




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  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			List[Any]:
							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(					snake_case_					):



	"""simple docstring"""






	@staticmethod
	def    __a     (					lowerCamelCase    ) ->      Union[str, Any]:





								"""simple docstring"""


								lowercase__  :	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=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    __a     (					lowerCamelCase	,				lowerCamelCase    ) ->      str:





								"""simple docstring"""


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





								"""simple docstring"""


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

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ) ->      Tuple:





								"""simple docstring"""


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

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

								lowercase__  :	List[str]        =							set(inspect.signature(pt_model.forward    ).parameters.keys()    )
								lowercase__  :	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:
															lowercase__  :	List[str]        =							load_dataset("cifar10"	,				"plain_text"	,				split="test"    )[:2]["img"]
															processor_inputs.update({"images": sample_images}    )
								if "input_features" in model_forward_signature:
															lowercase__  :	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:
																						lowercase__  :	Union[str, Any]        =							feature_extractor_signature["padding"].default
																						if default_strategy is not False and default_strategy is not None:
																													lowercase__  :	Tuple        =							default_strategy
																						else:
																													lowercase__  :	List[Any]        =							True
															else:
																						lowercase__  :	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}    )
								lowercase__  :	List[str]        =							processor(**lowerCamelCase	,				return_tensors="pt"    )
								lowercase__  :	Dict        =							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
								):
															lowercase__  :	List[Any]        =							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    __a     (					self    ) ->      Optional[int]:





								"""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
								lowercase__  :	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
								lowercase__  :	List[Any]        =							AutoConfig.from_pretrained(self._local_dir    )
								lowercase__  :	Tuple        =							config.architectures
								if self._override_model_class is not None:
															if self._override_model_class.startswith("TF"    ):
																						lowercase__  :	List[Any]        =							[self._override_model_class[2:]]
															else:
																						lowercase__  :	Tuple        =							[self._override_model_class]
															try:
																						lowercase__  :	Dict        =							getattr(import_module("transformers"    )	,				architectures[0]    )
															except AttributeError:
																						raise ValueError(f"""Model class {self._override_model_class} not found in transformers."""    )
															try:
																						lowercase__  :	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
															lowercase__  :	Optional[int]        =							getattr(import_module("transformers"    )	,				"AutoModel"    )
															lowercase__  :	Tuple        =							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]}"""    )
															lowercase__  :	Dict        =							getattr(import_module("transformers"    )	,				architectures[0]    )
															try:
																						lowercase__  :	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
								lowercase__  :	int        =							tf_class.from_config(lowerCamelCase    )
								lowercase__  :	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
								lowercase__  :	Tuple        =							pt_class.from_pretrained(self._local_dir    )
								pt_model.eval()

								lowercase__ ,       lowercase__  :	Optional[int]        =							self.get_inputs(lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    )

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

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

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

								lowercase__  :	List[str]        =							tf_class.from_pretrained(self._local_dir    )
								lowercase__  :	Tuple        =							tf_model(**lowerCamelCase	,				output_hidden_states=lowerCamelCase    )

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

								lowercase__  :	Optional[int]        =							"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..."    )
															lowercase__  :	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(lowerCamelCase    ):
																						lowercase__  :	str        =							[
																						    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:
																						lowercase__  :	List[str]        =							[CommitOperationAdd(path_in_repo=lowerCamelCase	,				path_or_fileobj=lowerCamelCase    )]

															lowercase__  :	Dict        =							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}"""    )





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a  :		Any		     =   logging.get_logger(__name__)

__a  :		Union[str, Any]		     =   {
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : str	  =					SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	a     : int	  =					"""squeezebert"""

	def __init__(					self	,				lowerCamelCase=30522	,				lowerCamelCase=768	,				lowerCamelCase=12	,				lowerCamelCase=12	,				lowerCamelCase=3072	,				lowerCamelCase="gelu"	,				lowerCamelCase=0.1	,				lowerCamelCase=0.1	,				lowerCamelCase=512	,				lowerCamelCase=2	,				lowerCamelCase=0.02	,				lowerCamelCase=1E-12	,				lowerCamelCase=0	,				lowerCamelCase=768	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=1	,				lowerCamelCase=4	,				lowerCamelCase=4	,				**lowerCamelCase	,				) ->      str:





								"""simple docstring"""


								super().__init__(pad_token_id=lowerCamelCase	,				**lowerCamelCase    )

								lowercase__  :	Any        =							vocab_size
								lowercase__  :	Tuple        =							hidden_size
								lowercase__  :	str        =							num_hidden_layers
								lowercase__  :	Optional[int]        =							num_attention_heads
								lowercase__  :	List[Any]        =							hidden_act
								lowercase__  :	Union[str, Any]        =							intermediate_size
								lowercase__  :	int        =							hidden_dropout_prob
								lowercase__  :	List[Any]        =							attention_probs_dropout_prob
								lowercase__  :	Any        =							max_position_embeddings
								lowercase__  :	List[Any]        =							type_vocab_size
								lowercase__  :	Any        =							initializer_range
								lowercase__  :	Optional[Any]        =							layer_norm_eps
								lowercase__  :	Any        =							embedding_size
								lowercase__  :	Optional[Any]        =							q_groups
								lowercase__  :	List[str]        =							k_groups
								lowercase__  :	List[Any]        =							v_groups
								lowercase__  :	str        =							post_attention_groups
								lowercase__  :	Optional[int]        =							intermediate_groups
								lowercase__  :	Optional[int]        =							output_groups







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	@property
	def    __a     (					self    ) ->      Mapping[str, Mapping[int, str]]:





								"""simple docstring"""


								if self.task == "multiple-choice":
															lowercase__  :	Union[str, Any]        =							{0: "batch", 1: "choice", 2: "sequence"}
								else:
															lowercase__  :	Union[str, Any]        =							{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ]    )