'''simple docstring'''




import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase				   =  logging.get_logger()






@dataclass
class     a__:


			'''simple docstring'''






			UpperCAmelCase_      :					nn.Module
			UpperCAmelCase_      :					List[nn.Module]								=	field(default_factory=lowerCAmelCase__     )
			UpperCAmelCase_      :					list								=	field(default_factory=lowerCAmelCase__     )
			UpperCAmelCase_      :					Dict[str, nn.Module]								=	field(default_factory=lowerCAmelCase__     )



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  len(list(m.modules())) == 1 or isinstance(__lowerCAmelCase	,   nn.Convad) or isinstance(__lowerCAmelCase	,   nn.BatchNormad)
					if has_not_submodules:
							self.traced.append(__lowerCAmelCase)
							lowerCAmelCase        =  m



			def __call__(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					for name, m in self.module.named_modules():
							self.handles.append(m.register_forward_hook(partial(self._forward_hook	,   name=__lowerCAmelCase)))
					self.module(__lowerCAmelCase)
					[x.remove() for x in self.handles]
					return self







			@property
			def 							a_	(       self):




					"""simple docstring"""


					return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys())) > 0}







class     a__( nn.Module     ):


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					super().__init__()

					lowerCAmelCase        =  []
					# - get the stem
					feature_blocks.append(("""conv1""", model.stem))
					# - get all the feature blocks
					for k, v in model.trunk_output.named_children():
							assert k.startswith("""block"""), f"Unexpected layer name {k}"
							lowerCAmelCase        =  len(__lowerCAmelCase) + 1
							feature_blocks.append((f"res{block_index}", v))

					lowerCAmelCase        =  nn.ModuleDict(__lowerCAmelCase)







			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					return get_trunk_forward_outputs(
					    __lowerCAmelCase	,   out_feat_keys=__lowerCAmelCase	,   feature_blocks=self._feature_blocks	,   )







class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			def 							a_	(       self):




					"""simple docstring"""


					return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]







def     snake_case__							(      _A: str			)   ->     Dict[str, str]:

		'''simple docstring'''


		lowerCAmelCase        =  RegNetConfig(depths=[2, 7, 17, 1]				,       hidden_sizes=[8, 8, 8, 8]				,       groups_width=8			)
		if "in1k" in model_name:
				lowerCAmelCase        =  RegNetForImageClassification(_A			)
		else:
				lowerCAmelCase        =  RegNetModel(_A			)
		# create from model (with small weights)
		lowerCAmelCase        =  FakeRegNetVisslWrapper(
		    RegNet(FakeRegNetParams(depth=27				,       group_width=1010				,       w_a=1744				,       w_a=620.83				,       w_m=2.52			)			)			)

		with torch.no_grad():
				lowerCAmelCase        =  from_model.eval()
				lowerCAmelCase        =  our_model.eval()

				lowerCAmelCase        =  torch.randn((1, 3, 32, 32)			)
				# trace both
				lowerCAmelCase        =  Tracker(_A			)
				lowerCAmelCase        =  dest_tracker(_A			).parametrized

				pprint(dest_tracker.nameamodule			)
				lowerCAmelCase        =  Tracker(_A			)
				lowerCAmelCase        =  src_tracker(_A			).parametrized

		# convert the keys -> module dict to keys -> params
		def to_params_dict(_A: Dict			):
				lowerCAmelCase        =  OrderedDict()
				for name, module in dict_with_modules.items():
						for param_name, param in module.state_dict().items():
								lowerCAmelCase        =  param
				return params_dict

		lowerCAmelCase        =  {}

		lowerCAmelCase        =  to_params_dict(_A			)
		lowerCAmelCase        =  to_params_dict(_A			)

		for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()				,       dst_state_dict.items()			):
				lowerCAmelCase        =  dest_key
				logger.info(f"{src_key} -> {dest_key}"			)
		# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
		if "in1k" in model_name:
				lowerCAmelCase        =  """classifier.1.weight"""
				lowerCAmelCase        =  """classifier.1.bias"""

		return from_to_ours_keys






def     snake_case__							(      _A: Path				,       _A: str = None				,       _A: bool = True			)   ->     Optional[int]:

		'''simple docstring'''


		lowerCAmelCase        =  """imagenet-1k-id2label.json"""
		lowerCAmelCase        =  1000

		lowerCAmelCase        =  """huggingface/label-files"""
		lowerCAmelCase        =  num_labels
		lowerCAmelCase        =  json.load(open(cached_download(hf_hub_url(_A				,       _A				,       repo_type="""dataset"""			)			)				,       """r"""			)			)
		lowerCAmelCase        =  {int(_A			): v for k, v in idalabel.items()}

		lowerCAmelCase        =  idalabel
		lowerCAmelCase        =  {v: k for k, v in idalabel.items()}

		lowerCAmelCase        =  partial(_A				,       num_labels=_A				,       idalabel=_A				,       labelaid=_A			)

		lowerCAmelCase        =  {
		    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]				,       hidden_sizes=[2020, 4040, 11110, 28280]				,       groups_width=1010			),
		    # finetuned on imagenet
		    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]				,       hidden_sizes=[2020, 4040, 11110, 28280]				,       groups_width=1010			),
		}

		# add seer weights logic
		def load_using_classy_vision(_A: str			) -> Tuple[Dict, Dict]:
				lowerCAmelCase        =  torch.hub.load_state_dict_from_url(_A				,       model_dir=str(_A			)				,       map_location="""cpu"""			)
				# check if we have a head, if yes add it
				lowerCAmelCase        =  files["""classy_state_dict"""]["""base_model"""]["""model"""]
				return model_state_dict["trunk"], model_state_dict["heads"]

		lowerCAmelCase        =  {
		    """regnet-y-10b-seer""": partial(
		        _A				,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""				,       ),
		    """regnet-y-10b-seer-in1k""": partial(
		        _A				,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""				,       ),
		}

		lowerCAmelCase        =  get_from_to_our_keys(_A			)

		if not (save_directory / f"{model_name}.pth").exists():
				logger.info("""Loading original state_dict."""			)
				lowerCAmelCase ,					lowerCAmelCase        =  names_to_from_model[model_name]()
				lowerCAmelCase        =  from_state_dict_trunk
				if "in1k" in model_name:
						# add the head
						lowerCAmelCase        =  {**from_state_dict_trunk, **from_state_dict_head}
				logger.info("""Done!"""			)

				lowerCAmelCase        =  {}

				lowerCAmelCase        =  list(from_state_dict.keys()			)
				lowerCAmelCase        =  r"""\.block.-part."""
				# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
				for key in from_state_dict.keys():
						# remove the weird "block[0,1]-part" from the key
						lowerCAmelCase        =  re.sub(_A				,       """"""				,       _A			)
						# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
						lowerCAmelCase        =  from_to_ours_keys[src_key]
						# store the parameter with our key
						lowerCAmelCase        =  from_state_dict[key]
						not_used_keys.remove(_A			)
				# check that all keys have been updated
				assert len(_A			) == 0, f"Some keys where not used {','.join(_A			)}"

				logger.info(f"The following keys were not used: {','.join(_A			)}"			)

				# save our state dict to disk
				torch.save(_A				,       save_directory / f"{model_name}.pth"			)

				del converted_state_dict
		else:
				logger.info("""The state_dict was already stored on disk."""			)
		if push_to_hub:
				logger.info(f"Token is {os.environ['HF_TOKEN']}"			)
				logger.info("""Loading our model."""			)
				# create our model
				lowerCAmelCase        =  names_to_config[model_name]
				lowerCAmelCase        =  RegNetModel
				if "in1k" in model_name:
						lowerCAmelCase        =  RegNetForImageClassification
				lowerCAmelCase        =  our_model_func(_A			)
				# place our model to the meta device (so remove all the weights)
				our_model.to(torch.device("""meta"""			)			)
				logger.info("""Loading state_dict in our model."""			)
				# load state dict
				lowerCAmelCase        =  our_model.state_dict().keys()
				PreTrainedModel._load_pretrained_model_low_mem(
				    _A				,       _A				,       [save_directory / f"{model_name}.pth"]			)
				logger.info("""Finally, pushing!"""			)
				# push it to hub
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / model_name				,       commit_message="""Add model"""				,       output_dir=save_directory / model_name				,       )
				lowerCAmelCase        =  384
				# we can use the convnext one
				lowerCAmelCase        =  AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""				,       size=_A			)
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / model_name				,       commit_message="""Add image processor"""				,       output_dir=save_directory / model_name				,       )


if __name__ == "__main__":
						__lowercase				   =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--model_name''',
						    default=None,
						    type=str,
						    help=(
						        '''The name of the model you wish to convert, it must be one of the supported regnet* architecture,'''
						        ''' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'''
						    ),
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default=None,
						    type=Path,
						    required=True,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument(
						    '''--push_to_hub''',
						    default=True,
						    type=bool,
						    required=False,
						    help='''If True, push model and image processor to the hub.''',
						)

						__lowercase				   =  parser.parse_args()

						__lowercase				   =  args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




'''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


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






def     snake_case__							(      _A: Namespace			)   ->     str:

		'''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_	(       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  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):




					"""simple docstring"""


					lowerCAmelCase        =  set(pt_outputs.keys())
					lowerCAmelCase        =  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):
									lowerCAmelCase        =  np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
									lowerCAmelCase        =  tensor_difference
							else:
									lowerCAmelCase        =  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):
													lowerCAmelCase        =  root_name + pt_item
													lowerCAmelCase        =  tf_out[pt_item]
													lowerCAmelCase        =  pt_out[pt_item]
											else:
													lowerCAmelCase        =  root_name + f"[{i}]"
													lowerCAmelCase        =  tf_out[i]
											lowerCAmelCase        =  _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	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  logging.get_logger("""transformers-cli/pt_to_tf""")
					lowerCAmelCase        =  model_name
					lowerCAmelCase        =  local_dir if local_dir else os.path.join("""/tmp"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  max_error
					lowerCAmelCase        =  new_weights
					lowerCAmelCase        =  no_pr
					lowerCAmelCase        =  push
					lowerCAmelCase        =  extra_commit_description
					lowerCAmelCase        =  override_model_class



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


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

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

					lowerCAmelCase        =  set(inspect.signature(pt_model.forward).parameters.keys())
					lowerCAmelCase        =  {}
					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        =  load_dataset("""cifar10"""	,   """plain_text"""	,   split="""test""")[:2]["""img"""]
							processor_inputs.update({"""images""": sample_images})
					if "input_features" in model_forward_signature:
							lowerCAmelCase        =  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        =  feature_extractor_signature["""padding"""].default
									if default_strategy is not False and default_strategy is not None:
											lowerCAmelCase        =  default_strategy
									else:
											lowerCAmelCase        =  True
							else:
									lowerCAmelCase        =  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        =  processor(**__lowerCAmelCase	,   return_tensors="""pt""")
					lowerCAmelCase        =  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
					):
							lowerCAmelCase        =  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):




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

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

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

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

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

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

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

							lowerCAmelCase        =  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}")




