






'''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       =      5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors





def 	_A						(							lowercase__		):
						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      A							(    UpperCamelCase__			):



			@staticmethod
			def A__	(   lowerCamelCase__    )     ->						str:




									'''simple docstring'''



									lowercase__								=    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=__lowerCamelCase    ,       required=__lowerCamelCase    ,       help="""The model name, including owner/organization, as seen on the hub."""    ,       )
									train_parser.add_argument(
									    """--local-dir"""    ,       type=__lowerCamelCase    ,       default=""""""    ,       help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""    ,       )
									train_parser.add_argument(
									    """--max-error"""    ,       type=__lowerCamelCase    ,       default=__lowerCamelCase    ,       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=__lowerCamelCase    ,       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=__lowerCamelCase    ,       default=__lowerCamelCase    ,       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=__lowerCamelCase    )



			@staticmethod
			def A__	(   lowerCamelCase__    ,       lowerCamelCase__    )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    set(pt_outputs.keys()    )
									lowercase__								=    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(lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__=""    ):
															# 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(__lowerCamelCase    ,       torch.Tensor    ):
																					lowercase__								=    np.max(np.abs(pt_out.numpy() - tf_out.numpy()    )    )
																					lowercase__								=    tensor_difference
															else:
																					lowercase__								=    attr_name
																					for i, pt_item in enumerate(__lowerCamelCase    ):
																											# If it is a named attribute, we keep the name. Otherwise, just its index.
																											if isinstance(__lowerCamelCase    ,       __lowerCamelCase    ):
																																	lowercase__								=    root_name + pt_item
																																	lowercase__								=    tf_out[pt_item]
																																	lowercase__								=    pt_out[pt_item]
																											else:
																																	lowercase__								=    root_name + F'''[{i}]'''
																																	lowercase__								=    tf_out[i]
																											lowercase__								=    _find_pt_tf_differences(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    )

															return differences

									return _find_pt_tf_differences(__lowerCamelCase    ,       __lowerCamelCase    ,       {}    )



			def __init__(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       *lowerCamelCase__    ,       )     ->						Dict:




									'''simple docstring'''



									lowercase__								=    logging.get_logger("""transformers-cli/pt_to_tf"""    )
									lowercase__								=    model_name
									lowercase__								=    local_dir if local_dir else os.path.join("""/tmp"""    ,       __lowerCamelCase    )
									lowercase__								=    max_error
									lowercase__								=    new_weights
									lowercase__								=    no_pr
									lowercase__								=    push
									lowercase__								=    extra_commit_description
									lowercase__								=    override_model_class



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    )     ->						List[Any]:




									'''simple docstring'''



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

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

									lowercase__								=    set(inspect.signature(pt_model.forward    ).parameters.keys()    )
									lowercase__								=    {}
									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:
															lowercase__								=    load_dataset("""cifar10"""    ,       """plain_text"""    ,       split="""test"""    )[:2]["img"]
															processor_inputs.update({"""images""": sample_images}    )
									if "input_features" in model_forward_signature:
															lowercase__								=    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:
																					lowercase__								=    feature_extractor_signature["padding"].default
																					if default_strategy is not False and default_strategy is not None:
																											lowercase__								=    default_strategy
																					else:
																											lowercase__								=    True
															else:
																					lowercase__								=    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}    )
									lowercase__								=    processor(**__lowerCamelCase    ,       return_tensors="""pt"""    )
									lowercase__								=    processor(**__lowerCamelCase    ,       return_tensors="""tf"""    )

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

									return pt_input, tf_input



			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



									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
									lowercase__								=    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
									lowercase__								=    AutoConfig.from_pretrained(self._local_dir    )
									lowercase__								=    config.architectures
									if self._override_model_class is not None:
															if self._override_model_class.startswith("""TF"""    ):
																					lowercase__								=    [self._override_model_class[2:]]
															else:
																					lowercase__								=    [self._override_model_class]
															try:
																					lowercase__								=    getattr(import_module("""transformers"""    )    ,       architectures[0]    )
															except AttributeError:
																					raise ValueError(F'''Model class {self._override_model_class} not found in transformers.'''    )
															try:
																					lowercase__								=    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
															lowercase__								=    getattr(import_module("""transformers"""    )    ,       """AutoModel"""    )
															lowercase__								=    getattr(import_module("""transformers"""    )    ,       """TFAutoModel"""    )
															self._logger.warning("""No detected architecture, using AutoModel/TFAutoModel"""    )
									else:  # Architecture defined -- use it
															if len(__lowerCamelCase    ) > 1:
																					raise ValueError(F'''More than one architecture was found, aborting. (architectures = {architectures})'''    )
															self._logger.warning(F'''Detected architecture: {architectures[0]}'''    )
															lowercase__								=    getattr(import_module("""transformers"""    )    ,       architectures[0]    )
															try:
																					lowercase__								=    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
									lowercase__								=    tf_class.from_config(__lowerCamelCase    )
									lowercase__								=    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
									lowercase__								=    pt_class.from_pretrained(self._local_dir    )
									pt_model.eval()

									lowercase__								=    self.get_inputs(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    )

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

									lowercase__								=    tf_class.from_pretrained(self._local_dir    ,       from_pt=__lowerCamelCase    )
									lowercase__								=    tf_from_pt_model(**__lowerCamelCase    ,       output_hidden_states=__lowerCamelCase    ,       training=__lowerCamelCase    )

									# Confirms that cross loading PT weights into TF worked.
									lowercase__								=    self.find_pt_tf_differences(__lowerCamelCase    ,       __lowerCamelCase    )
									lowercase__								=    {k: v for k, v in crossload_differences.items() if "hidden" not in k}
									lowercase__								=    {k: v for k, v in crossload_differences.items() if "hidden" in k}
									if len(__lowerCamelCase    ) == 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'"""    )
									lowercase__								=    max(output_differences.values()    ) if output_differences else 0.0
									lowercase__								=    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
									lowercase__								=    os.path.join(self._local_dir    ,       __lowerCamelCase    )
									lowercase__								=    os.path.join(self._local_dir    ,       __lowerCamelCase    )
									if (not os.path.exists(__lowerCamelCase    ) and not os.path.exists(__lowerCamelCase    )) 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

									lowercase__								=    tf_class.from_pretrained(self._local_dir    )
									lowercase__								=    tf_model(**__lowerCamelCase    ,       output_hidden_states=__lowerCamelCase    )

									lowercase__								=    self.find_pt_tf_differences(__lowerCamelCase    ,       __lowerCamelCase    )
									lowercase__								=    {k: v for k, v in conversion_differences.items() if "hidden" not in k}
									lowercase__								=    {k: v for k, v in conversion_differences.items() if "hidden" in k}
									if len(__lowerCamelCase    ) == 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'"""    )
									lowercase__								=    max(output_differences.values()    ) if output_differences else 0.0
									lowercase__								=    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]    )    )

									lowercase__								=    "Update TF weights" if self._new_weights else "Add TF weights"
									if self._push:
															repo.git_add(auto_lfs_track=__lowerCamelCase    )
															repo.git_commit(__lowerCamelCase    )
															repo.git_push(blocking=__lowerCamelCase    )  # 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..."""    )
															lowercase__								=    (
															    "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(__lowerCamelCase    ):
																					lowercase__								=    [
																					    CommitOperationAdd(path_in_repo=__lowerCamelCase    ,       path_or_fileobj=__lowerCamelCase    )
																					]
																					for shard_path in tf.io.gfile.glob(self._local_dir + """/tf_model-*.h5"""    ):
																											operations += [
																											    CommitOperationAdd(path_in_repo=os.path.basename(__lowerCamelCase    )    ,       path_or_fileobj=__lowerCamelCase    )
																											]
															else:
																					lowercase__								=    [CommitOperationAdd(path_in_repo=__lowerCamelCase    ,       path_or_fileobj=__lowerCamelCase    )]

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








'''simple docstring'''



def 	_A						(							lowercase__		):
						lowercase__								=    1.3
						lowercase__								=    len(lowercase__		)
						lowercase__								=    False

						while not completed:
												# Update the gap value for a next comb
												lowercase__								=    int(gap / shrink_factor		)
												if gap <= 1:
																		lowercase__								=    True

												lowercase__								=    0
												while index + gap < len(lowercase__		):
																		if data[index] > data[index + gap]:
																								# Swap values
																								lowercase__	,  lowercase__								=    data[index + gap], data[index]
																								lowercase__								=    False
																		index += 1

						return data


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__A       =      input("Enter numbers separated by a comma:\n").strip()
				__A       =      [int(item) for item in user_input.split(",")]
				print(comb_sort(unsorted))






