					parser.add_parser(
							    "pt-to-tf"     ,     help=(
							        "CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
							        " Can also be used to validate existing weights without opening PRs, with --no-pr."
							    )     ,     )
							train_parser.add_argument(
							    "--model-name"     ,     type=__SCREAMING_SNAKE_CASE     ,     required=__SCREAMING_SNAKE_CASE     ,     help="The model name, including owner/organization, as seen on the hub."     ,     )
							train_parser.add_argument(
							    "--local-dir"     ,     type=__SCREAMING_SNAKE_CASE     ,     default=""     ,     help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"     ,     )
							train_parser.add_argument(
							    "--max-error"     ,     type=__SCREAMING_SNAKE_CASE     ,     default=__SCREAMING_SNAKE_CASE     ,     help=(
							        F"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
							    )     ,     )
							train_parser.add_argument(
							    "--new-weights"     ,     action="store_true"     ,     help="Optional flag to create new TensorFlow weights, even if they already exist."     ,     )
							train_parser.add_argument(
							    "--no-pr"     ,     action="store_true"     ,     help="Optional flag to NOT open a PR with converted weights.")
							train_parser.add_argument(
							    "--push"     ,     action="store_true"     ,     help="Optional flag to push the weights directly to `main` (requires permissions)"     ,     )
							train_parser.add_argument(
							    "--extra-commit-description"     ,     type=__SCREAMING_SNAKE_CASE     ,     default=""     ,     help="Optional additional commit description to use when opening a PR (e.g. to tag the owner)."     ,     )
							train_parser.add_argument(
							    "--override-model-class"     ,     type=__SCREAMING_SNAKE_CASE     ,     default=__SCREAMING_SNAKE_CASE     ,     help="If you think you know better than the auto-detector, you can specify the model class here. "
							    "Can be either an AutoModel class or a specific model class like BertForSequenceClassification."     ,     )
							train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE)


		@staticmethod
		def 					SCREAMING_SNAKE_CASE__   (__SCREAMING_SNAKE_CASE :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple):
							A			=					set(pt_outputs.keys())
							A			=					set(tf_outputs.keys())
							if pt_out_attrs != tf_out_attrs:
												raise ValueError(
												    F"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
												    F""" {tf_out_attrs})""")

							# 2. For each output attribute, computes the difference
							def _find_pt_tf_differences(__SCREAMING_SNAKE_CASE :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Dict     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple=""):
												# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
												# recursivelly, keeping the name of the attribute.
												if isinstance(__SCREAMING_SNAKE_CASE     ,     torch.Tensor):
																	A			=					np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
																	A			=					tensor_difference
												else:
																	A			=					attr_name
																	for i, pt_item in enumerate(__SCREAMING_SNAKE_CASE):
																						# If it is a named attribute, we keep the name. Otherwise, just its index.
																						if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																											A			=					root_name + pt_item
																											A			=					tf_out[pt_item]
																											A			=					pt_out[pt_item]
																						else:
																											A			=					root_name + F"""[{i}]"""
																											A			=					tf_out[i]
																						A			=					_find_pt_tf_differences(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

												return differences

							return _find_pt_tf_differences(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     {})


		def __init__(self :		Any     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		float     ,     __SCREAMING_SNAKE_CASE :		bool     ,     __SCREAMING_SNAKE_CASE :		bool     ,     __SCREAMING_SNAKE_CASE :		bool     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		str     ,     *__SCREAMING_SNAKE_CASE :		Tuple     ,     ):
							A			=					logging.get_logger("transformers-cli/pt_to_tf")
							A			=					model_name
							A			=					local_dir if local_dir else os.path.join("/tmp"     ,     __SCREAMING_SNAKE_CASE)
							A			=					max_error
							A			=					new_weights
							A			=					no_pr
							A			=					push
							A			=					extra_commit_description
							A			=					override_model_class


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict     ,     __SCREAMING_SNAKE_CASE :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		int):

							def _get_audio_input():
												A			=					load_dataset("hf-internal-testing/librispeech_asr_dummy"     ,     "clean"     ,     split="validation")
												A			=					ds.sort("id").select(range(2))[:2]["audio"]
												A			=					[x["array"] for x in speech_samples]
												return raw_samples

							A			=					type(pt_model.config)
							if model_config_class in PROCESSOR_MAPPING:
												A			=					AutoProcessor.from_pretrained(self._local_dir)
												if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
																	A			=					processor.tokenizer.eos_token
							elif model_config_class in IMAGE_PROCESSOR_MAPPING:
												A			=					AutoImageProcessor.from_pretrained(self._local_dir)
							elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
												A			=					AutoFeatureExtractor.from_pretrained(self._local_dir)
							elif model_config_class in TOKENIZER_MAPPING:
												A			=					AutoTokenizer.from_pretrained(self._local_dir)
												if processor.pad_token is None:
																	A			=					processor.eos_token
							else:
												raise ValueError(F"""Unknown data processing type (model config type: {model_config_class})""")

							A			=					set(inspect.signature(pt_model.forward).parameters.keys())
							A			=					{}
							if "input_ids" in model_forward_signature:
												processor_inputs.update(
												    {
												        "text": ["Hi there!", "I am a batch with more than one row and different input lengths."],
												        "padding": True,
												        "truncation": True,
												    })
							if "pixel_values" in model_forward_signature:
												A			=					load_dataset("cifar10"     ,     "plain_text"     ,     split="test")[:2]["img"]
												processor_inputs.update({"images": sample_images})
							if "input_features" in model_forward_signature:
												A			=					inspect.signature(processor.feature_extractor).parameters
												# Pad to the largest input length by default but take feature extractor default
												# padding value if it exists e.g. "max_length" and is not False or None
												if "padding" in feature_extractor_signature:
																	A			=					feature_extractor_signature["padding"].default
																	if default_strategy is not False and default_strategy is not None:
																						A			=					default_strategy
																	else:
																						A			=					True
												else:
																	A			=					True
												processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy})
							if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
												processor_inputs.update({"audio": _get_audio_input(), "padding": True})
							A			=					processor(**__SCREAMING_SNAKE_CASE     ,     return_tensors="pt")
							A			=					processor(**__SCREAMING_SNAKE_CASE     ,     return_tensors="tf")

							# Extra input requirements, in addition to the input modality
							if (
							    config.is_encoder_decoder
							    or (hasattr(__SCREAMING_SNAKE_CASE     ,     "encoder") and hasattr(__SCREAMING_SNAKE_CASE     ,     "decoder"))
							    or "decoder_input_ids" in tf_dummy_inputs
							):
												A			=					np.asarray([[1], [1]]     ,     dtype=__SCREAMING_SNAKE_CASE) * (pt_model.config.decoder_start_token_id or 0)
												pt_input.update({"decoder_input_ids": torch.tensor(__SCREAMING_SNAKE_CASE)})
												tf_input.update({"decoder_input_ids": tf.convert_to_tensor(__SCREAMING_SNAKE_CASE)})

							return pt_input, tf_input


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							# hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
							if version.parse(huggingface_hub.__version__) < version.parse("0.9.0"):
												raise ImportError(
												    "The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
												    " installation.")
							else:
												from huggingface_hub import Repository, create_commit
												from huggingface_hub._commit_api import CommitOperationAdd

							# Fetch remote data
							A			=					Repository(local_dir=self._local_dir     ,     clone_from=self._model_name)

							# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
							A			=					AutoConfig.from_pretrained(self._local_dir)
							A			=					config.architectures
							if self._override_model_class is not None:
												if self._override_model_class.startswith("TF"):
																	A			=					[self._override_model_class[2:]]
												else:
																	A			=					[self._override_model_class]
												try:
																	A			=					getattr(import_module("transformers")     ,     architectures[0])
												except AttributeError:
																	raise ValueError(F"""Model class {self._override_model_class} not found in transformers.""")
												try:
																	A			=					getattr(import_module("transformers")     ,     "TF" + architectures[0])
												except AttributeError:
																	raise ValueError(F"""TF model class TF{self._override_model_class} not found in transformers.""")
							elif architectures is None:  # No architecture defined -- use auto classes
												A			=					getattr(import_module("transformers")     ,     "AutoModel")
												A			=					getattr(import_module("transformers")     ,     "TFAutoModel")
												self._logger.warning("No detected architecture, using AutoModel/TFAutoModel")
							else:  # Architecture defined -- use it
												if len(__SCREAMING_SNAKE_CASE) > 1:
																	raise ValueError(F"""More than one architecture was found, aborting. (architectures = {architectures})""")
												self._logger.warning(F"""Detected architecture: {architectures[0]}""")
												A			=					getattr(import_module("transformers")     ,     architectures[0])
												try:
																	A			=					getattr(import_module("transformers")     ,     "TF" + architectures[0])
												except AttributeError:
																	raise AttributeError(F"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers.""")

        # Check the TF dummy inputs to see what keys we need in the forward pass
							A			=					tf_class.from_config(__SCREAMING_SNAKE_CASE)
							A			=					tf_from_pt_model.dummy_inputs

							del tf_from_pt_model  # Try to keep only one model in memory at a time

							# Load the model and get some basic inputs
							A			=					pt_class.from_pretrained(self._local_dir)
							pt_model.eval()

							A			,    A			=					self.get_inputs(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							with torch.no_grad():
												A			=					pt_model(**__SCREAMING_SNAKE_CASE     ,     output_hidden_states=__SCREAMING_SNAKE_CASE)
							del pt_model  # will no longer be used, and may have a large memory footprint

							A			=					tf_class.from_pretrained(self._local_dir     ,     from_pt=__SCREAMING_SNAKE_CASE)
							A			=					tf_from_pt_model(**__SCREAMING_SNAKE_CASE     ,     output_hidden_states=__SCREAMING_SNAKE_CASE     ,     training=__SCREAMING_SNAKE_CASE)

							# Confirms that cross loading PT weights into TF worked.
							A			=					self.find_pt_tf_differences(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					{k: v for k, v in crossload_differences.items() if "hidden" not in k}
							A			=					{k: v for k, v in crossload_differences.items() if "hidden" in k}
							if len(__SCREAMING_SNAKE_CASE) == 0 and architectures is not None:
												raise ValueError(
												    F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
												    " output was found. All outputs start with 'hidden'")
							A			=					max(output_differences.values()) if output_differences else 0.0
							A			=					max(hidden_differences.values())
							if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
												raise ValueError(
												    "The cross-loaded TensorFlow model has different outputs, something went wrong!\n"
												    + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
												    + "\n".join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error])
												    + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
												    + "\n".join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]))

							# Save the weights in a TF format (if needed) and confirms that the results are still good
							A			=					os.path.join(self._local_dir     ,     __SCREAMING_SNAKE_CASE)
							A			=					os.path.join(self._local_dir     ,     __SCREAMING_SNAKE_CASE)
							if (not os.path.exists(__SCREAMING_SNAKE_CASE) and not os.path.exists(__SCREAMING_SNAKE_CASE)) or self._new_weights:
												tf_from_pt_model.save_pretrained(self._local_dir)
							del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

							A			=					tf_class.from_pretrained(self._local_dir)
							A			=					tf_model(**__SCREAMING_SNAKE_CASE     ,     output_hidden_states=__SCREAMING_SNAKE_CASE)

							A			=					self.find_pt_tf_differences(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					{k: v for k, v in conversion_differences.items() if "hidden" not in k}
							A			=					{k: v for k, v in conversion_differences.items() if "hidden" in k}
							if len(__SCREAMING_SNAKE_CASE) == 0 and architectures is not None:
												raise ValueError(
												    F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
												    " output was found. All outputs start with 'hidden'")
							A			=					max(output_differences.values()) if output_differences else 0.0
							A			=					max(hidden_differences.values())
							if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
												raise ValueError(
												    "The converted TensorFlow model has different outputs, something went wrong!\n"
												    + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
												    + "\n".join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error])
												    + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
												    + "\n".join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]))

							A			=					"Update TF weights" if self._new_weights else "Add TF weights"
							if self._push:
												repo.git_add(auto_lfs_track=__SCREAMING_SNAKE_CASE)
												repo.git_commit(__SCREAMING_SNAKE_CASE)
												repo.git_push(blocking=__SCREAMING_SNAKE_CASE)  # this prints a progress bar with the upload
												self._logger.warning(F"""TF weights pushed into {self._model_name}""")
							elif not self._no_pr:
												self._logger.warning("Uploading the weights into a new PR...")
												A			=					(
												    "Model converted by the [`transformers`' `pt_to_tf`"
												    " CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). "
												    "All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"
												    F"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
												    F"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
												    F"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
												    F"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
												)
												if self._max_error > MAX_ERROR:
																	commit_descrition += (
																	    F"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
																	)
												if self._extra_commit_description:
																	commit_descrition += "\n\n" + self._extra_commit_description

												# sharded model -> adds all related files (index and .h5 shards)
												if os.path.exists(__SCREAMING_SNAKE_CASE):
																	A			=					[
																	    CommitOperationAdd(path_in_repo=__SCREAMING_SNAKE_CASE     ,     path_or_fileobj=__SCREAMING_SNAKE_CASE)
																	]
																	for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"):
																						operations += [
																						    CommitOperationAdd(path_in_repo=os.path.basename(__SCREAMING_SNAKE_CASE)     ,     path_or_fileobj=__SCREAMING_SNAKE_CASE)
																						]
												else:
																	A			=					[CommitOperationAdd(path_in_repo=__SCREAMING_SNAKE_CASE     ,     path_or_fileobj=__SCREAMING_SNAKE_CASE)]

												A			=					create_commit(
												    repo_id=self._model_name     ,     operations=__SCREAMING_SNAKE_CASE     ,     commit_message=__SCREAMING_SNAKE_CASE     ,     commit_description=__SCREAMING_SNAKE_CASE     ,     repo_type="model"     ,     create_pr=__SCREAMING_SNAKE_CASE     ,     ).pr_url
												self._logger.warning(F"""PR open in {hub_pr_url}""")








"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A     :    str    =		logging.get_logger(__name__)

__A     :    Union[str, Any]    =		{
    'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
    'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
    'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
}





class 			__UpperCamelCase (   _A						):


		def __init__(self :		int     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=2_5_6     ,     __SCREAMING_SNAKE_CASE :		List[str]=1_0_2_4     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=1_6     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=1_6     ,     __SCREAMING_SNAKE_CASE :		List[str]=4     ,     __SCREAMING_SNAKE_CASE :		List[Any]="gelu"     ,     __SCREAMING_SNAKE_CASE :		str=1E-6     ,     **__SCREAMING_SNAKE_CASE :		Optional[int]     ,     ):
							super().__init__(**__SCREAMING_SNAKE_CASE)
							A			=					hidden_size
							A			=					image_size
							A			=					patch_size
							A			=					image_size // patch_size
							A			=					mask_input_channels
							A			=					num_point_embeddings
							A			=					hidden_act
							A			=					layer_norm_eps





class 			__UpperCamelCase (   _A						):


		def __init__(self :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple=2_5_6     ,     __SCREAMING_SNAKE_CASE :		str="relu"     ,     __SCREAMING_SNAKE_CASE :		List[Any]=2_0_4_8     ,     __SCREAMING_SNAKE_CASE :		Tuple=2     ,     __SCREAMING_SNAKE_CASE :		Any=8     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=2     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=3     ,     __SCREAMING_SNAKE_CASE :		List[Any]=3     ,     __SCREAMING_SNAKE_CASE :		Tuple=2_5_6     ,     __SCREAMING_SNAKE_CASE :		int=1E-6     ,     **__SCREAMING_SNAKE_CASE :		int     ,     ):
							super().__init__(**__SCREAMING_SNAKE_CASE)
							A			=					hidden_size
							A			=					hidden_act
							A			=					mlp_dim
							A			=					num_hidden_layers
							A			=					num_attention_heads
							A			=					attention_downsample_rate
							A			=					num_multimask_outputs
							A			=					iou_head_depth
							A			=					iou_head_hidden_dim
							A			=					layer_norm_eps





class 			__UpperCamelCase (   _A						):


		def __init__(self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		List[Any]=7_6_8     ,     __SCREAMING_SNAKE_CASE :		int=2_5_6     ,     __SCREAMING_SNAKE_CASE :		Tuple=1_2     ,     __SCREAMING_SNAKE_CASE :		str=1_2     ,     __SCREAMING_SNAKE_CASE :		Any=3     ,     __SCREAMING_SNAKE_CASE :		Dict=1_0_2_4     ,     __SCREAMING_SNAKE_CASE :		str=1_6     ,     __SCREAMING_SNAKE_CASE :		Tuple="gelu"     ,     __SCREAMING_SNAKE_CASE :		int=1E-06     ,     __SCREAMING_SNAKE_CASE :		Dict=0.0     ,     __SCREAMING_SNAKE_CASE :		str=1E-10     ,     __SCREAMING_SNAKE_CASE :		List[str]=True     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=4.0     ,     __SCREAMING_SNAKE_CASE :		str=True     ,     __SCREAMING_SNAKE_CASE :		Any=True     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=1_4     ,     __SCREAMING_SNAKE_CASE :		Tuple=[2, 5, 8, 1_1]     ,     __SCREAMING_SNAKE_CASE :		str=1_2_8     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=None     ,     **__SCREAMING_SNAKE_CASE :		Optional[Any]     ,     ):
							super().__init__(**__SCREAMING_SNAKE_CASE)

							A			=					hidden_size
							A			=					output_channels
							A			=					num_hidden_layers
							A			=					num_attention_heads
							A			=					num_channels
							A			=					image_size
							A			=					patch_size
							A			=					hidden_act
							A			=					layer_norm_eps
							A			=					attention_dropout
							A			=					initializer_range
							A			=					qkv_bias
							A			=					mlp_ratio
							A			=					use_abs_pos
							A			=					use_rel_pos
							A			=					window_size
							A			=					global_attn_indexes
							A			=					num_pos_feats
							A			=					int(hidden_size * mlp_ratio) if mlp_dim is None else mlp_dim





class 			__UpperCamelCase (   _A						):
		SCREAMING_SNAKE_CASE								=    "sam"
		SCREAMING_SNAKE_CASE								=    True


		def __init__(self :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		Dict=None     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=None     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=None     ,     __SCREAMING_SNAKE_CASE :		List[Any]=0.0_2     ,     **__SCREAMING_SNAKE_CASE :		Optional[Any]     ,     ):
							super().__init__(**__SCREAMING_SNAKE_CASE)
							A			=					vision_config if vision_config is not None else {}
							A			=					prompt_encoder_config if prompt_encoder_config is not None else {}
							A			=					mask_decoder_config if mask_decoder_config is not None else {}

							if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
												A			=					vision_config.to_dict()
							if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
												A			=					prompt_encoder_config.to_dict()
							if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
												A			=					mask_decoder_config.to_dict()

							A			=					SamVisionConfig(**__SCREAMING_SNAKE_CASE)
							A			=					SamPromptEncoderConfig(**__SCREAMING_SNAKE_CASE)
							A			=					SamMaskDecoderConfig(**__SCREAMING_SNAKE_CASE)
							A			=					initializer_range


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					copy.deepcopy(self.__dict__)
							A			=					self.vision_config.to_dict()
							A			=					self.prompt_encoder_config.to_dict()
							A			=					self.mask_decoder_config.to_dict()
							A			=					self.__class__.model_type
							return output





