





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


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

def      a    (   _lowercase :  Namespace							)  ->List[str]:
		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 	_UpperCAmelCase						( _lowercase       ):




					@staticmethod
					def 						snake_case_		(    __lowerCAmelCase							)    ->	Optional[Any]:
							UpperCamelCase__								=						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 						snake_case_		(    __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	List[Any]:
							UpperCamelCase__								=						set(pt_outputs.keys()							)
							UpperCamelCase__								=						set(tf_outputs.keys()							)
							if pt_out_attrs != tf_out_attrs:
									raise ValueError(
									    F"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
									    F""" {tf_out_attrs})"""							)

							# 2. For each output attribute, computes the difference
							def _find_pt_tf_differences(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase=""							):
									# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
									# recursivelly, keeping the name of the attribute.
									if isinstance(__lowerCAmelCase     ,   torch.Tensor							):
											UpperCamelCase__								=						np.max(np.abs(pt_out.numpy() - tf_out.numpy()							)							)
											UpperCamelCase__								=						tensor_difference
									else:
											UpperCamelCase__								=						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							):
															UpperCamelCase__								=						root_name + pt_item
															UpperCamelCase__								=						tf_out[pt_item]
															UpperCamelCase__								=						pt_out[pt_item]
													else:
															UpperCamelCase__								=						root_name + F"""[{i}]"""
															UpperCamelCase__								=						tf_out[i]
													UpperCamelCase__								=						_find_pt_tf_differences(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)

									return differences

							return _find_pt_tf_differences(__lowerCAmelCase     ,   __lowerCAmelCase     ,   {}							)




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




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	Dict:

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

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

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

							UpperCamelCase__	,      UpperCamelCase__								=						self.get_inputs(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)

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

							UpperCamelCase__								=						tf_class.from_pretrained(self._local_dir     ,   from_pt=__lowerCAmelCase							)
							UpperCamelCase__								=						tf_from_pt_model(**__lowerCAmelCase     ,   output_hidden_states=__lowerCAmelCase     ,   training=__lowerCAmelCase							)

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

							UpperCamelCase__								=						tf_class.from_pretrained(self._local_dir							)
							UpperCamelCase__								=						tf_model(**__lowerCAmelCase     ,   output_hidden_states=__lowerCAmelCase							)

							UpperCamelCase__								=						self.find_pt_tf_differences(__lowerCAmelCase     ,   __lowerCAmelCase							)
							UpperCamelCase__								=						{k: v for k, v in conversion_differences.items() if """hidden""" not in k}
							UpperCamelCase__								=						{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'"""							)
							UpperCamelCase__								=						max(output_differences.values()							) if output_differences else 0.0
							UpperCamelCase__								=						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__								=						"""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..."""							)
									UpperCamelCase__								=						(
									    """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							):
											UpperCamelCase__								=						[
											    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:
											UpperCamelCase__								=						[CommitOperationAdd(path_in_repo=__lowerCAmelCase     ,   path_or_fileobj=__lowerCAmelCase							)]

									UpperCamelCase__								=						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}"""							)












def      a    (   _lowercase :  Dict			,    _lowercase :  List[str]							)  ->List[Any]:
		if len(str(_lowercase							)							) == 1 or len(str(_lowercase							)							) == 1:
				return a * b

		UpperCamelCase__								=						max(len(str(_lowercase							)							)			,    len(str(_lowercase							)							)							)
		UpperCamelCase__								=						ma // 2

		UpperCamelCase__	,      UpperCamelCase__								=						divmod(_lowercase			,    1_0**ma							)
		UpperCamelCase__	,      UpperCamelCase__								=						divmod(_lowercase			,    1_0**ma							)

		UpperCamelCase__								=						karatsuba(_lowercase			,    _lowercase							)
		UpperCamelCase__								=						karatsuba((aa + aa)			,    (ba + ba)							)
		UpperCamelCase__								=						karatsuba(_lowercase			,    _lowercase							)

		return (z * 1_0 ** (2 * ma)) + ((y - z - x) * 1_0 ** (ma)) + (x)

def      a    (   )  ->List[Any]:
		print(karatsuba(1_5_4_6_3			,    2_3_4_8_9							)							)


if __name__ == "__main__":
				main()





