





import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__  :	Dict     =logging.get_logger(__name__)





def    _lowercase    (  _UpperCAmelCase )  ->     Optional[int]:
				lowerCamelCase		       =ConvNextVaConfig()

				if "atto" in checkpoint_url:
								lowerCamelCase		       =[2, 2, 6, 2]
								lowerCamelCase		       =[40, 80, 1_60, 3_20]
				if "femto" in checkpoint_url:
								lowerCamelCase		       =[2, 2, 6, 2]
								lowerCamelCase		       =[48, 96, 1_92, 3_84]
				if "pico" in checkpoint_url:
								lowerCamelCase		       =[2, 2, 6, 2]
								lowerCamelCase		       =[64, 1_28, 2_56, 5_12]
				if "nano" in checkpoint_url:
								lowerCamelCase		       =[2, 2, 8, 2]
								lowerCamelCase		       =[80, 1_60, 3_20, 6_40]
				if "tiny" in checkpoint_url:
								lowerCamelCase		       =[3, 3, 9, 3]
								lowerCamelCase		       =[96, 1_92, 3_84, 7_68]
				if "base" in checkpoint_url:
								lowerCamelCase		       =[3, 3, 27, 3]
								lowerCamelCase		       =[1_28, 2_56, 5_12, 10_24]
				if "large" in checkpoint_url:
								lowerCamelCase		       =[3, 3, 27, 3]
								lowerCamelCase		       =[1_92, 3_84, 7_68, 15_36]
				if "huge" in checkpoint_url:
								lowerCamelCase		       =[3, 3, 27, 3]
								lowerCamelCase		       =[3_52, 7_04, 14_08, 28_16]

				lowerCamelCase		       =10_00
				lowerCamelCase		       ="""imagenet-1k-id2label.json"""
				lowerCamelCase		       =(1, 10_00)

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

				lowerCamelCase		       =idalabel
				lowerCamelCase		       ={v: k for k, v in idalabel.items()}
				lowerCamelCase		       =hidden_sizes
				lowerCamelCase		       =depths

				return config, expected_shape





def    _lowercase    (  _UpperCAmelCase )  ->     List[str]:


				if "downsample_layers.0.0" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.0.0"""	,					"""embeddings.patch_embeddings""" )
				if "downsample_layers.0.1" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.0.1"""	,					"""embeddings.norm""" )  # we rename to layernorm later on
				if "downsample_layers.1.0" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.1.0"""	,					"""stages.1.downsampling_layer.0""" )
				if "downsample_layers.1.1" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.1.1"""	,					"""stages.1.downsampling_layer.1""" )
				if "downsample_layers.2.0" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.2.0"""	,					"""stages.2.downsampling_layer.0""" )
				if "downsample_layers.2.1" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.2.1"""	,					"""stages.2.downsampling_layer.1""" )
				if "downsample_layers.3.0" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.3.0"""	,					"""stages.3.downsampling_layer.0""" )
				if "downsample_layers.3.1" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.3.1"""	,					"""stages.3.downsampling_layer.1""" )
				if "stages" in name and "downsampling_layer" not in name:
								# stages.0.0. for instance should be renamed to stages.0.layers.0.
								lowerCamelCase		       =name[: len("""stages.0""" )] + """.layers""" + name[len("""stages.0""" ) :]
				if "gamma" in name:
								lowerCamelCase		       =name.replace("""gamma"""	,					"""weight""" )
				if "beta" in name:
								lowerCamelCase		       =name.replace("""beta"""	,					"""bias""" )
				if "stages" in name:
								lowerCamelCase		       =name.replace("""stages"""	,					"""encoder.stages""" )
				if "norm" in name:
								lowerCamelCase		       =name.replace("""norm"""	,					"""layernorm""" )
				if "head" in name:
								lowerCamelCase		       =name.replace("""head"""	,					"""classifier""" )

				return name





def    _lowercase    (  )  ->     Union[str, Any]:
				lowerCamelCase		       ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowerCamelCase		       =Image.open(requests.get(_SCREAMING_SNAKE_CASE	,					stream=_SCREAMING_SNAKE_CASE ).raw )
				return im





def    _lowercase    (  _UpperCAmelCase )  ->     Any:


				if "224" in checkpoint_url:
								lowerCamelCase		       =2_24
								lowerCamelCase		       =2_24 / 2_56
				elif "384" in checkpoint_url:
								lowerCamelCase		       =3_84
								lowerCamelCase		       =None
				else:
								lowerCamelCase		       =5_12
								lowerCamelCase		       =None

				return ConvNextImageProcessor(
				    size=_SCREAMING_SNAKE_CASE	,					crop_pct=_SCREAMING_SNAKE_CASE	,					image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]	,					image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]	,					resample=PILImageResampling.BICUBIC	,					)





@torch.no_grad()
def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     Dict:
				print("""Downloading original model from checkpoint...""" )
				# define ConvNeXTV2 configuration based on URL
				lowerCamelCase       ,     lowerCamelCase		       =get_convnextva_config(_SCREAMING_SNAKE_CASE )
				# load original state_dict from URL
				lowerCamelCase		       =torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE )["""model"""]

				print("""Converting model parameters...""" )
				# rename keys
				for key in state_dict.copy().keys():
								lowerCamelCase		       =state_dict.pop(_SCREAMING_SNAKE_CASE )
								lowerCamelCase		       =val
				# add prefix to all keys expect classifier head
				for key in state_dict.copy().keys():
								lowerCamelCase		       =state_dict.pop(_SCREAMING_SNAKE_CASE )
								if not key.startswith("""classifier""" ):
												lowerCamelCase		       ="""convnextv2.""" + key
								lowerCamelCase		       =val

				# load HuggingFace model
				lowerCamelCase		       =ConvNextVaForImageClassification(_SCREAMING_SNAKE_CASE )
				model.load_state_dict(_SCREAMING_SNAKE_CASE )
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				lowerCamelCase		       =convert_preprocessor(_SCREAMING_SNAKE_CASE )
				lowerCamelCase		       =preprocessor(images=prepare_img()	,					return_tensors="""pt""" )
				lowerCamelCase		       =model(**_SCREAMING_SNAKE_CASE ).logits

				# note: the logits below were obtained without center cropping
				if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
								lowerCamelCase		       =torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
								lowerCamelCase		       =torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
								lowerCamelCase		       =torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
								lowerCamelCase		       =torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6] )
				else:
								raise ValueError(F"""Unknown URL: {checkpoint_url}""" )

				assert torch.allclose(logits[0, :5]	,					_SCREAMING_SNAKE_CASE	,					atol=1e-3 )
				assert logits.shape == expected_shape
				print("""Model outputs match the original results!""" )

				if save_model:
								print("""Saving model to local...""" )
								# Create folder to save model
								if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
												os.mkdir(_SCREAMING_SNAKE_CASE )

								model.save_pretrained(_SCREAMING_SNAKE_CASE )
								preprocessor.save_pretrained(_SCREAMING_SNAKE_CASE )

				lowerCamelCase		       ="""convnextv2"""
				if "atto" in checkpoint_url:
								model_name += "-atto"
				if "femto" in checkpoint_url:
								model_name += "-femto"
				if "pico" in checkpoint_url:
								model_name += "-pico"
				if "nano" in checkpoint_url:
								model_name += "-nano"
				elif "tiny" in checkpoint_url:
								model_name += "-tiny"
				elif "base" in checkpoint_url:
								model_name += "-base"
				elif "large" in checkpoint_url:
								model_name += "-large"
				elif "huge" in checkpoint_url:
								model_name += "-huge"
				if "22k" in checkpoint_url and "1k" not in checkpoint_url:
								model_name += "-22k"
				elif "22k" in checkpoint_url and "1k" in checkpoint_url:
								model_name += "-22k-1k"
				elif "1k" in checkpoint_url:
								model_name += "-1k"
				if "224" in checkpoint_url:
								model_name += "-224"
				elif "384" in checkpoint_url:
								model_name += "-384"
				elif "512" in checkpoint_url:
								model_name += "-512"

				if push_to_hub:
								print(F"""Pushing {model_name} to the hub...""" )
								model.push_to_hub(_SCREAMING_SNAKE_CASE )
								preprocessor.push_to_hub(_SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							UpperCAmelCase__  :	List[Any]     =argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt''',
							    type=str,
							    help='''URL of the original ConvNeXTV2 checkpoint you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default='''model''',
							    type=str,
							    help='''Path to the output PyTorch model directory.''',
							)
							parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
							parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image preprocessor to the hub''')

							UpperCAmelCase__  :	int     =parser.parse_args()
							convert_convnextva_checkpoint(
							    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
							)








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


UpperCamelCase__     :					str								=			5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						Namespace				):





    """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 					__snake_case     (			lowerCAmelCase__				):

  @staticmethod
  def   lowerCAmelCase__   (    _A):
      SCREAMING_SNAKE_CASE_													=  parser.add_parser(
          'pt-to-tf'  ,						help=(
              'CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'
              ' Can also be used to validate existing weights without opening PRs, with --no-pr.'
          )  ,						)
      train_parser.add_argument(
          '--model-name'  ,						type=_A  ,						required=_A  ,						help='The model name, including owner/organization, as seen on the hub.'  ,						)
      train_parser.add_argument(
          '--local-dir'  ,						type=_A  ,						default=''  ,						help='Optional local directory of the model repository. Defaults to /tmp/{model_name}'  ,						)
      train_parser.add_argument(
          '--max-error'  ,						type=_A  ,						default=_A  ,						help=(
              f"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
          )  ,						)
      train_parser.add_argument(
          '--new-weights'  ,						action='store_true'  ,						help='Optional flag to create new TensorFlow weights, even if they already exist.'  ,						)
      train_parser.add_argument(
          '--no-pr'  ,						action='store_true'  ,						help='Optional flag to NOT open a PR with converted weights.')
      train_parser.add_argument(
          '--push'  ,						action='store_true'  ,						help='Optional flag to push the weights directly to `main` (requires permissions)'  ,						)
      train_parser.add_argument(
          '--extra-commit-description'  ,						type=_A  ,						default=''  ,						help='Optional additional commit description to use when opening a PR (e.g. to tag the owner).'  ,						)
      train_parser.add_argument(
          '--override-model-class'  ,						type=_A  ,						default=_A  ,						help='If you think you know better than the auto-detector, you can specify the model class here. '
          'Can be either an AutoModel class or a specific model class like BertForSequenceClassification.'  ,						)
      train_parser.set_defaults(func=_A)

  @staticmethod
  def   lowerCAmelCase__   (    _A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  set(pt_outputs.keys())
      SCREAMING_SNAKE_CASE_													=  set(tf_outputs.keys())
      if pt_out_attrs != tf_out_attrs:
          raise ValueError(
              f"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
              f""" {tf_out_attrs})""")

      # 2. For each output attribute, computes the difference
      def _find_pt_tf_differences(_A  ,						_A  ,						_A  ,						_A=""):
          # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
          # recursivelly, keeping the name of the attribute.
          if isinstance(_A  ,						torch.Tensor):
              SCREAMING_SNAKE_CASE_													=  np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
              SCREAMING_SNAKE_CASE_													=  tensor_difference
          else:
              SCREAMING_SNAKE_CASE_													=  attr_name
              for i, pt_item in enumerate(_A):
                  # If it is a named attribute, we keep the name. Otherwise, just its index.
                  if isinstance(_A  ,						_A):
                      SCREAMING_SNAKE_CASE_													=  root_name + pt_item
                      SCREAMING_SNAKE_CASE_													=  tf_out[pt_item]
                      SCREAMING_SNAKE_CASE_													=  pt_out[pt_item]
                  else:
                      SCREAMING_SNAKE_CASE_													=  root_name + f"""[{i}]"""
                      SCREAMING_SNAKE_CASE_													=  tf_out[i]
                  SCREAMING_SNAKE_CASE_													=  _find_pt_tf_differences(_A  ,						_A  ,						_A  ,						_A)

          return differences

      return _find_pt_tf_differences(_A  ,						_A  ,						{})

  def __init__(    self  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						*_A  ,						):
      SCREAMING_SNAKE_CASE_													=  logging.get_logger('transformers-cli/pt_to_tf')
      SCREAMING_SNAKE_CASE_													=  model_name
      SCREAMING_SNAKE_CASE_													=  local_dir if local_dir else os.path.join('/tmp'  ,						_A)
      SCREAMING_SNAKE_CASE_													=  max_error
      SCREAMING_SNAKE_CASE_													=  new_weights
      SCREAMING_SNAKE_CASE_													=  no_pr
      SCREAMING_SNAKE_CASE_													=  push
      SCREAMING_SNAKE_CASE_													=  extra_commit_description
      SCREAMING_SNAKE_CASE_													=  override_model_class

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A):

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

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

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

      # Extra input requirements, in addition to the input modality
      if (
          config.is_encoder_decoder
          or (hasattr(_A  ,						'encoder') and hasattr(_A  ,						'decoder'))
          or "decoder_input_ids" in tf_dummy_inputs
      ):
          SCREAMING_SNAKE_CASE_													=  np.asarray([[1], [1]]  ,						dtype=_A) * (pt_model.config.decoder_start_token_id or 0)
          pt_input.update({'decoder_input_ids': torch.tensor(_A)})
          tf_input.update({'decoder_input_ids': tf.convert_to_tensor(_A)})

      return pt_input, tf_input


  def   lowerCAmelCase__   (    self):
      # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
      if version.parse(huggingface_hub.__version__) < version.parse('0.9.0'):
          raise ImportError(
              'The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'
              ' installation.')
      else:
          from huggingface_hub import Repository, create_commit
          from huggingface_hub._commit_api import CommitOperationAdd

      # Fetch remote data
      SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  AutoConfig.from_pretrained(self._local_dir)
      SCREAMING_SNAKE_CASE_													=  config.architectures
      if self._override_model_class is not None:
          if self._override_model_class.startswith('TF'):
              SCREAMING_SNAKE_CASE_													=  [self._override_model_class[2:]]
          else:
              SCREAMING_SNAKE_CASE_													=  [self._override_model_class]
          try:
              SCREAMING_SNAKE_CASE_													=  getattr(import_module('transformers')  ,						architectures[0])
          except AttributeError:
              raise ValueError(f"""Model class {self._override_model_class} not found in transformers.""")
          try:
              SCREAMING_SNAKE_CASE_													=  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
          SCREAMING_SNAKE_CASE_													=  getattr(import_module('transformers')  ,						'AutoModel')
          SCREAMING_SNAKE_CASE_													=  getattr(import_module('transformers')  ,						'TFAutoModel')
          self._logger.warning('No detected architecture, using AutoModel/TFAutoModel')
      else:  # Architecture defined -- use it
          if len(_A) > 1:
              raise ValueError(f"""More than one architecture was found, aborting. (architectures = {architectures})""")
          self._logger.warning(f"""Detected architecture: {architectures[0]}""")
          SCREAMING_SNAKE_CASE_													=  getattr(import_module('transformers')  ,						architectures[0])
          try:
              SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  tf_class.from_config(_A)
      SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  pt_class.from_pretrained(self._local_dir)
      pt_model.eval()

      SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  self.get_inputs(_A  ,						_A  ,						_A)

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

      SCREAMING_SNAKE_CASE_													=  tf_class.from_pretrained(self._local_dir  ,						from_pt=_A)
      SCREAMING_SNAKE_CASE_													=  tf_from_pt_model(**_A  ,						output_hidden_states=_A  ,						training=_A)

      # Confirms that cross loading PT weights into TF worked.
      SCREAMING_SNAKE_CASE_													=  self.find_pt_tf_differences(_A  ,						_A)
      SCREAMING_SNAKE_CASE_													=  {k: v for k, v in crossload_differences.items() if 'hidden' not in k}
      SCREAMING_SNAKE_CASE_													=  {k: v for k, v in crossload_differences.items() if 'hidden' in k}
      if len(_A) == 0 and architectures is not None:
          raise ValueError(
              f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
              ' output was found. All outputs start with \'hidden\'')
      SCREAMING_SNAKE_CASE_													=  max(output_differences.values()) if output_differences else 0.0
      SCREAMING_SNAKE_CASE_													=  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
      SCREAMING_SNAKE_CASE_													=  os.path.join(self._local_dir  ,						_A)
      SCREAMING_SNAKE_CASE_													=  os.path.join(self._local_dir  ,						_A)
      if (not os.path.exists(_A) and not os.path.exists(_A)) or self._new_weights:
          tf_from_pt_model.save_pretrained(self._local_dir)
      del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

      SCREAMING_SNAKE_CASE_													=  tf_class.from_pretrained(self._local_dir)
      SCREAMING_SNAKE_CASE_													=  tf_model(**_A  ,						output_hidden_states=_A)

      SCREAMING_SNAKE_CASE_													=  self.find_pt_tf_differences(_A  ,						_A)
      SCREAMING_SNAKE_CASE_													=  {k: v for k, v in conversion_differences.items() if 'hidden' not in k}
      SCREAMING_SNAKE_CASE_													=  {k: v for k, v in conversion_differences.items() if 'hidden' in k}
      if len(_A) == 0 and architectures is not None:
          raise ValueError(
              f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
              ' output was found. All outputs start with \'hidden\'')
      SCREAMING_SNAKE_CASE_													=  max(output_differences.values()) if output_differences else 0.0
      SCREAMING_SNAKE_CASE_													=  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]))

      SCREAMING_SNAKE_CASE_													=  'Update TF weights' if self._new_weights else 'Add TF weights'
      if self._push:
          repo.git_add(auto_lfs_track=_A)
          repo.git_commit(_A)
          repo.git_push(blocking=_A)  # this prints a progress bar with the upload
          self._logger.warning(f"""TF weights pushed into {self._model_name}""")
      elif not self._no_pr:
          self._logger.warning('Uploading the weights into a new PR...')
          SCREAMING_SNAKE_CASE_													=  (
              'Model converted by the [`transformers`\' `pt_to_tf`'
              ' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '
              'All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'
              f"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
              f"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
              f"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
              f"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
          )
          if self._max_error > MAX_ERROR:
              commit_descrition += (
                  f"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
              )
          if self._extra_commit_description:
              commit_descrition += "\n\n" + self._extra_commit_description

          # sharded model -> adds all related files (index and .h5 shards)
          if os.path.exists(_A):
              SCREAMING_SNAKE_CASE_													=  [
                  CommitOperationAdd(path_in_repo=_A  ,						path_or_fileobj=_A)
              ]
              for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5'):
                  operations += [
                      CommitOperationAdd(path_in_repo=os.path.basename(_A)  ,						path_or_fileobj=_A)
                  ]
          else:
              SCREAMING_SNAKE_CASE_													=  [CommitOperationAdd(path_in_repo=_A  ,						path_or_fileobj=_A)]

          SCREAMING_SNAKE_CASE_													=  create_commit(
              repo_id=self._model_name  ,						operations=_A  ,						commit_message=_A  ,						commit_description=_A  ,						repo_type='model'  ,						create_pr=_A  ,						).pr_url
          self._logger.warning(f"""PR open in {hub_pr_url}""")


