



"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.xglm.modeling_tf_xglm import (
     TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFXGLMForCausalLM,
     TFXGLMModel,
 )






@require_tf
class       UpperCAmelCase_    :
      __SCREAMING_SNAKE_CASE		:  str						=					XGLMConfig
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					{}
      __SCREAMING_SNAKE_CASE		:  List[Any]						=					'gelu'



      def __init__(	self	:      int	,      A	:      int	,      A	:      Any=1_4	,      A	:      List[Any]=7	,      A	:      int=True	,      A	:      Optional[Any]=True	,      A	:      List[str]=True	,      A	:      List[Any]=9_9	,      A	:      Dict=3_2	,      A	:      Tuple=2	,      A	:      str=4	,      A	:      Dict=3_7	,      A	:      Optional[Any]="gelu"	,      A	:      Union[str, Any]=0.1	,      A	:      Tuple=0.1	,      A	:      Any=5_1_2	,      A	:      str=0.02	,      ):
           _UpperCAmelCase     :      Dict														=       parent
           _UpperCAmelCase     :      Union[str, Any]														=       batch_size
           _UpperCAmelCase     :      Any														=       seq_length
           _UpperCAmelCase     :      Optional[int]														=       is_training
           _UpperCAmelCase     :      Dict														=       use_input_mask
           _UpperCAmelCase     :      Optional[Any]														=       use_labels
           _UpperCAmelCase     :      List[Any]														=       vocab_size
           _UpperCAmelCase     :      Any														=       d_model
           _UpperCAmelCase     :      List[str]														=       num_hidden_layers
           _UpperCAmelCase     :      Tuple														=       num_attention_heads
           _UpperCAmelCase     :      Any														=       ffn_dim
           _UpperCAmelCase     :      str														=       activation_function
           _UpperCAmelCase     :      Dict														=       activation_dropout
           _UpperCAmelCase     :      Optional[Any]														=       attention_dropout
           _UpperCAmelCase     :      List[str]														=       max_position_embeddings
           _UpperCAmelCase     :      List[Any]														=       initializer_range
           _UpperCAmelCase     :      List[Any]														=       None
           _UpperCAmelCase     :      List[str]														=       0
           _UpperCAmelCase     :      Union[str, Any]														=       2
           _UpperCAmelCase     :      Tuple														=       1



      def 		snake_case_   (	self	:      Union[str, Any]		):
           return XGLMConfig.from_pretrained("facebook/xglm-564M"		)



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      List[str]														=       tf.clip_by_value(
               ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size		)	,      clip_value_min=0	,      clip_value_max=3		)

           _UpperCAmelCase     :      str														=       None
           if self.use_input_mask:
                _UpperCAmelCase     :      Union[str, Any]														=       random_attention_mask([self.batch_size, self.seq_length]		)

           _UpperCAmelCase     :      Optional[Any]														=       self.get_config()

           _UpperCAmelCase     :      Dict														=       floats_tensor([self.num_hidden_layers, self.num_attention_heads]	,      2		)

           return (
               config,
               input_ids,
               input_mask,
               head_mask,
           )



      def 		snake_case_   (	self	:      str		):
           return XGLMConfig(
               vocab_size=self.vocab_size	,      d_model=self.hidden_size	,      num_layers=self.num_hidden_layers	,      attention_heads=self.num_attention_heads	,      ffn_dim=self.ffn_dim	,      activation_function=self.activation_function	,      activation_dropout=self.activation_dropout	,      attention_dropout=self.attention_dropout	,      max_position_embeddings=self.max_position_embeddings	,      initializer_range=self.initializer_range	,      use_cache=A	,      bos_token_id=self.bos_token_id	,      eos_token_id=self.eos_token_id	,      pad_token_id=self.pad_token_id	,      return_dict=A	,      )



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       self.prepare_config_and_inputs()

           (
               (
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			
           )     :      List[str]														=       config_and_inputs

           _UpperCAmelCase     :      Optional[Any]														=       {
               "input_ids": input_ids,
               "head_mask": head_mask,
           }

           return config, inputs_dict







@require_tf
class       UpperCAmelCase_    (							_UpperCamelCase			,     _UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  List[str]						=					(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
      __SCREAMING_SNAKE_CASE		:  int						=					(TFXGLMForCausalLM,) if is_tf_available() else ()
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					(
          {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
      )
      __SCREAMING_SNAKE_CASE		:  int						=					False
      __SCREAMING_SNAKE_CASE		:  List[str]						=					False
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					False



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[str]														=       TFXGLMModelTester(self		)
           _UpperCAmelCase     :      List[Any]														=       ConfigTester(self	,      config_class=A	,      n_embd=3_7		)



      def 		snake_case_   (	self	:      str		):
           self.config_tester.run_common_tests()



      @slow
      def 		snake_case_   (	self	:      List[str]		):
           for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase     :      List[Any]														=       TFXGLMModel.from_pretrained(A		)
                self.assertIsNotNone(A		)



      @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."		)
      def 		snake_case_   (	self	:      Dict		):
           super().test_resize_token_embeddings()







@require_tf
class       UpperCAmelCase_    (							unittest.TestCase		):



      @slow
      def 		snake_case_   (	self	:      Any	,      A	:      str=True		):
           _UpperCAmelCase     :      Tuple														=       TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
           _UpperCAmelCase     :      List[str]														=       tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]	,      dtype=tf.intaa		)  # The dog
           # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
           # fmt: off
           _UpperCAmelCase     :      Optional[int]														=       [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
           # fmt: on
           _UpperCAmelCase     :      str														=       model.generate(A	,      do_sample=A	,      num_beams=1		)
           if verify_outputs:
                self.assertListEqual(output_ids[0].numpy().tolist()	,      A		)



      @slow
      def 		snake_case_   (	self	:      Optional[int]		):
           _UpperCAmelCase     :      Optional[int]														=       XGLMTokenizer.from_pretrained("facebook/xglm-564M"		)
           _UpperCAmelCase     :      Union[str, Any]														=       TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)

           tf.random.set_seed(0		)
           _UpperCAmelCase     :      str														=       tokenizer("Today is a nice day and"	,      return_tensors="tf"		)
           _UpperCAmelCase     :      Dict														=       tokenized.input_ids
           # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
           with tf.device(":/CPU:0"		):
                _UpperCAmelCase     :      Optional[Any]														=       model.generate(A	,      do_sample=A	,      seed=[7, 0]		)
           _UpperCAmelCase     :      Dict														=       tokenizer.decode(output_ids[0]	,      skip_special_tokens=A		)

           _UpperCAmelCase     :      int														=       (
               "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
           )
           self.assertEqual(A	,      A		)



      @slow
      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Optional[Any]														=       TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
           _UpperCAmelCase     :      List[Any]														=       XGLMTokenizer.from_pretrained("facebook/xglm-564M"		)

           _UpperCAmelCase     :      Optional[int]														=       "left"

           # use different length sentences to test batching
           _UpperCAmelCase     :      int														=       [
               "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
               "left-padding, such as in batched generation. The output for the sequence below should be the same "
               "regardless of whether left padding is applied or not. When",
               "Hello, my dog is a little",
           ]

           _UpperCAmelCase     :      List[Any]														=       tokenizer(A	,      return_tensors="tf"	,      padding=A		)
           _UpperCAmelCase     :      Union[str, Any]														=       inputs["input_ids"]

           _UpperCAmelCase     :      Dict														=       model.generate(input_ids=A	,      attention_mask=inputs["attention_mask"]	,      max_new_tokens=1_2		)

           _UpperCAmelCase     :      Dict														=       tokenizer(sentences[0]	,      return_tensors="tf"		).input_ids
           _UpperCAmelCase     :      Union[str, Any]														=       model.generate(input_ids=A	,      max_new_tokens=1_2		)

           _UpperCAmelCase     :      Optional[Any]														=       tokenizer(sentences[1]	,      return_tensors="tf"		).input_ids
           _UpperCAmelCase     :      Tuple														=       model.generate(input_ids=A	,      max_new_tokens=1_2		)

           _UpperCAmelCase     :      List[str]														=       tokenizer.batch_decode(A	,      skip_special_tokens=A		)
           _UpperCAmelCase     :      int														=       tokenizer.decode(output_non_padded[0]	,      skip_special_tokens=A		)
           _UpperCAmelCase     :      Any														=       tokenizer.decode(output_padded[0]	,      skip_special_tokens=A		)

           _UpperCAmelCase     :      List[str]														=       [
               "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
               "left-padding, such as in batched generation. The output for the sequence below should be the same "
               "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
               "a single",
               "Hello, my dog is a little bit of a shy one, but he is very friendly",
           ]
           self.assertListEqual(A	,      A		)
           self.assertListEqual(A	,      [non_padded_sentence, padded_sentence]		)





"""simple docstring"""




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE__							)
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     :      Optional[Any]														=       reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     :      Optional[int]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      int														=       True
          # hparam_utils.py hparams
          _UpperCAmelCase     :      Optional[int]														=       0.664_694
          _UpperCAmelCase     :      Any														=       0.207_951
          _UpperCAmelCase     :      Any														=       0.121_194
          _UpperCAmelCase     :      List[Any]														=       True
          _UpperCAmelCase     :      Dict														=       True
          _UpperCAmelCase     :      Tuple														=       False
          _UpperCAmelCase     :      List[Any]														=       0.0_352_513

          _UpperCAmelCase     :      Union[str, Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      Optional[int]														=       False
          # hparam_utils.py hparams
          _UpperCAmelCase     :      List[Any]														=       36.4_519
          _UpperCAmelCase     :      Union[str, Any]														=       0.903_421
          _UpperCAmelCase     :      List[Any]														=       222.088
          _UpperCAmelCase     :      Optional[Any]														=       True
          _UpperCAmelCase     :      str														=       True
          _UpperCAmelCase     :      Union[str, Any]														=       True
          _UpperCAmelCase     :      str														=       0.763_141

          _UpperCAmelCase     :      Optional[Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "TABFACT":
          _UpperCAmelCase     :      int														=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE__							)
     elif task == "MLM":
          _UpperCAmelCase     :      List[str]														=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     :      Optional[Any]														=       TapasModel(config=SCREAMING_SNAKE_CASE__							)
     else:
          raise ValueError(f'Task {task} not supported.'							)

     print(f'Building PyTorch model from configuration: {config}'							)
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'							)
     _UpperCAmelCase     :      Optional[int]														=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"		,    model_max_length=512							)
     tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)

     print("Used relative position embeddings:"		,    model.config.reset_position_index_per_cell							)


if __name__ == "__main__":
 _lowerCAmelCase							:   Union[str, Any]				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
 )
 parser.add_argument(
     "--reset_position_index_per_cell",
     default=False,
     action="store_true",
     help="Whether to use relative position embeddings or not. Defaults to True.",
 )
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--tapas_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained TAPAS model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 _lowerCAmelCase							:   Tuple				=				parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )
