'''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


UpperCamelCase       :     Optional[Any]			=    5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Namespace  )   ->				List[Any]:







     """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   UpperCamelCase					(       a_       ):







    """simple docstring"""





    @staticmethod
    def    SCREAMING_SNAKE_CASE_     (					UpperCAmelCase_     :						ArgumentParser):


         """simple docstring"""







         a					:							Optional[Any]							= parser.add_parser(
             'pt-to-tf'							,							help=(
                 'CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'
                 ' Can also be used to validate existing weights without opening PRs, with --no-pr.'
             )							,							)
         train_parser.add_argument(
             '--model-name'							,							type=UpperCAmelCase_							,							required=UpperCAmelCase_							,							help='The model name, including owner/organization, as seen on the hub.'							,							)
         train_parser.add_argument(
             '--local-dir'							,							type=UpperCAmelCase_							,							default=''							,							help='Optional local directory of the model repository. Defaults to /tmp/{model_name}'							,							)
         train_parser.add_argument(
             '--max-error'							,							type=UpperCAmelCase_							,							default=UpperCAmelCase_							,							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=UpperCAmelCase_							,							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=UpperCAmelCase_							,							default=UpperCAmelCase_							,							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=UpperCAmelCase_)





    @staticmethod
    def    SCREAMING_SNAKE_CASE_     (					UpperCAmelCase_     :						int							,							UpperCAmelCase_     :						Dict):


         """simple docstring"""







         a					:							Dict							= set(pt_outputs.keys())
         a					:							List[Any]							= 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(UpperCAmelCase_     :						List[str]							,							UpperCAmelCase_     :						List[Any]							,							UpperCAmelCase_     :						Dict							,							UpperCAmelCase_     :						Any=""):
              # 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(UpperCAmelCase_							,							torch.Tensor):
                   a					:							Dict							= np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
                   a					:							Union[str, Any]							= tensor_difference
              else:
                   a					:							List[str]							= attr_name
                   for i, pt_item in enumerate(UpperCAmelCase_):
                        # If it is a named attribute, we keep the name. Otherwise, just its index.
                        if isinstance(UpperCAmelCase_							,							UpperCAmelCase_):
                             a					:							List[str]							= root_name + pt_item
                             a					:							Union[str, Any]							= tf_out[pt_item]
                             a					:							Optional[Any]							= pt_out[pt_item]
                        else:
                             a					:							Optional[int]							= root_name + f"""[{i}]"""
                             a					:							List[Any]							= tf_out[i]
                        a					:							Union[str, Any]							= _find_pt_tf_differences(UpperCAmelCase_							,							UpperCAmelCase_							,							UpperCAmelCase_							,							UpperCAmelCase_)

              return differences

         return _find_pt_tf_differences(UpperCAmelCase_							,							UpperCAmelCase_							,							{})





    def __init__(					self     :						Optional[Any]							,							UpperCAmelCase_     :						str							,							UpperCAmelCase_     :						str							,							UpperCAmelCase_     :						float							,							UpperCAmelCase_     :						bool							,							UpperCAmelCase_     :						bool							,							UpperCAmelCase_     :						bool							,							UpperCAmelCase_     :						str							,							UpperCAmelCase_     :						str							,							*UpperCAmelCase_     :						List[Any]							,							):


         """simple docstring"""







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





    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						int):


         """simple docstring"""







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

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

         a					:							Optional[int]							= set(inspect.signature(pt_model.forward).parameters.keys())
         a					:							Optional[int]							= {}
         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:
              a					:							Tuple							= load_dataset('cifar10'							,							'plain_text'							,							split='test')[:2]['img']
              processor_inputs.update({'images': sample_images})
         if "input_features" in model_forward_signature:
              a					:							Union[str, Any]							= inspect.signature(processor.feature_extractor).parameters
              # Pad to the largest input length by default but take feature extractor default
              # padding value if it exists e.g. "max_length" and is not False or None
              if "padding" in feature_extractor_signature:
                   a					:							Tuple							= feature_extractor_signature['padding'].default
                   if default_strategy is not False and default_strategy is not None:
                        a					:							Optional[Any]							= default_strategy
                   else:
                        a					:							Union[str, Any]							= True
              else:
                   a					:							List[Any]							= True
              processor_inputs.update({'audio': _get_audio_input(), 'padding': padding_strategy})
         if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
              processor_inputs.update({'audio': _get_audio_input(), 'padding': True})
         a					:							Tuple							= processor(**UpperCAmelCase_							,							return_tensors='pt')
         a					:							Any							= processor(**UpperCAmelCase_							,							return_tensors='tf')

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

         return pt_input, tf_input







    def    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         if version.parse(huggingface_hub.__version__) < version.parse('0.9.0'):
              raise ImportError(
                  'The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'
                  ' installation.')
         else:
              from huggingface_hub import Repository, create_commit
              from huggingface_hub._commit_api import CommitOperationAdd

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

         a							,			a					:							Union[str, Any]							= self.get_inputs(UpperCAmelCase_							,							UpperCAmelCase_							,							UpperCAmelCase_)

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

         a					:							Tuple							= tf_class.from_pretrained(self._local_dir							,							from_pt=UpperCAmelCase_)
         a					:							str							= tf_from_pt_model(**UpperCAmelCase_							,							output_hidden_states=UpperCAmelCase_							,							training=UpperCAmelCase_)

         # Confirms that cross loading PT weights into TF worked.
         a					:							List[Any]							= self.find_pt_tf_differences(UpperCAmelCase_							,							UpperCAmelCase_)
         a					:							str							= {k: v for k, v in crossload_differences.items() if 'hidden' not in k}
         a					:							Optional[Any]							= {k: v for k, v in crossload_differences.items() if 'hidden' in k}
         if len(UpperCAmelCase_) == 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\'')
         a					:							int							= max(output_differences.values()) if output_differences else 0.0
         a					:							Dict							= 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
         a					:							Dict							= os.path.join(self._local_dir							,							UpperCAmelCase_)
         a					:							Optional[Any]							= os.path.join(self._local_dir							,							UpperCAmelCase_)
         if (not os.path.exists(UpperCAmelCase_) and not os.path.exists(UpperCAmelCase_)) 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

         a					:							Union[str, Any]							= tf_class.from_pretrained(self._local_dir)
         a					:							Optional[Any]							= tf_model(**UpperCAmelCase_							,							output_hidden_states=UpperCAmelCase_)

         a					:							int							= self.find_pt_tf_differences(UpperCAmelCase_							,							UpperCAmelCase_)
         a					:							List[Any]							= {k: v for k, v in conversion_differences.items() if 'hidden' not in k}
         a					:							Union[str, Any]							= {k: v for k, v in conversion_differences.items() if 'hidden' in k}
         if len(UpperCAmelCase_) == 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\'')
         a					:							str							= max(output_differences.values()) if output_differences else 0.0
         a					:							Optional[int]							= 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]))

         a					:							Union[str, Any]							= 'Update TF weights' if self._new_weights else 'Add TF weights'
         if self._push:
              repo.git_add(auto_lfs_track=UpperCAmelCase_)
              repo.git_commit(UpperCAmelCase_)
              repo.git_push(blocking=UpperCAmelCase_)  # 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...')
              a					:							List[str]							= (
                  '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(UpperCAmelCase_):
                   a					:							Union[str, Any]							= [
                       CommitOperationAdd(path_in_repo=UpperCAmelCase_							,							path_or_fileobj=UpperCAmelCase_)
                   ]
                   for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5'):
                        operations += [
                            CommitOperationAdd(path_in_repo=os.path.basename(UpperCAmelCase_)							,							path_or_fileobj=UpperCAmelCase_)
                        ]
              else:
                   a					:							Dict							= [CommitOperationAdd(path_in_repo=UpperCAmelCase_							,							path_or_fileobj=UpperCAmelCase_)]

              a					:							Optional[int]							= create_commit(
                  repo_id=self._model_name							,							operations=UpperCAmelCase_							,							commit_message=UpperCAmelCase_							,							commit_description=UpperCAmelCase_							,							repo_type='model'							,							create_pr=UpperCAmelCase_							,							).pr_url
              self._logger.warning(f"""PR open in {hub_pr_url}""")


'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase       :     Optional[Any]			=    logging.get_logger(__name__)

UpperCamelCase       :     Union[str, Any]			=    {
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    int				=			"tvlt"





    def __init__(					self     :						List[str]							,							UpperCAmelCase_     :						Any=2_2_4							,							UpperCAmelCase_     :						Optional[Any]=2_0_4_8							,							UpperCAmelCase_     :						Dict=1_2_8							,							UpperCAmelCase_     :						str=[1_6, 1_6]							,							UpperCAmelCase_     :						int=[1_6, 1_6]							,							UpperCAmelCase_     :						int=3							,							UpperCAmelCase_     :						Optional[int]=1							,							UpperCAmelCase_     :						List[str]=8							,							UpperCAmelCase_     :						List[Any]=7_6_8							,							UpperCAmelCase_     :						int=1_2							,							UpperCAmelCase_     :						List[Any]=1_2							,							UpperCAmelCase_     :						Optional[Any]=3_0_7_2							,							UpperCAmelCase_     :						List[str]="gelu"							,							UpperCAmelCase_     :						Tuple=0.0							,							UpperCAmelCase_     :						Optional[Any]=0.0							,							UpperCAmelCase_     :						Tuple=0.02							,							UpperCAmelCase_     :						Any=1e-6							,							UpperCAmelCase_     :						Dict=True							,							UpperCAmelCase_     :						Union[str, Any]=False							,							UpperCAmelCase_     :						Tuple=1_6							,							UpperCAmelCase_     :						str=5_1_2							,							UpperCAmelCase_     :						List[str]=8							,							UpperCAmelCase_     :						Union[str, Any]=2_0_4_8							,							UpperCAmelCase_     :						Tuple=0.75							,							UpperCAmelCase_     :						Optional[Any]=0.15							,							UpperCAmelCase_     :						Union[str, Any]="frame-level"							,							UpperCAmelCase_     :						List[str]=True							,							UpperCAmelCase_     :						Any=True							,							UpperCAmelCase_     :						Optional[int]="classification"							,							**UpperCAmelCase_     :						Any							,							):


         """simple docstring"""







         super().__init__(**UpperCAmelCase_)

         if audio_mask_type not in ("frame-level", "patch_level"):
              raise ValueError(
                  'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
                  f"""got {audio_mask_type}""")

         a					:							str							= image_size
         a					:							List[Any]							= spectrogram_length
         a					:							Union[str, Any]							= frequency_length
         a					:							str							= image_patch_size
         a					:							Dict							= audio_patch_size
         a					:							Dict							= num_image_channels
         a					:							Tuple							= num_audio_channels
         a					:							Optional[int]							= num_frames

         a					:							str							= hidden_size
         a					:							List[Any]							= num_hidden_layers
         a					:							Optional[Any]							= num_attention_heads
         a					:							Any							= intermediate_size
         a					:							List[Any]							= hidden_act
         a					:							int							= hidden_dropout_prob
         a					:							Optional[Any]							= attention_probs_dropout_prob
         a					:							Optional[Any]							= initializer_range
         a					:							List[str]							= layer_norm_eps
         a					:							List[Any]							= qkv_bias
         a					:							Dict							= use_mean_pooling

         a					:							List[Any]							= decoder_num_attention_heads
         a					:							Any							= decoder_hidden_size
         a					:							int							= decoder_num_hidden_layers
         a					:							Tuple							= decoder_intermediate_size
         a					:							Tuple							= pixel_mask_ratio
         a					:							List[Any]							= audio_mask_ratio
         a					:							Optional[Any]							= audio_mask_type

         a					:							Any							= task_matching
         a					:							Optional[Any]							= task_mae
         a					:							List[str]							= loss_type

