





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


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


def 			lowerCamelCase_		(					lowerCAmelCase:		Namespace )->						Union[str, Any]:
  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 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  @staticmethod
  def UpperCamelCase_       (					UpperCamelCase					:    ArgumentParser							):


    '''simple docstring'''





    _snake_case      : 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 UpperCamelCase_       (					UpperCamelCase					:    Optional[int]					, UpperCamelCase					:    int							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		set(pt_outputs.keys()							)
    _snake_case      : str														=		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[Any]					, UpperCamelCase					:    List[Any]					, UpperCamelCase					:    List[Any]					, UpperCamelCase					:    Dict=""							):
      # 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							):
        _snake_case      : int														=		np.max(np.abs(pt_out.numpy() - tf_out.numpy()							)							)
        _snake_case      : List[str]														=		tensor_difference
      else:
        _snake_case      : Optional[int]														=		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							):
            _snake_case      : Dict														=		root_name + pt_item
            _snake_case      : Tuple														=		tf_out[pt_item]
            _snake_case      : Dict														=		pt_out[pt_item]
          else:
            _snake_case      : Dict														=		root_name + f"""[{i}]"""
            _snake_case      : int														=		tf_out[i]
          _snake_case      : Dict														=		_find_pt_tf_differences(UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)

      return differences

    return _find_pt_tf_differences(UpperCamelCase					, UpperCamelCase					, {}							)







  def __init__(					self					:    Optional[int]					, UpperCamelCase					:    str					, UpperCamelCase					:    str					, UpperCamelCase					:    float					, UpperCamelCase					:    bool					, UpperCamelCase					:    bool					, UpperCamelCase					:    bool					, UpperCamelCase					:    str					, UpperCamelCase					:    str					, *UpperCamelCase					:    int					, ):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    Union[str, Any]					, UpperCamelCase					:    Any					, UpperCamelCase					:    Optional[Any]					, UpperCamelCase					:    Optional[int]							):


    '''simple docstring'''





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

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

    _snake_case      : Dict														=		set(inspect.signature(pt_model.forward							).parameters.keys()							)
    _snake_case      : Optional[Any]														=		{}
    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:
      _snake_case      : Optional[int]														=		load_dataset('cifar10'					, 'plain_text'					, split='test'							)[:2]['img']
      processor_inputs.update({'images': sample_images}							)
    if "input_features" in model_forward_signature:
      _snake_case      : List[str]														=		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:
        _snake_case      : int														=		feature_extractor_signature['padding'].default
        if default_strategy is not False and default_strategy is not None:
          _snake_case      : Tuple														=		default_strategy
        else:
          _snake_case      : List[str]														=		True
      else:
        _snake_case      : Tuple														=		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}							)
    _snake_case      : Union[str, Any]														=		processor(**UpperCamelCase					, return_tensors='pt'							)
    _snake_case      : List[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
    ):
      _snake_case      : Union[str, Any]														=		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 UpperCamelCase_       (					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
    _snake_case      : Optional[Any]														=		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
    _snake_case      : Any														=		AutoConfig.from_pretrained(self._local_dir							)
    _snake_case      : Optional[Any]														=		config.architectures
    if self._override_model_class is not None:
      if self._override_model_class.startswith('TF'							):
        _snake_case      : Tuple														=		[self._override_model_class[2:]]
      else:
        _snake_case      : Tuple														=		[self._override_model_class]
      try:
        _snake_case      : int														=		getattr(import_module('transformers'							)					, architectures[0]							)
      except AttributeError:
        raise ValueError(f"""Model class {self._override_model_class} not found in transformers."""							)
      try:
        _snake_case      : 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
      _snake_case      : Any														=		getattr(import_module('transformers'							)					, 'AutoModel'							)
      _snake_case      : Any														=		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]}"""							)
      _snake_case      : str														=		getattr(import_module('transformers'							)					, architectures[0]							)
      try:
        _snake_case      : int														=		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
    _snake_case      : Dict														=		tf_class.from_config(UpperCamelCase							)
    _snake_case      : Tuple														=		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
    _snake_case      : Dict														=		pt_class.from_pretrained(self._local_dir							)
    pt_model.eval()

    _snake_case							,    _snake_case      : str														=		self.get_inputs(UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)

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

    _snake_case      : str														=		tf_class.from_pretrained(self._local_dir					, from_pt=UpperCamelCase							)
    _snake_case      : Dict														=		tf_from_pt_model(**UpperCamelCase					, output_hidden_states=UpperCamelCase					, training=UpperCamelCase							)

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

    _snake_case      : Any														=		tf_class.from_pretrained(self._local_dir							)
    _snake_case      : Union[str, Any]														=		tf_model(**UpperCamelCase					, output_hidden_states=UpperCamelCase							)

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

    _snake_case      : int														=		'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...'							)
      _snake_case      : Optional[Any]														=		(
          '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							):
        _snake_case      : int														=		[
            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:
        _snake_case      : List[Any]														=		[CommitOperationAdd(path_in_repo=UpperCamelCase					, path_or_fileobj=UpperCamelCase							)]

      _snake_case      : Dict														=		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}"""							)










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					:     Optional[int]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Optional[int]					  =  {
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
				lowerCAmelCase__			=   "squeezebert"



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						Tuple=3_0522						,		_UpperCAmelCase					:						str=768						,		_UpperCAmelCase					:						int=12						,		_UpperCAmelCase					:						Tuple=12						,		_UpperCAmelCase					:						Tuple=3072						,		_UpperCAmelCase					:						Union[str, Any]="gelu"						,		_UpperCAmelCase					:						Union[str, Any]=0.1						,		_UpperCAmelCase					:						List[Any]=0.1						,		_UpperCAmelCase					:						List[Any]=512						,		_UpperCAmelCase					:						Tuple=2						,		_UpperCAmelCase					:						Optional[Any]=0.02						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=768						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Optional[int]=4						,		_UpperCAmelCase					:						str=1						,		_UpperCAmelCase					:						Union[str, Any]=4						,		_UpperCAmelCase					:						List[str]=4						,		**_UpperCAmelCase					:						int						,		)     ->			Any:





								'''simple docstring'''




								super().__init__(pad_token_id=_UpperCAmelCase						,		**_UpperCAmelCase					)

								_lowerCAmelCase			:			Optional[Any]        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					hidden_size
								_lowerCAmelCase			:			Tuple        =					num_hidden_layers
								_lowerCAmelCase			:			int        =					num_attention_heads
								_lowerCAmelCase			:			List[Any]        =					hidden_act
								_lowerCAmelCase			:			List[Any]        =					intermediate_size
								_lowerCAmelCase			:			List[str]        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			str        =					max_position_embeddings
								_lowerCAmelCase			:			int        =					type_vocab_size
								_lowerCAmelCase			:			str        =					initializer_range
								_lowerCAmelCase			:			int        =					layer_norm_eps
								_lowerCAmelCase			:			int        =					embedding_size
								_lowerCAmelCase			:			Optional[int]        =					q_groups
								_lowerCAmelCase			:			List[Any]        =					k_groups
								_lowerCAmelCase			:			List[Any]        =					v_groups
								_lowerCAmelCase			:			Dict        =					post_attention_groups
								_lowerCAmelCase			:			Optional[Any]        =					intermediate_groups
								_lowerCAmelCase			:			str        =					output_groups






class 				__snake_case     (_a						):



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			Mapping[str, Mapping[int, str]]:





								'''simple docstring'''




								if self.task == "multiple-choice":
												_lowerCAmelCase			:			Optional[int]        =					{0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
												_lowerCAmelCase			:			int        =					{0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								        ("""token_type_ids""", dynamic_axis),
								    ]					)

