





'''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


snake_case_   :  Union[str, Any]								=      5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors



def  lowercase__(		_UpperCamelCase  : Namespace				)->   Tuple:






					"""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_ (  lowerCAmelCase_  ):

			'''simple docstring'''





			@staticmethod
			def       a	(					A_   ):
								_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=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       a	(					A_			,     A_   ):
								_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(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   ):
																		_UpperCamelCase											=   np.max(np.abs(pt_out.numpy() - tf_out.numpy()   )   )
																		_UpperCamelCase											=   tensor_difference
													else:
																		_UpperCamelCase											=   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_   ):
																												_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(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_			,     ):
								_UpperCamelCase											=   logging.get_logger("transformers-cli/pt_to_tf"   )
								_UpperCamelCase											=   model_name
								_UpperCamelCase											=   local_dir if local_dir else os.path.join("/tmp"			,     A_   )
								_UpperCamelCase											=   max_error
								_UpperCamelCase											=   new_weights
								_UpperCamelCase											=   no_pr
								_UpperCamelCase											=   push
								_UpperCamelCase											=   extra_commit_description
								_UpperCamelCase											=   override_model_class




			def       a	(					self			,     A_			,     A_			,     A_   ):

								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(**A_			,     return_tensors="pt"   )
								_UpperCamelCase											=   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
								):
													_UpperCamelCase											=   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       a	(					self   ):
								# 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(A_   ) > 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(A_   )
								_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(A_			,     A_			,     A_   )

								with torch.no_grad():
													_UpperCamelCase											=   pt_model(**A_			,     output_hidden_states=A_   )
								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=A_   )
								_UpperCamelCase											=   tf_from_pt_model(**A_			,     output_hidden_states=A_			,     training=A_   )

								# Confirms that cross loading PT weights into TF worked.
								_UpperCamelCase											=   self.find_pt_tf_differences(A_			,     A_   )
								_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(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'"   )
								_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			,     A_   )
								_UpperCamelCase											=   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

								_UpperCamelCase											=   tf_class.from_pretrained(self._local_dir   )
								_UpperCamelCase											=   tf_model(**A_			,     output_hidden_states=A_   )

								_UpperCamelCase											=   self.find_pt_tf_differences(A_			,     A_   )
								_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(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'"   )
								_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=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..."   )
													_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(A_   ):
																		_UpperCamelCase											=   [
																		    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:
																		_UpperCamelCase											=   [CommitOperationAdd(path_in_repo=A_			,     path_or_fileobj=A_   )]

													_UpperCamelCase											=   create_commit(
													    repo_id=self._model_name			,     operations=A_			,     commit_message=A_			,     commit_description=A_			,     repo_type="model"			,     create_pr=A_			,     ).pr_url
													self._logger.warning(F"PR open in {hub_pr_url}"   )










'''simple docstring'''



import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class     A_ (  nn.Linear  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_			,     A_ = True			,     A_ = "constant"			,     A_ = 0.0			,     A_ = "topK"			,     ):
								super(A_			,     self   ).__init__(in_features=A_			,     out_features=A_			,     bias=A_   )
								assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
								_UpperCamelCase											=   pruning_method

								if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
													_UpperCamelCase											=   mask_scale
													_UpperCamelCase											=   mask_init
													_UpperCamelCase											=   nn.Parameter(torch.empty(self.weight.size()   )   )
													self.init_mask()




			def       a	(					self   ):
								if self.mask_init == "constant":
													init.constant_(self.mask_scores			,     val=self.mask_scale   )
								elif self.mask_init == "uniform":
													init.uniform_(self.mask_scores			,     a=-self.mask_scale			,     b=self.mask_scale   )
								elif self.mask_init == "kaiming":
													init.kaiming_uniform_(self.mask_scores			,     a=math.sqrt(5   )   )




			def       a	(					self			,     A_			,     A_   ):
								# Get the mask
								if self.pruning_method == "topK":
													_UpperCamelCase											=   TopKBinarizer.apply(self.mask_scores			,     A_   )
								elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
													_UpperCamelCase											=   "sigmoied" in self.pruning_method
													_UpperCamelCase											=   ThresholdBinarizer.apply(self.mask_scores			,     A_			,     A_   )
								elif self.pruning_method == "magnitude":
													_UpperCamelCase											=   MagnitudeBinarizer.apply(self.weight			,     A_   )
								elif self.pruning_method == "l0":
													_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   -0.1, 1.1, 2 / 3
													if self.training:
																		_UpperCamelCase											=   torch.zeros_like(self.mask_scores   ).uniform_().clamp(0.0001			,     0.9999   )
																		_UpperCamelCase											=   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b   )
													else:
																		_UpperCamelCase											=   torch.sigmoid(self.mask_scores   )
													_UpperCamelCase											=   s * (r - l) + l
													_UpperCamelCase											=   s_bar.clamp(min=0.0			,     max=1.0   )
								# Mask weights with computed mask
								_UpperCamelCase											=   mask * self.weight
								# Compute output (linear layer) with masked weights
								return nn.functional.linear(A_			,     A_			,     self.bias   )



