






"""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


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

def 	__lowerCamelCase			(						__UpperCamelCase )		->					Dict:

		"""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 	__lowerCamelCase       (				A_    ):





		'''simple docstring'''






		@staticmethod
		def      lowerCamelCase (     a_							:			ArgumentParser			):
				lowerCAmelCase_     :							Any             =    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=a_		,    required=a_		,    help="The model name, including owner/organization, as seen on the hub."		,    )
				train_parser.add_argument(
				    "--local-dir"		,    type=a_		,    default=""		,    help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"		,    )
				train_parser.add_argument(
				    "--max-error"		,    type=a_		,    default=a_		,    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=a_		,    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=a_		,    default=a_		,    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=a_			)





		@staticmethod
		def      lowerCamelCase (     a_							:			int		,    a_							:			str			):
				lowerCAmelCase_     :							Dict             =    set(pt_outputs.keys()			)
				lowerCAmelCase_     :							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(a_							:			Optional[int]		,    a_							:			str		,    a_							:			Optional[Any]		,    a_							:			str=""			):
						# 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(a_		,    torch.Tensor			):
								lowerCAmelCase_     :							str             =    np.max(np.abs(pt_out.numpy() - tf_out.numpy()			)			)
								lowerCAmelCase_     :							Dict             =    tensor_difference
						else:
								lowerCAmelCase_     :							Tuple             =    attr_name
								for i, pt_item in enumerate(a_			):
										# If it is a named attribute, we keep the name. Otherwise, just its index.
										if isinstance(a_		,    a_			):
												lowerCAmelCase_     :							List[str]             =    root_name + pt_item
												lowerCAmelCase_     :							Dict             =    tf_out[pt_item]
												lowerCAmelCase_     :							Tuple             =    pt_out[pt_item]
										else:
												lowerCAmelCase_     :							Dict             =    root_name + f'''[{i}]'''
												lowerCAmelCase_     :							List[Any]             =    tf_out[i]
										lowerCAmelCase_     :							Any             =    _find_pt_tf_differences(a_		,    a_		,    a_		,    a_			)

						return differences

				return _find_pt_tf_differences(a_		,    a_		,    {}			)





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





		def      lowerCamelCase (     self							:			Tuple		,    a_							:			Dict		,    a_							:			List[Any]		,    a_							:			Union[str, Any]			):



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

				lowerCAmelCase_     :							List[str]             =    type(pt_model.config			)
				if model_config_class in PROCESSOR_MAPPING:
						lowerCAmelCase_     :							str             =    AutoProcessor.from_pretrained(self._local_dir			)
						if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
								lowerCAmelCase_     :							str             =    processor.tokenizer.eos_token
				elif model_config_class in IMAGE_PROCESSOR_MAPPING:
						lowerCAmelCase_     :							List[Any]             =    AutoImageProcessor.from_pretrained(self._local_dir			)
				elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
						lowerCAmelCase_     :							Union[str, Any]             =    AutoFeatureExtractor.from_pretrained(self._local_dir			)
				elif model_config_class in TOKENIZER_MAPPING:
						lowerCAmelCase_     :							Any             =    AutoTokenizer.from_pretrained(self._local_dir			)
						if processor.pad_token is None:
								lowerCAmelCase_     :							Tuple             =    processor.eos_token
				else:
						raise ValueError(f'''Unknown data processing type (model config type: {model_config_class})'''			)

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

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

				return pt_input, tf_input





		def      lowerCamelCase (     self							:			Optional[Any]			):



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

				lowerCAmelCase_     :							Union[str, Any]             =    self.get_inputs(a_		,    a_		,    a_			)

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

				lowerCAmelCase_     :							Dict             =    tf_class.from_pretrained(self._local_dir		,    from_pt=a_			)
				lowerCAmelCase_     :							List[Any]             =    tf_from_pt_model(**a_		,    output_hidden_states=a_		,    training=a_			)

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

				lowerCAmelCase_     :							int             =    tf_class.from_pretrained(self._local_dir			)
				lowerCAmelCase_     :							Optional[int]             =    tf_model(**a_		,    output_hidden_states=a_			)

				lowerCAmelCase_     :							int             =    self.find_pt_tf_differences(a_		,    a_			)
				lowerCAmelCase_     :							Optional[Any]             =    {k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
				lowerCAmelCase_     :							int             =    {k: v for k, v in conversion_differences.items() if '''hidden''' in k}
				if len(a_			) == 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\'"			)
				lowerCAmelCase_     :							Optional[int]             =    max(output_differences.values()			) if output_differences else 0.0
				lowerCAmelCase_     :							int             =    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]			)			)

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

						lowerCAmelCase_     :							List[str]             =    create_commit(
						    repo_id=self._model_name		,    operations=a_		,    commit_message=a_		,    commit_description=a_		,    repo_type="model"		,    create_pr=a_		,    ).pr_url
						self._logger.warning(f'''PR open in {hub_pr_url}'''			)








"""simple docstring"""

from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
lowercase__      =				{
    """1""": ["""2""", """3""", """4""", """5"""],
    """2""": ["""1""", """3""", """4""", """5"""],
    """3""": ["""1""", """2""", """4""", """5""", """10"""],
    """4""": ["""1""", """2""", """3""", """5""", """6"""],
    """5""": ["""1""", """2""", """3""", """4""", """7"""],
    """6""": ["""7""", """8""", """9""", """10""", """4"""],
    """7""": ["""6""", """8""", """9""", """10""", """5"""],
    """8""": ["""6""", """7""", """9""", """10"""],
    """9""": ["""6""", """7""", """8""", """10"""],
    """10""": ["""6""", """7""", """8""", """9""", """3"""],
}

def 	__lowerCamelCase			(						__UpperCamelCase )		->					set[tuple[str, str]]:

		"""simple docstring"""







		lowerCAmelCase_     :							Union[str, Any]             =    {node: {node} for node in graph}

		lowerCAmelCase_     :							List[str]             =    {node: graph[node][:] for node in graph}

		while len(__UpperCamelCase ) > 2:
				# Choose a random edge.
				lowerCAmelCase_     :							int             =    random.choice(list(graph_copy.keys() ) )
				lowerCAmelCase_     :							Any             =    random.choice(graph_copy[u] )

				# Contract edge (u, v) to new node uv
				lowerCAmelCase_     :							Union[str, Any]             =    u + v
				lowerCAmelCase_     :							List[Any]             =    list(set(graph_copy[u] + graph_copy[v] ) )
				uv_neighbors.remove(__UpperCamelCase )
				uv_neighbors.remove(__UpperCamelCase )
				lowerCAmelCase_     :							str             =    uv_neighbors
				for neighbor in uv_neighbors:
						graph_copy[neighbor].append(__UpperCamelCase )

				lowerCAmelCase_     :							Optional[int]             =    set(contracted_nodes[u].union(contracted_nodes[v] ) )

				# Remove nodes u and v.
				del graph_copy[u]
				del graph_copy[v]
				for neighbor in uv_neighbors:
						if u in graph_copy[neighbor]:
								graph_copy[neighbor].remove(__UpperCamelCase )
						if v in graph_copy[neighbor]:
								graph_copy[neighbor].remove(__UpperCamelCase )

    # Find cutset.
		lowerCAmelCase_     :							Union[str, Any]             =    [contracted_nodes[node] for node in graph_copy]
		return {
		    (node, neighbor)
		    for node in groups[0]
		    for neighbor in graph[node]
		    if neighbor in groups[1]
		}


if __name__ == "__main__":
					print(partition_graph(TEST_GRAPH))
