





import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
      raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
__UpperCAmelCase			:  str										=				logging.get_logger(__name__)

__UpperCAmelCase			:  int										=				"Hello world! cécé herlolip"







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       List[Any]:
  __snake_case    ,  __snake_case:		Optional[Any]					    =		os.path.split(SCREAMING_SNAKE_CASE__)
  __snake_case:		int					    =		DataaVecTextModel.from_pretrained(
      SCREAMING_SNAKE_CASE__   ,   checkpoint_file=SCREAMING_SNAKE_CASE__)
  dataavec.eval()  # disable dropout
  __snake_case:		Any					    =		dataavec.models[0]
  __snake_case:		Any					    =		dataavec_model.encoder.sentence_encoder
  __snake_case:		List[Any]					    =		DataaVecTextConfig(
      vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings   ,   hidden_size=dataavec_model.args.encoder_embed_dim   ,   num_hidden_layers=dataavec_model.args.encoder_layers   ,   num_attention_heads=dataavec_model.args.encoder_attention_heads   ,   intermediate_size=dataavec_model.args.encoder_ffn_embed_dim   ,   max_position_embeddings=514   ,   type_vocab_size=1   ,   layer_norm_eps=1e-5   ,   )
  if classification_head:
    __snake_case:		Union[str, Any]					    =		dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
  print("""Our BERT config:"""   ,   SCREAMING_SNAKE_CASE__)

  __snake_case:		Tuple					    =		DataaVecTextForSequenceClassification(SCREAMING_SNAKE_CASE__) if classification_head else DataaVecTextForMaskedLM(SCREAMING_SNAKE_CASE__)
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  __snake_case:		Any					    =		dataavec_sent_encoder.embed_tokens.weight
  __snake_case:		Any					    =		dataavec_sent_encoder.embed_positions.weight
  __snake_case:		Dict					    =		torch.zeros_like(
      model.dataavec_text.embeddings.token_type_embeddings.weight)  # just zero them out b/c data2vec doesn't use them.
  __snake_case:		Dict					    =		dataavec_sent_encoder.layernorm_embedding.weight
  __snake_case:		Tuple					    =		dataavec_sent_encoder.layernorm_embedding.bias

  for i in range(config.num_hidden_layers):
    # Encoder: start of layer
    __snake_case:		BertLayer					    =		model.dataavec_text.encoder.layer[i]
    __snake_case:		TransformerSentenceEncoderLayer					    =		dataavec_sent_encoder.layers[i]

    # self attention
    __snake_case:		BertSelfAttention					    =		layer.attention.self
    assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
        (config.hidden_size, config.hidden_size)), (
        "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
        F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
    )
    assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
        (config.hidden_size, config.hidden_size)), (
        "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
        F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
    )
    assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
        (config.hidden_size, config.hidden_size)), (
        "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
        F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
    )

    __snake_case:		str					    =		dataavec_layer.self_attn.q_proj.weight
    __snake_case:		Dict					    =		dataavec_layer.self_attn.q_proj.bias
    __snake_case:		Union[str, Any]					    =		dataavec_layer.self_attn.k_proj.weight
    __snake_case:		Optional[Any]					    =		dataavec_layer.self_attn.k_proj.bias
    __snake_case:		List[Any]					    =		dataavec_layer.self_attn.v_proj.weight
    __snake_case:		List[Any]					    =		dataavec_layer.self_attn.v_proj.bias

    # self-attention output
    __snake_case:		BertSelfOutput					    =		layer.attention.output
    assert (
        self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
    ), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
    __snake_case:		Union[str, Any]					    =		dataavec_layer.self_attn.out_proj.weight
    __snake_case:		str					    =		dataavec_layer.self_attn.out_proj.bias
    __snake_case:		Optional[Any]					    =		dataavec_layer.self_attn_layer_norm.weight
    __snake_case:		str					    =		dataavec_layer.self_attn_layer_norm.bias

    # intermediate
    __snake_case:		BertIntermediate					    =		layer.intermediate
    assert (
        intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
    ), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
    __snake_case:		Tuple					    =		dataavec_layer.fca.weight
    __snake_case:		Tuple					    =		dataavec_layer.fca.bias

    # output
    __snake_case:		BertOutput					    =		layer.output
    assert (
        bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
    ), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
    __snake_case:		List[Any]					    =		dataavec_layer.fca.weight
    __snake_case:		Any					    =		dataavec_layer.fca.bias
    __snake_case:		int					    =		dataavec_layer.final_layer_norm.weight
    __snake_case:		Dict					    =		dataavec_layer.final_layer_norm.bias
    # end of layer

  if classification_head:
    __snake_case:		Any					    =		dataavec.model.classification_heads["""mnli"""].dense.weight
    __snake_case:		Tuple					    =		dataavec.model.classification_heads["""mnli"""].dense.bias
    __snake_case:		List[str]					    =		dataavec.model.classification_heads["""mnli"""].out_proj.weight
    __snake_case:		List[str]					    =		dataavec.model.classification_heads["""mnli"""].out_proj.bias
  else:
    # LM Head
    __snake_case:		int					    =		dataavec_model.encoder.lm_head.dense.weight
    __snake_case:		Union[str, Any]					    =		dataavec_model.encoder.lm_head.dense.bias
    __snake_case:		Tuple					    =		dataavec_model.encoder.lm_head.layer_norm.weight
    __snake_case:		List[Any]					    =		dataavec_model.encoder.lm_head.layer_norm.bias
    __snake_case:		int					    =		dataavec_model.encoder.lm_head.weight
    __snake_case:		Dict					    =		dataavec_model.encoder.lm_head.bias

  # Let's check that we get the same results.
  __snake_case:		torch.Tensor					    =		dataavec.encode(SCREAMING_SNAKE_CASE__).unsqueeze(0)  # batch of size 1

  __snake_case:		int					    =		model(SCREAMING_SNAKE_CASE__)[0]
  if classification_head:
    __snake_case:		Optional[Any]					    =		dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(SCREAMING_SNAKE_CASE__))
  else:
    __snake_case:		List[Any]					    =		dataavec_model(SCREAMING_SNAKE_CASE__)[0]
  print(our_output.shape   ,   their_output.shape)
  __snake_case:		Dict					    =		torch.max(torch.abs(our_output - their_output)).item()
  print(F'''max_absolute_diff = {max_absolute_diff}''')  # ~ 1e-7
  __snake_case:		int					    =		torch.allclose(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   atol=1e-3)
  print("""Do both models output the same tensors?"""   ,   """🔥""" if success else """💩""")
  if not success:
    raise Exception("""Something went wRoNg""")

  pathlib.Path(SCREAMING_SNAKE_CASE__).mkdir(parents=SCREAMING_SNAKE_CASE__   ,   exist_ok=SCREAMING_SNAKE_CASE__)
  print(F'''Saving model to {pytorch_dump_folder_path}''')
  model.save_pretrained(SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      __UpperCAmelCase			:  Dict										=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--classification_head", action="store_true", help="Whether to convert a final classification head."
      )
      __UpperCAmelCase			:  List[str]										=				parser.parse_args()
      convert_dataavec_checkpoint_to_pytorch(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )













import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          FunnelBaseModel,
          FunnelForMaskedLM,
          FunnelForMultipleChoice,
          FunnelForPreTraining,
          FunnelForQuestionAnswering,
          FunnelForSequenceClassification,
          FunnelForTokenClassification,
          FunnelModel,
      )







class        __snake_case			:





       '''simple docstring'''







       def __init__(    self					:       str				,							A					:       int				,							A					:       List[Any]=13				,							A					:       Dict=7				,							A					:       Optional[int]=True				,							A					:       Dict=True				,							A					:       str=True				,							A					:       Union[str, Any]=True				,							A					:       Union[str, Any]=99				,							A					:       Dict=[1, 1, 2]				,							A					:       List[Any]=1				,							A					:       List[str]=32				,							A					:       Dict=4				,							A					:       Tuple=8				,							A					:       List[Any]=37				,							A					:       Dict="gelu_new"				,							A					:       Dict=0.1				,							A					:       List[Any]=0.1				,							A					:       Optional[int]=0.0				,							A					:       Tuple=512				,							A					:       Tuple=3				,							A					:       List[str]=0.02				,							A					:       str=3				,							A					:       Optional[Any]=4				,							A					:       str=None				,							A					:       Union[str, Any]=False				,							):
         __snake_case:		int					    =		parent
         __snake_case:		Optional[Any]					    =		batch_size
         __snake_case:		Optional[Any]					    =		seq_length
         __snake_case:		List[Any]					    =		is_training
         __snake_case:		List[str]					    =		use_input_mask
         __snake_case:		Any					    =		use_token_type_ids
         __snake_case:		Union[str, Any]					    =		use_labels
         __snake_case:		Any					    =		vocab_size
         __snake_case:		int					    =		block_sizes
         __snake_case:		Optional[int]					    =		num_decoder_layers
         __snake_case:		Union[str, Any]					    =		d_model
         __snake_case:		List[str]					    =		n_head
         __snake_case:		Optional[Any]					    =		d_head
         __snake_case:		str					    =		d_inner
         __snake_case:		Optional[Any]					    =		hidden_act
         __snake_case:		List[Any]					    =		hidden_dropout
         __snake_case:		List[Any]					    =		attention_dropout
         __snake_case:		Tuple					    =		activation_dropout
         __snake_case:		Optional[int]					    =		max_position_embeddings
         __snake_case:		Optional[int]					    =		type_vocab_size
         __snake_case:		Any					    =		2
         __snake_case:		Dict					    =		num_labels
         __snake_case:		List[Any]					    =		num_choices
         __snake_case:		Any					    =		scope
         __snake_case:		Optional[Any]					    =		initializer_std

         # Used in the tests to check the size of the first attention layer
         __snake_case:		Union[str, Any]					    =		n_head
         # Used in the tests to check the size of the first hidden state
         __snake_case:		Union[str, Any]					    =		self.d_model
         # Used in the tests to check the number of output hidden states/attentions
         __snake_case:		Optional[int]					    =		sum(self.block_sizes						) + (0 if base else self.num_decoder_layers)
         # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
         # the last hidden state of the first block (which is the first hidden state of the decoder).
         if not base:
           __snake_case:		Any					    =		self.num_hidden_layers + 2




       def 				UpperCAmelCase__					(    self					:       Any						):
         __snake_case:		Tuple					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size						)

         __snake_case:		Optional[int]					    =		None
         if self.use_input_mask:
           __snake_case:		Optional[Any]					    =		ids_tensor([self.batch_size, self.seq_length]				,							vocab_size=2						)

         __snake_case:		Dict					    =		None
         if self.use_token_type_ids:
           __snake_case:		List[str]					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.type_vocab_size						)

         __snake_case:		Optional[Any]					    =		None
         __snake_case:		Optional[int]					    =		None
         __snake_case:		Any					    =		None
         if self.use_labels:
           __snake_case:		List[Any]					    =		ids_tensor([self.batch_size]				,							self.type_sequence_label_size						)
           __snake_case:		Any					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.num_labels						)
           __snake_case:		Union[str, Any]					    =		ids_tensor([self.batch_size]				,							self.num_choices						)
           __snake_case:		Tuple					    =		ids_tensor([self.batch_size, self.seq_length]				,							1						)

         __snake_case:		Dict					    =		self.get_config()

         return (
             config,
             input_ids,
             token_type_ids,
             input_mask,
             sequence_labels,
             token_labels,
             choice_labels,
             fake_token_labels,
         )




       def 				UpperCAmelCase__					(    self					:       Dict						):
         return FunnelConfig(
             vocab_size=self.vocab_size				,							block_sizes=self.block_sizes				,							num_decoder_layers=self.num_decoder_layers				,							d_model=self.d_model				,							n_head=self.n_head				,							d_head=self.d_head				,							d_inner=self.d_inner				,							hidden_act=self.hidden_act				,							hidden_dropout=self.hidden_dropout				,							attention_dropout=self.attention_dropout				,							activation_dropout=self.activation_dropout				,							max_position_embeddings=self.max_position_embeddings				,							type_vocab_size=self.type_vocab_size				,							initializer_std=self.initializer_std				,							)




       def 				UpperCAmelCase__					(    self					:       List[Any]				,							A					:       Tuple				,							A					:       Any				,							A					:       int				,							A					:       Optional[Any]				,							A					:       List[str]				,							A					:       Optional[int]				,							A					:       Tuple				,							A					:       Optional[Any]				,							):
         __snake_case:		int					    =		FunnelModel(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Union[str, Any]					    =		model(A				,							attention_mask=A				,							token_type_ids=A						)
         __snake_case:		Dict					    =		model(A				,							token_type_ids=A						)
         __snake_case:		Optional[int]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.d_model)						)

         __snake_case:		Any					    =		False
         __snake_case:		List[Any]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.d_model)						)

         __snake_case:		List[str]					    =		False
         __snake_case:		Optional[int]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.d_model)						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]				,							A					:       Union[str, Any]				,							A					:       str				,							A					:       Any				,							A					:       str				,							A					:       Any				,							A					:       str				,							A					:       List[str]				,							A					:       Dict				,							):
         __snake_case:		Dict					    =		FunnelBaseModel(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		List[str]					    =		model(A				,							attention_mask=A				,							token_type_ids=A						)
         __snake_case:		Union[str, Any]					    =		model(A				,							token_type_ids=A						)
         __snake_case:		List[Any]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, 2, self.d_model)						)

         __snake_case:		Optional[Any]					    =		False
         __snake_case:		Tuple					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, 3, self.d_model)						)

         __snake_case:		Union[str, Any]					    =		False
         __snake_case:		List[Any]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, 2, self.d_model)						)




       def 				UpperCAmelCase__					(    self					:       Tuple				,							A					:       Union[str, Any]				,							A					:       Optional[int]				,							A					:       Optional[int]				,							A					:       Any				,							A					:       str				,							A					:       Optional[int]				,							A					:       Dict				,							A					:       str				,							):
         __snake_case:		Optional[int]					    =		self.num_labels
         __snake_case:		Optional[Any]					    =		FunnelForPreTraining(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Dict					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length)						)




       def 				UpperCAmelCase__					(    self					:       List[str]				,							A					:       Optional[int]				,							A					:       List[str]				,							A					:       Tuple				,							A					:       Any				,							A					:       str				,							A					:       Optional[Any]				,							A					:       Optional[Any]				,							A					:       Union[str, Any]				,							):
         __snake_case:		List[str]					    =		FunnelForMaskedLM(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Any					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.vocab_size)						)




       def 				UpperCAmelCase__					(    self					:       Dict				,							A					:       str				,							A					:       Dict				,							A					:       List[str]				,							A					:       Any				,							A					:       Optional[Any]				,							A					:       Optional[Any]				,							A					:       Optional[Any]				,							A					:       Any				,							):
         __snake_case:		Dict					    =		self.num_labels
         __snake_case:		Dict					    =		FunnelForSequenceClassification(A						)
         model.to(A						)
         model.eval()
         __snake_case:		str					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)						)




       def 				UpperCAmelCase__					(    self					:       List[str]				,							A					:       Optional[int]				,							A					:       str				,							A					:       Optional[int]				,							A					:       str				,							A					:       Tuple				,							A					:       int				,							A					:       int				,							A					:       Dict				,							):
         __snake_case:		List[Any]					    =		self.num_choices
         __snake_case:		Optional[Any]					    =		FunnelForMultipleChoice(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		int					    =		input_ids.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		Tuple					    =		token_type_ids.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		List[str]					    =		input_mask.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		Tuple					    =		model(
             A				,							attention_mask=A				,							token_type_ids=A				,							labels=A				,							)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_choices)						)




       def 				UpperCAmelCase__					(    self					:       List[Any]				,							A					:       Optional[int]				,							A					:       str				,							A					:       Union[str, Any]				,							A					:       Optional[int]				,							A					:       Union[str, Any]				,							A					:       Optional[int]				,							A					:       Union[str, Any]				,							A					:       Union[str, Any]				,							):
         __snake_case:		Dict					    =		self.num_labels
         __snake_case:		List[Any]					    =		FunnelForTokenClassification(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Tuple					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.num_labels)						)




       def 				UpperCAmelCase__					(    self					:       Dict				,							A					:       Optional[Any]				,							A					:       int				,							A					:       Dict				,							A					:       Optional[int]				,							A					:       Tuple				,							A					:       str				,							A					:       List[Any]				,							A					:       Optional[Any]				,							):
         __snake_case:		List[str]					    =		FunnelForQuestionAnswering(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		str					    =		model(
             A				,							attention_mask=A				,							token_type_ids=A				,							start_positions=A				,							end_positions=A				,							)
         self.parent.assertEqual(result.start_logits.shape				,							(self.batch_size, self.seq_length)						)
         self.parent.assertEqual(result.end_logits.shape				,							(self.batch_size, self.seq_length)						)






       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		Optional[int]					    =		self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  
         ):		Dict					    =		config_and_inputs
         __snake_case:		Union[str, Any]					    =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict







@require_torch
class        __snake_case			(				__lowerCamelCase  ,     __lowerCamelCase  ,     unittest.TestCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	(
           (
               FunnelModel,
               FunnelForMaskedLM,
               FunnelForPreTraining,
               FunnelForQuestionAnswering,
               FunnelForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__ 						=	(
           {
               """feature-extraction""": (FunnelBaseModel, FunnelModel),
               """fill-mask""": FunnelForMaskedLM,
               """question-answering""": FunnelForQuestionAnswering,
               """text-classification""": FunnelForSequenceClassification,
               """token-classification""": FunnelForTokenClassification,
               """zero-shot""": FunnelForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )




       def 				UpperCAmelCase__					(    self					:       int				,							A					:       Optional[Any]				,							A					:       List[str]				,							A					:       int=False						):
         __snake_case:		str					    =		super()._prepare_for_class(A				,							A				,							return_labels=A						)

         if return_labels:
           if model_class in get_values(A						):
             __snake_case:		str					    =		torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.seq_length)				,							dtype=torch.long				,							device=A						)
         return inputs_dict




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         __snake_case:		Union[str, Any]					    =		FunnelModelTester(self						)
         __snake_case:		List[str]					    =		ConfigTester(self				,							config_class=A						)




       def 				UpperCAmelCase__					(    self					:       Dict						):
         self.config_tester.run_common_tests()




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		int					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case:		Optional[int]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*A						)




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		Optional[int]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case:		Any					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*A						)




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		str					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*A						)






       def 				UpperCAmelCase__					(    self					:       Union[str, Any]				,							A					:       List[Any]						):
         if hasattr(A				,							"""weight"""						) and module.weight is not None:
           module.weight.data.fill_(3						)
         if hasattr(A				,							"""bias"""						) and module.bias is not None:
           module.bias.data.fill_(3						)

         for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
           if hasattr(A				,							A						) and getattr(A				,							A						) is not None:
             __snake_case:		str					    =		getattr(A				,							A						)
             weight.data.fill_(3						)







@require_torch
class        __snake_case			(				__lowerCamelCase  ,     unittest.TestCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	(
           (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
       )




       def 				UpperCAmelCase__					(    self					:       Any						):
         __snake_case:		Optional[int]					    =		FunnelModelTester(self				,							base=A						)
         __snake_case:		int					    =		ConfigTester(self				,							config_class=A						)




       def 				UpperCAmelCase__					(    self					:       Dict						):
         self.config_tester.run_common_tests()




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case:		List[str]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_base_model(*A						)




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		Dict					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		List[str]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case    ,  __snake_case:		Optional[Any]					    =		self.model_tester.prepare_config_and_inputs_for_common()
         __snake_case:		List[str]					    =		True

         for model_class in self.all_model_classes:
           if model_class.__name__ == "FunnelBaseModel":
             continue
           __snake_case:		List[Any]					    =		model_class(A						)
           model.to(A						)
           model.train()
           __snake_case:		Optional[int]					    =		self._prepare_for_class(A				,							A				,							return_labels=A						)
           __snake_case:		Optional[int]					    =		model(**A						).loss
           loss.backward()






       def 				UpperCAmelCase__					(    self					:       Union[str, Any]				,							A					:       Optional[Any]						):
         if hasattr(A				,							"""weight"""						) and module.weight is not None:
           module.weight.data.fill_(3						)
         if hasattr(A				,							"""bias"""						) and module.bias is not None:
           module.bias.data.fill_(3						)

         for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
           if hasattr(A				,							A						) and getattr(A				,							A						) is not None:
             __snake_case:		int					    =		getattr(A				,							A						)
             weight.data.fill_(3						)







@require_torch
@require_sentencepiece
@require_tokenizers
class        __snake_case			(				unittest.TestCase     ):





       '''simple docstring'''







       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		List[str]					    =		13
         __snake_case:		List[Any]					    =		7
         __snake_case:		List[Any]					    =		torch.arange(0				,							batch_size * sequence_length						).long().reshape(A				,							A						)
         __snake_case:		Tuple					    =		[0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
         __snake_case:		List[str]					    =		torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]						)

         __snake_case:		Optional[int]					    =		FunnelModel.from_pretrained("""sgugger/funnel-random-tiny"""						)
         __snake_case:		List[Any]					    =		model(A				,							token_type_ids=A						)[0].abs()

         __snake_case:		str					    =		torch.tensor(2344.8352						)
         __snake_case:		Union[str, Any]					    =		torch.tensor(0.8052						)
         self.assertTrue(torch.allclose(output.sum()				,							A				,							atol=1E-4						)						)
         self.assertTrue(torch.allclose(output.mean()				,							A				,							atol=1E-4						)						)

         __snake_case:		str					    =		torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]						)
         __snake_case:		Tuple					    =		model(A				,							attention_mask=A				,							token_type_ids=A						)[0].abs()

         __snake_case:		List[str]					    =		torch.tensor(2343.8425						)
         __snake_case:		List[Any]					    =		torch.tensor(0.8049						)
         self.assertTrue(torch.allclose(output.sum()				,							A				,							atol=1E-4						)						)
         self.assertTrue(torch.allclose(output.mean()				,							A				,							atol=1E-4						)						)






       @slow
       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		Union[str, Any]					    =		FunnelTokenizer.from_pretrained("""huggingface/funnel-small"""						)
         __snake_case:		Tuple					    =		FunnelModel.from_pretrained("""huggingface/funnel-small"""						)
         __snake_case:		Tuple					    =		tokenizer("""Hello! I am the Funnel Transformer model."""				,							return_tensors="""pt"""						)
         __snake_case:		Union[str, Any]					    =		model(**A						)[0]

         __snake_case:		int					    =		torch.tensor(235.7246						)
         __snake_case:		Dict					    =		torch.tensor(0.0256						)
         self.assertTrue(torch.allclose(output.sum()				,							A				,							atol=1E-4						)						)
         self.assertTrue(torch.allclose(output.mean()				,							A				,							atol=1E-4						)						)






