'''simple docstring'''




import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__lowercase				   =  1_6
__lowercase				   =  3_2






def     snake_case__							(      _A: Union[str, Any]			)   ->     Tuple:

		'''simple docstring'''


		return int(x / 2**20			)






class     a__:


			'''simple docstring'''






			def __enter__(       self):




					"""simple docstring"""


					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					lowerCAmelCase        =  torch.cuda.memory_allocated()
					lowerCAmelCase        =  psutil.Process()

					lowerCAmelCase        =  self.cpu_mem_used()
					lowerCAmelCase        =  True
					lowerCAmelCase        =  threading.Thread(target=self.peak_monitor_func)
					lowerCAmelCase        =  True
					peak_monitor_thread.start()
					return self



			def 							a_	(       self):




					"""simple docstring"""


					return self.process.memory_info().rss



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  -1

					while True:
							lowerCAmelCase        =  max(self.cpu_mem_used()	,   self.cpu_peak)

							# can't sleep or will not catch the peak right (this comment is here on purpose)
							# time.sleep(0.001) # 1msec

							if not self.peak_monitoring:
									break







			def __exit__(       self	,   *__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  False

					gc.collect()
					torch.cuda.empty_cache()
					lowerCAmelCase        =  torch.cuda.memory_allocated()
					lowerCAmelCase        =  torch.cuda.max_memory_allocated()
					lowerCAmelCase        =  bamb(self.end - self.begin)
					lowerCAmelCase        =  bamb(self.peak - self.begin)

					lowerCAmelCase        =  self.cpu_mem_used()
					lowerCAmelCase        =  bamb(self.cpu_end - self.cpu_begin)
					lowerCAmelCase        =  bamb(self.cpu_peak - self.cpu_begin)
					# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						__lowercase				   =  mocked_dataloaders  # noqa: F811






def     snake_case__							(      _A: List[str]				,       _A: Optional[int]			)   ->     Tuple:

		'''simple docstring'''


		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,       _A			) == "1":
				lowerCAmelCase        =  2

		# New Code #
		# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
		lowerCAmelCase        =  FullyShardedDataParallelPlugin(
		    state_dict_config=FullStateDictConfig(offload_to_cpu=_A				,       ranka_only=_A			)				,       optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_A				,       ranka_only=_A			)				,       )

		# Initialize accelerator
		if args.with_tracking:
				lowerCAmelCase        =  Accelerator(
				    cpu=args.cpu				,       mixed_precision=args.mixed_precision				,       log_with="""wandb"""				,       project_dir=args.logging_dir				,       fsdp_plugin=_A				,       )
		else:
				lowerCAmelCase        =  Accelerator(fsdp_plugin=_A			)
		accelerator.print(accelerator.distributed_type			)

		if hasattr(args.checkpointing_steps				,       """isdigit"""			):
				if args.checkpointing_steps == "epoch":
						lowerCAmelCase        =  args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						lowerCAmelCase        =  int(args.checkpointing_steps			)
				else:
						raise ValueError(
						    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."			)
		else:
				lowerCAmelCase        =  None
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase        =  config["""lr"""]
		lowerCAmelCase        =  int(config["""num_epochs"""]			)
		lowerCAmelCase        =  int(config["""seed"""]			)
		lowerCAmelCase        =  int(config["""batch_size"""]			)

		# We need to initialize the trackers we use, and also store our configuration
		if args.with_tracking:
				lowerCAmelCase        =  vars(_A			)
				accelerator.init_trackers("""fsdp_glue_no_trainer"""				,       _A			)

		lowerCAmelCase        =  AutoTokenizer.from_pretrained(args.model_name_or_path			)
		lowerCAmelCase        =  load_dataset("""glue"""				,       """mrpc"""			)
		lowerCAmelCase        =  evaluate.load("""glue"""				,       """mrpc"""			)

		def tokenize_function(_A: Tuple			):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase        =  tokenizer(examples["""sentence1"""]				,       examples["""sentence2"""]				,       truncation=_A				,       max_length=_A			)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				lowerCAmelCase        =  datasets.map(
				    _A				,       batched=_A				,       remove_columns=["""idx""", """sentence1""", """sentence2"""]				,       )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowerCAmelCase        =  tokenized_datasets.rename_column("""label"""				,       """labels"""			)

		# If the batch size is too big we use gradient accumulation
		lowerCAmelCase        =  1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				lowerCAmelCase        =  batch_size // MAX_GPU_BATCH_SIZE
				lowerCAmelCase        =  MAX_GPU_BATCH_SIZE

		def collate_fn(_A: str			):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowerCAmelCase        =  128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowerCAmelCase        =  16
				elif accelerator.mixed_precision != "no":
						lowerCAmelCase        =  8
				else:
						lowerCAmelCase        =  None

				return tokenizer.pad(
				    _A				,       padding="""longest"""				,       max_length=_A				,       pad_to_multiple_of=_A				,       return_tensors="""pt"""				,       )

		# Instantiate dataloaders.
		lowerCAmelCase        =  DataLoader(
		    tokenized_datasets["""train"""]				,       shuffle=_A				,       collate_fn=_A				,       batch_size=_A			)
		lowerCAmelCase        =  DataLoader(
		    tokenized_datasets["""validation"""]				,       shuffle=_A				,       collate_fn=_A				,       batch_size=_A			)

		set_seed(_A			)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCAmelCase        =  AutoModelForSequenceClassification.from_pretrained(
		    args.model_name_or_path				,       return_dict=_A				,       low_cpu_mem_usage=_A			)

		# New Code #
		# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
		lowerCAmelCase        =  accelerator.prepare(_A			)
		accelerator.print(_A			)

		# Instantiate optimizer
		# New Code #
		# For FSDP feature, at present it doesn't support multiple parameter groups,
		# so we need to create a single parameter group for the whole model
		lowerCAmelCase        =  torch.optim.AdamW(params=model.parameters()				,       lr=_A				,       weight_decay=2e-4			)

		# Instantiate scheduler
		lowerCAmelCase        =  get_linear_schedule_with_warmup(
		    optimizer=_A				,       num_warmup_steps=10				,       num_training_steps=(len(_A			) * num_epochs) // gradient_accumulation_steps				,       )

		# New Code #
		# For FSDP feature, prepare everything except the model as we have already prepared the model
		# before creating the optimizer
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  accelerator.prepare(
		    _A				,       _A				,       _A				,       _A			)

		lowerCAmelCase        =  0

		# Potentially load in the weights and states from a previous save
		if args.resume_from_checkpoint:
				if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
						accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"			)
						accelerator.load_state(args.resume_from_checkpoint			)
						lowerCAmelCase        =  os.path.basename(args.resume_from_checkpoint			)
				else:
						# Get the most recent checkpoint
						lowerCAmelCase        =  [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
						dirs.sort(key=os.path.getctime			)
						lowerCAmelCase        =  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract `epoch_{i}` or `step_{i}`
				lowerCAmelCase        =  os.path.splitext(_A			)[0]

				if "epoch" in training_difference:
						num_epochs -= int(training_difference.replace("""epoch_"""				,       """"""			)			)
						lowerCAmelCase        =  None
				else:
						lowerCAmelCase        =  int(training_difference.replace("""step_"""				,       """"""			)			)
						num_epochs -= resume_step // len(_A			)
						# If resuming by step, we also need to know exactly how far into the DataLoader we went
						lowerCAmelCase        =  (num_epochs * len(_A			)) - resume_step

    # Now we train the model
		for epoch in range(_A			):
				# New Code #
				# context manager to track the peak memory usage during the training epoch
				with TorchTracemalloc() as tracemalloc:
						model.train()
						if args.with_tracking:
								lowerCAmelCase        =  0
						for step, batch in enumerate(_A			):
								# We need to skip steps until we reach the resumed step
								if args.resume_from_checkpoint and epoch == 0:
										if resume_step is not None and step < resume_step:
												pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device			)
								lowerCAmelCase        =  model(**_A			)
								lowerCAmelCase        =  outputs.loss
								lowerCAmelCase        =  loss / gradient_accumulation_steps
								# We keep track of the loss at each epoch
								if args.with_tracking:
										total_loss += loss.detach().float()
								accelerator.backward(_A			)
								if step % gradient_accumulation_steps == 0:
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										# accelerator.print(lr_scheduler.get_lr())

								overall_step += 1

								if isinstance(_A				,       _A			):
										lowerCAmelCase        =  f"step_{overall_step}"
										if overall_step % checkpointing_steps == 0:
												if args.output_dir is not None:
														lowerCAmelCase        =  os.path.join(args.output_dir				,       _A			)
												accelerator.save_state(_A			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
				accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin			)			)			)
				accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used			)			)
				accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked			)			)
				accelerator.print(
				    """Total Peak Memory consumed during the train (max): {}""".format(
				        tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
				# Logging the peak memory usage of the GPU to the tracker
				if args.with_tracking:
						accelerator.log(
						    {
						        """train_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin			),
						    }				,       step=_A				,       )

				# New Code #
				# context manager to track the peak memory usage during the evaluation
				with TorchTracemalloc() as tracemalloc:
						model.eval()
						for step, batch in enumerate(_A			):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device			)
								with torch.no_grad():
										lowerCAmelCase        =  model(**_A			)
								lowerCAmelCase        =  outputs.logits.argmax(dim=-1			)
								lowerCAmelCase ,					lowerCAmelCase        =  accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
								metric.add_batch(
								    predictions=_A				,       references=_A				,       )

						lowerCAmelCase        =  metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"epoch {epoch}:"				,       _A			)
						if args.with_tracking:
								accelerator.log(
								    {
								        """accuracy""": eval_metric["""accuracy"""],
								        """f1""": eval_metric["""f1"""],
								        """train_loss""": total_loss.item() / len(_A			),
								    }				,       step=_A				,       )

						if checkpointing_steps == "epoch":
								lowerCAmelCase        =  f"epoch_{epoch}"
								if args.output_dir is not None:
										lowerCAmelCase        =  os.path.join(args.output_dir				,       _A			)
								accelerator.save_state(_A			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
				accelerator.print("""Memory before entering the eval : {}""".format(bamb(tracemalloc.begin			)			)			)
				accelerator.print("""Memory consumed at the end of the eval (end-begin): {}""".format(tracemalloc.used			)			)
				accelerator.print("""Peak Memory consumed during the eval (max-begin): {}""".format(tracemalloc.peaked			)			)
				accelerator.print(
				    """Total Peak Memory consumed during the eval (max): {}""".format(tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
				# Logging the peak memory usage of the GPU to the tracker
				if args.with_tracking:
						accelerator.log(
						    {
						        """eval_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin			),
						    }				,       step=_A				,       )

		if args.with_tracking:
				accelerator.end_training()






def     snake_case__							(      )   ->     Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase        =  argparse.ArgumentParser(description="""Simple example of training script."""			)
		parser.add_argument(
		    """--mixed_precision"""				,       type=_A				,       default=_A				,       choices=["""no""", """fp16""", """bf16""", """fp8"""]				,       help="""Whether to use mixed precision. Choose"""
		    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
		    """and an Nvidia Ampere GPU."""				,       )
		parser.add_argument("""--cpu"""				,       action="""store_true"""				,       help="""If passed, will train on the CPU."""			)
		parser.add_argument(
		    """--checkpointing_steps"""				,       type=_A				,       default=_A				,       help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""				,       )
		parser.add_argument(
		    """--resume_from_checkpoint"""				,       type=_A				,       default=_A				,       help="""If the training should continue from a checkpoint folder."""				,       )
		parser.add_argument(
		    """--with_tracking"""				,       action="""store_true"""				,       help="""Whether to load in all available experiment trackers from the environment and use them for logging."""				,       )
		parser.add_argument(
		    """--output_dir"""				,       type=_A				,       default="""."""				,       help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				,       )
		parser.add_argument(
		    """--logging_dir"""				,       type=_A				,       default="""logs"""				,       help="""Location on where to store experiment tracking logs`"""				,       )
		parser.add_argument(
		    """--model_name_or_path"""				,       type=_A				,       help="""Path to pretrained model or model identifier from huggingface.co/models."""				,       required=_A				,       )
		lowerCAmelCase        =  parser.parse_args()
		lowerCAmelCase        =  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(_A				,       _A			)


if __name__ == "__main__":
						main()





'''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}")




