

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





def 				__UpperCamelCase   (    a__: Namespace			)				->			Any:




					"""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 			_snake_case     (						UpperCamelCase       ):







						@staticmethod
						def        _UpperCamelCase    (UpperCAmelCase_							)			->						Optional[int]:
											__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=UpperCAmelCase_     ,							required=UpperCAmelCase_     ,							help='''The model name, including owner/organization, as seen on the hub.'''     ,							)
											train_parser.add_argument(
											    '''--local-dir'''     ,							type=UpperCAmelCase_     ,							default=''''''     ,							help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''     ,							)
											train_parser.add_argument(
											    '''--max-error'''     ,							type=UpperCAmelCase_     ,							default=UpperCAmelCase_     ,							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=UpperCAmelCase_     ,							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=UpperCAmelCase_     ,							default=UpperCAmelCase_     ,							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=UpperCAmelCase_							)







						@staticmethod
						def        _UpperCamelCase    (UpperCAmelCase_     ,							UpperCAmelCase_							)			->						Dict:
											__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(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_=""							):
																# 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(UpperCAmelCase_     ,							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(UpperCAmelCase_							):
																										# If it is a named attribute, we keep the name. Otherwise, just its index.
																										if isinstance(UpperCAmelCase_     ,							UpperCAmelCase_							):
																															__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(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)

																return differences

											return _find_pt_tf_differences(UpperCAmelCase_     ,							UpperCAmelCase_     ,							{}							)







						def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							*UpperCAmelCase_     ,							)			->						Any:
											__lowercase												=			logging.get_logger('''transformers-cli/pt_to_tf'''							)
											__lowercase												=			model_name
											__lowercase												=			local_dir if local_dir else os.path.join('''/tmp'''     ,							UpperCAmelCase_							)
											__lowercase												=			max_error
											__lowercase												=			new_weights
											__lowercase												=			no_pr
											__lowercase												=			push
											__lowercase												=			extra_commit_description
											__lowercase												=			override_model_class







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						Dict:

											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(**UpperCAmelCase_     ,							return_tensors='''pt'''							)
											__lowercase												=			processor(**UpperCAmelCase_     ,							return_tensors='''tf'''							)

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

											return pt_input, tf_input







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											# 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
											__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(UpperCAmelCase_							) > 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(UpperCAmelCase_							)
											__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						, __lowercase												=			self.get_inputs(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)

											with torch.no_grad():
																__lowercase												=			pt_model(**UpperCAmelCase_     ,							output_hidden_states=UpperCAmelCase_							)
											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=UpperCAmelCase_							)
											__lowercase												=			tf_from_pt_model(**UpperCAmelCase_     ,							output_hidden_states=UpperCAmelCase_     ,							training=UpperCAmelCase_							)

											# Confirms that cross loading PT weights into TF worked.
											__lowercase												=			self.find_pt_tf_differences(UpperCAmelCase_     ,							UpperCAmelCase_							)
											__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(UpperCAmelCase_							) == 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     ,							UpperCAmelCase_							)
											__lowercase												=			os.path.join(self._local_dir     ,							UpperCAmelCase_							)
											if (not os.path.exists(UpperCAmelCase_							) and not os.path.exists(UpperCAmelCase_							)) 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(**UpperCAmelCase_     ,							output_hidden_states=UpperCAmelCase_							)

											__lowercase												=			self.find_pt_tf_differences(UpperCAmelCase_     ,							UpperCAmelCase_							)
											__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(UpperCAmelCase_							) == 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=UpperCAmelCase_							)
																repo.git_commit(UpperCAmelCase_							)
																repo.git_push(blocking=UpperCAmelCase_							)  # 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(UpperCAmelCase_							):
																					__lowercase												=			[
																					    CommitOperationAdd(path_in_repo=UpperCAmelCase_     ,							path_or_fileobj=UpperCAmelCase_							)
																					]
																					for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''							):
																										operations += [
																										    CommitOperationAdd(path_in_repo=os.path.basename(UpperCAmelCase_							)     ,							path_or_fileobj=UpperCAmelCase_							)
																										]
																else:
																					__lowercase												=			[CommitOperationAdd(path_in_repo=UpperCAmelCase_     ,							path_or_fileobj=UpperCAmelCase_							)]

																__lowercase												=			create_commit(
																    repo_id=self._model_name     ,							operations=UpperCAmelCase_     ,							commit_message=UpperCAmelCase_     ,							commit_description=UpperCAmelCase_     ,							repo_type='''model'''     ,							create_pr=UpperCAmelCase_     ,							).pr_url
																self._logger.warning(f'''PR open in {hub_pr_url}'''							)





"""simple docstring"""

import json
import logging
import os
import subprocess
from argparse import ArgumentParser


a__ :	List[str]						= logging.getLogger(__name__)





def 				__UpperCamelCase   (    )				->			Optional[Any]:




					"""simple docstring"""



					__lowercase												=			ArgumentParser()
					__lowercase						, __lowercase												=			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(('''-''', '''--''')			):
															parser.add_argument(arg.split('''='''			)[0]			)

					return parser.parse_args()





def 				__UpperCamelCase   (    )				->			int:




					"""simple docstring"""



					__lowercase												=			parse_args()
					__lowercase												=			88_88
					__lowercase												=			int(os.environ['''SM_NUM_GPUS''']			)
					__lowercase												=			json.loads(os.environ['''SM_HOSTS''']			)
					__lowercase												=			len(a__			)
					__lowercase												=			os.environ['''SM_CURRENT_HOST''']
					__lowercase												=			hosts.index(a__			)
					__lowercase												=			'''INFO'''

					if num_nodes > 1:
										__lowercase												=			f'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					else:
										__lowercase												=			f'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					try:
										subprocess.run(a__ ,	shell=a__			)
					except Exception as e:
										logger.info(a__			)


if __name__ == "__main__":
							main()


