






'''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


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


def   lowercase_	(   lowerCAmelCase__	:  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 _A  (    lowercase_    ):





		@staticmethod
		def 							__A       (     __UpperCAmelCase				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[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=__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 							__A       (     __UpperCAmelCase ,       __UpperCAmelCase				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     set(pt_outputs.keys()				)
					__UpperCAmelCase   :      Optional[int]										=     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				):
											__UpperCAmelCase   :      Dict										=     np.max(np.abs(pt_out.numpy() - tf_out.numpy()				)				)
											__UpperCAmelCase   :      Any										=     tensor_difference
								else:
											__UpperCAmelCase   :      List[str]										=     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				):
																	__UpperCAmelCase   :      Union[str, Any]										=     root_name + pt_item
																	__UpperCAmelCase   :      Dict										=     tf_out[pt_item]
																	__UpperCAmelCase   :      Optional[Any]										=     pt_out[pt_item]
														else:
																	__UpperCAmelCase   :      Union[str, Any]										=     root_name + f'[{i}]'
																	__UpperCAmelCase   :      List[Any]										=     tf_out[i]
														__UpperCAmelCase   :      Optional[Any]										=     _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 ,       )       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     logging.get_logger("""transformers-cli/pt_to_tf"""				)
					__UpperCAmelCase   :      List[Any]										=     model_name
					__UpperCAmelCase   :      List[str]										=     local_dir if local_dir else os.path.join("""/tmp""" ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      str										=     max_error
					__UpperCAmelCase   :      Tuple										=     new_weights
					__UpperCAmelCase   :      List[Any]										=     no_pr
					__UpperCAmelCase   :      Optional[Any]										=     push
					__UpperCAmelCase   :      Dict										=     extra_commit_description
					__UpperCAmelCase   :      Dict										=     override_model_class





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Tuple:

					'''simple docstring'''







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

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

					__UpperCAmelCase   :      str										=     set(inspect.signature(pt_model.forward				).parameters.keys()				)
					__UpperCAmelCase   :      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:
								__UpperCAmelCase   :      int										=     load_dataset("""cifar10""" ,       """plain_text""" ,       split="""test"""				)[:2]["""img"""]
								processor_inputs.update({"""images""": sample_images}				)
					if "input_features" in model_forward_signature:
								__UpperCAmelCase   :      List[str]										=     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:
											__UpperCAmelCase   :      List[str]										=     feature_extractor_signature["""padding"""].default
											if default_strategy is not False and default_strategy is not None:
														__UpperCAmelCase   :      int										=     default_strategy
											else:
														__UpperCAmelCase   :      Optional[int]										=     True
								else:
											__UpperCAmelCase   :      Dict										=     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}				)
					__UpperCAmelCase   :      int										=     processor(**__UpperCAmelCase ,       return_tensors="""pt"""				)
					__UpperCAmelCase   :      str										=     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
					):
								__UpperCAmelCase   :      Dict										=     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 							__A       (     self				)       ->     List[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
					__UpperCAmelCase   :      List[Any]										=     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
					__UpperCAmelCase   :      str										=     AutoConfig.from_pretrained(self._local_dir				)
					__UpperCAmelCase   :      Optional[Any]										=     config.architectures
					if self._override_model_class is not None:
								if self._override_model_class.startswith("""TF"""				):
											__UpperCAmelCase   :      List[str]										=     [self._override_model_class[2:]]
								else:
											__UpperCAmelCase   :      Optional[int]										=     [self._override_model_class]
								try:
											__UpperCAmelCase   :      Optional[int]										=     getattr(import_module("""transformers"""				) ,       architectures[0]				)
								except AttributeError:
											raise ValueError(f'Model class {self._override_model_class} not found in transformers.'				)
								try:
											__UpperCAmelCase   :      List[Any]										=     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
								__UpperCAmelCase   :      List[Any]										=     getattr(import_module("""transformers"""				) ,       """AutoModel"""				)
								__UpperCAmelCase   :      Optional[int]										=     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]}'				)
								__UpperCAmelCase   :      Union[str, Any]										=     getattr(import_module("""transformers"""				) ,       architectures[0]				)
								try:
											__UpperCAmelCase   :      Optional[int]										=     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
					__UpperCAmelCase   :      Optional[Any]										=     tf_class.from_config(__UpperCAmelCase				)
					__UpperCAmelCase   :      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
					__UpperCAmelCase   :      int										=     pt_class.from_pretrained(self._local_dir				)
					pt_model.eval()

					__UpperCAmelCase				,  __UpperCAmelCase   :      List[Any]										=     self.get_inputs(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)

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

					__UpperCAmelCase   :      List[Any]										=     tf_class.from_pretrained(self._local_dir ,       from_pt=__UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     tf_from_pt_model(**__UpperCAmelCase ,       output_hidden_states=__UpperCAmelCase ,       training=__UpperCAmelCase				)

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

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

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

					__UpperCAmelCase   :      Union[str, Any]										=     """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..."""				)
								__UpperCAmelCase   :      Optional[int]										=     (
								    """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				):
											__UpperCAmelCase   :      List[Any]										=     [
											    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:
											__UpperCAmelCase   :      List[Any]										=     [CommitOperationAdd(path_in_repo=__UpperCAmelCase ,       path_or_fileobj=__UpperCAmelCase				)]

								__UpperCAmelCase   :      str										=     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'''
from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims




