



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


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







def     __magic_name__  (      lowerCAmelCase_):
	'''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__				(			snake_case_       ):






						"""simple docstring"""




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

								return differences

							return _find_pt_tf_differences(a_				,							a_				,							{}     )




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




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_     ):


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

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

							lowerCamelCase_			:							List[str]      =       set(inspect.signature(pt_model.forward     ).parameters.keys()     )
							lowerCamelCase_			:							List[Any]      =       {}
							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[str]      =       load_dataset("cifar10"				,							"plain_text"				,							split="test"     )[:2]["img"]
								processor_inputs.update({"images": sample_images}     )
							if "input_features" in model_forward_signature:
								lowerCamelCase_			:							Union[str, Any]      =       inspect.signature(processor.feature_extractor     ).parameters
								# Pad to the largest input length by default but take feature extractor default
								# padding value if it exists e.g. "max_length" and is not False or None
								if "padding" in feature_extractor_signature:
									lowerCamelCase_			:							Union[str, Any]      =       feature_extractor_signature["padding"].default
									if default_strategy is not False and default_strategy is not None:
										lowerCamelCase_			:							Tuple      =       default_strategy
									else:
										lowerCamelCase_			:							List[Any]      =       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_			:							List[str]      =       processor(**a_				,							return_tensors="pt"     )
							lowerCamelCase_			:							Dict      =       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_			:							List[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 					_UpperCamelCase			(   self     ):


							if version.parse(huggingface_hub.__version__     ) < version.parse("0.9.0"     ):
								raise ImportError(
								    "The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
								    " installation."     )
							else:
								from huggingface_hub import Repository, create_commit
								from huggingface_hub._commit_api import CommitOperationAdd

							# Fetch remote data
							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[Any]      =       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_			:							List[Any]      =       [self._override_model_class[2:]]
								else:
									lowerCamelCase_			:							Tuple      =       [self._override_model_class]
								try:
									lowerCamelCase_			:							Dict      =       getattr(import_module("transformers"     )				,							architectures[0]     )
								except AttributeError:
									raise ValueError(F"""Model class {self._override_model_class} not found in transformers."""     )
								try:
									lowerCamelCase_			:							Optional[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
								lowerCamelCase_			:							Optional[int]      =       getattr(import_module("transformers"     )				,							"AutoModel"     )
								lowerCamelCase_			:							Tuple      =       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_			:							Dict      =       getattr(import_module("transformers"     )				,							architectures[0]     )
								try:
									lowerCamelCase_			:							str      =       getattr(import_module("transformers"     )				,							"TF" + architectures[0]     )
								except AttributeError:
									raise AttributeError(F"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."""     )

        # Check the TF dummy inputs to see what keys we need in the forward pass
							lowerCamelCase_			:							int      =       tf_class.from_config(a_     )
							lowerCamelCase_			:							int      =       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_			:							Tuple      =       pt_class.from_pretrained(self._local_dir     )
							pt_model.eval()

							lowerCamelCase_			:							Optional[int]      =       self.get_inputs(a_				,							a_				,							a_     )

							with torch.no_grad():
								lowerCamelCase_			:							Optional[Any]      =       pt_model(**a_				,							output_hidden_states=a_     )
							del pt_model  # will no longer be used, and may have a large memory footprint

							lowerCamelCase_			:							Optional[Any]      =       tf_class.from_pretrained(self._local_dir				,							from_pt=a_     )
							lowerCamelCase_			:							Optional[int]      =       tf_from_pt_model(**a_				,							output_hidden_states=a_				,							training=a_     )

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

							lowerCamelCase_			:							List[str]      =       self.find_pt_tf_differences(a_				,							a_     )
							lowerCamelCase_			:							Any      =       {k: v for k, v in conversion_differences.items() if "hidden" not in k}
							lowerCamelCase_			:							Optional[Any]      =       {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[Any]      =       max(output_differences.values()     ) if output_differences else 0.0
							lowerCamelCase_			:							str      =       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_			:							Optional[int]      =       "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_			:							Union[str, 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_			:							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_			:							List[str]      =       [CommitOperationAdd(path_in_repo=a_				,							path_or_fileobj=a_     )]

								lowerCamelCase_			:							Dict      =       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}"""     )





def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Union[str, Any]      =       0
	for ele in arr:
		total_sum += product_sum(lowerCAmelCase_      ,					depth + 1) if isinstance(lowerCAmelCase_      ,					lowerCAmelCase_) else ele
	return total_sum * depth







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	return product_sum(lowerCAmelCase_      ,					1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

