'''simple docstring'''




def     snake_case__							(      _A: str				,       _A: Dict			)   ->     list:

		'''simple docstring'''


		lowerCAmelCase        =  sorted(_A				,       key=lambda _A			: value[2]				,       reverse=_A			)

		# Create a list of size equal to the maximum deadline
		# and initialize it with -1
		lowerCAmelCase        =  max(_A				,       key=lambda _A			: value[1]			)[1]
		lowerCAmelCase        =  [-1] * max_deadline

		# Finding the maximum profit and the count of jobs
		lowerCAmelCase        =  0
		lowerCAmelCase        =  0
		for job in jobs:
				# Find a free time slot for this job
				# (Note that we start from the last possible slot)
				for i in range(job[1] - 1				,       -1				,       -1			):
						if time_slots[i] == -1:
								lowerCAmelCase        =  job[0]
								count += 1
								max_profit += job[2]
								break
		return [count, max_profit]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




'''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}")




