'''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 					UpperCAmelCase__      (							_lowerCAmelCase						: List[Any])						->		Optional[int]:



						"""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 		SCREAMING_SNAKE_CASE_				(lowerCAmelCase__			):



			"""simple docstring"""






			@staticmethod
			def      lowerCamelCase   (       snake_case_				):
									snake_case   :					Dict							=						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      lowerCamelCase   (       snake_case_							, snake_case_				):
									snake_case   :					Tuple							=						set(pt_outputs.keys()				)
									snake_case   :					List[str]							=						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(snake_case_							, snake_case_							, snake_case_							, snake_case_=""				):
															# 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				):
																					snake_case   :					Optional[Any]							=						np.max(np.abs(pt_out.numpy() - tf_out.numpy()				)				)
																					snake_case   :					Optional[Any]							=						tensor_difference
															else:
																					snake_case   :					Optional[Any]							=						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				):
																																	snake_case   :					Union[str, Any]							=						root_name + pt_item
																																	snake_case   :					Optional[int]							=						tf_out[pt_item]
																																	snake_case   :					List[Any]							=						pt_out[pt_item]
																											else:
																																	snake_case   :					List[Any]							=						root_name + F"""[{i}]"""
																																	snake_case   :					List[str]							=						tf_out[i]
																											snake_case   :					Optional[int]							=						_find_pt_tf_differences(_lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase				)

															return differences

									return _find_pt_tf_differences(_lowerCamelCase							, _lowerCamelCase							, {}				)



			def __init__(       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, *snake_case_							, ):
									snake_case   :					Union[str, Any]							=						logging.get_logger("transformers-cli/pt_to_tf"				)
									snake_case   :					str							=						model_name
									snake_case   :					Tuple							=						local_dir if local_dir else os.path.join("/tmp"							, _lowerCamelCase				)
									snake_case   :					Tuple							=						max_error
									snake_case   :					Optional[int]							=						new_weights
									snake_case   :					List[str]							=						no_pr
									snake_case   :					int							=						push
									snake_case   :					List[str]							=						extra_commit_description
									snake_case   :					str							=						override_model_class



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_				):

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

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

									snake_case   :					List[Any]							=						set(inspect.signature(pt_model.forward				).parameters.keys()				)
									snake_case   :					Tuple							=						{}
									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:
															snake_case   :					int							=						load_dataset("cifar10"							, "plain_text"							, split="test"				)[:2]["""img"""]
															processor_inputs.update({"images": sample_images}				)
									if "input_features" in model_forward_signature:
															snake_case   :					Union[str, Any]							=						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:
																					snake_case   :					List[str]							=						feature_extractor_signature["""padding"""].default
																					if default_strategy is not False and default_strategy is not None:
																											snake_case   :					Optional[int]							=						default_strategy
																					else:
																											snake_case   :					Tuple							=						True
															else:
																					snake_case   :					List[Any]							=						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}				)
									snake_case   :					Tuple							=						processor(**_lowerCamelCase							, return_tensors="pt"				)
									snake_case   :					Optional[Any]							=						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
									):
															snake_case   :					Dict							=						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      lowerCamelCase   (       self				):

									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
									snake_case   :					str							=						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
									snake_case   :					List[str]							=						AutoConfig.from_pretrained(self._local_dir				)
									snake_case   :					Optional[int]							=						config.architectures
									if self._override_model_class is not None:
															if self._override_model_class.startswith("TF"				):
																					snake_case   :					Dict							=						[self._override_model_class[2:]]
															else:
																					snake_case   :					List[str]							=						[self._override_model_class]
															try:
																					snake_case   :					Dict							=						getattr(import_module("transformers"				)							, architectures[0]				)
															except AttributeError:
																					raise ValueError(F"""Model class {self._override_model_class} not found in transformers."""				)
															try:
																					snake_case   :					Dict							=						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
															snake_case   :					Union[str, Any]							=						getattr(import_module("transformers"				)							, "AutoModel"				)
															snake_case   :					List[str]							=						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]}"""				)
															snake_case   :					Optional[int]							=						getattr(import_module("transformers"				)							, architectures[0]				)
															try:
																					snake_case   :					List[str]							=						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
									snake_case   :					str							=						tf_class.from_config(_lowerCamelCase				)
									snake_case   :					Optional[Any]							=						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
									snake_case   :					List[Any]							=						pt_class.from_pretrained(self._local_dir				)
									pt_model.eval()

									snake_case   :					Dict							=						self.get_inputs(_lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase				)

									with torch.no_grad():
															snake_case   :					List[str]							=						pt_model(**_lowerCamelCase							, output_hidden_states=_lowerCamelCase				)
									del pt_model  # will no longer be used, and may have a large memory footprint

									snake_case   :					Dict							=						tf_class.from_pretrained(self._local_dir							, from_pt=_lowerCamelCase				)
									snake_case   :					List[str]							=						tf_from_pt_model(**_lowerCamelCase							, output_hidden_states=_lowerCamelCase							, training=_lowerCamelCase				)

									# Confirms that cross loading PT weights into TF worked.
									snake_case   :					Optional[Any]							=						self.find_pt_tf_differences(_lowerCamelCase							, _lowerCamelCase				)
									snake_case   :					int							=						{k: v for k, v in crossload_differences.items() if """hidden""" not in k}
									snake_case   :					Union[str, Any]							=						{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'"				)
									snake_case   :					Union[str, Any]							=						max(output_differences.values()				) if output_differences else 0.0
									snake_case   :					str							=						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
									snake_case   :					Dict							=						os.path.join(self._local_dir							, _lowerCamelCase				)
									snake_case   :					str							=						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

									snake_case   :					Dict							=						tf_class.from_pretrained(self._local_dir				)
									snake_case   :					Tuple							=						tf_model(**_lowerCamelCase							, output_hidden_states=_lowerCamelCase				)

									snake_case   :					Tuple							=						self.find_pt_tf_differences(_lowerCamelCase							, _lowerCamelCase				)
									snake_case   :					Any							=						{k: v for k, v in conversion_differences.items() if """hidden""" not in k}
									snake_case   :					List[str]							=						{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'"				)
									snake_case   :					List[str]							=						max(output_differences.values()				) if output_differences else 0.0
									snake_case   :					Tuple							=						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]				)				)

									snake_case   :					List[Any]							=						"""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..."				)
															snake_case   :					List[str]							=						(
															    """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				):
																					snake_case   :					List[str]							=						[
																					    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:
																					snake_case   :					List[str]							=						[CommitOperationAdd(path_in_repo=_lowerCamelCase							, path_or_fileobj=_lowerCamelCase				)]

															snake_case   :					List[str]							=						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'''

import math


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		bool:



						"""simple docstring"""

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5,			int(math.sqrt(_lowerCAmelCase) + 1),			6):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int = 200_0000)						->		int:



						"""simple docstring"""

						return sum(num for num in range(3,			_lowerCAmelCase,			2) if is_prime(_lowerCAmelCase)) + 2 if n > 2 else 0


if __name__ == "__main__":
						print(f"{solution() = }")





