from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
     import tensorflow as tf

     from transformers.models.openai.modeling_tf_openai import (
         TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFOpenAIGPTDoubleHeadsModel,
         TFOpenAIGPTForSequenceClassification,
         TFOpenAIGPTLMHeadModel,
         TFOpenAIGPTModel,
     )







class        UpperCAmelCase_					:




    def __init__(       self	: Tuple   ,   lowerCAmelCase	: str   ,   )						->			Union[str, Any]:
      a__  		=  parent
      a__  		=  13
      a__  		=  7
      a__  		=  True
      a__  		=  True
      a__  		=  True
      a__  		=  True
      a__  		=  True
      a__  		=  99
      a__  		=  32
      a__  		=  2
      a__  		=  4
      a__  		=  37
      a__  		=  "gelu"
      a__  		=  0.1
      a__  		=  0.1
      a__  		=  512
      a__  		=  16
      a__  		=  2
      a__  		=  0.02
      a__  		=  3
      a__  		=  4
      a__  		=  None
      a__  		=  self.vocab_size - 1




    def     __lowerCamelCase			(       self	: Dict )						->			Tuple:
      a__  		=  ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size )

      a__  		=  None
      if self.use_input_mask:
        a__  		=  random_attention_mask([self.batch_size, self.seq_length] )

      a__  		=  None
      if self.use_token_type_ids:
        a__  		=  ids_tensor([self.batch_size, self.seq_length]   ,   self.type_vocab_size )

      a__  		=  None
      if self.use_mc_token_ids:
        a__  		=  ids_tensor([self.batch_size, self.num_choices]   ,   self.seq_length )

      a__  		=  None
      a__  		=  None
      a__  		=  None
      if self.use_labels:
        a__  		=  ids_tensor([self.batch_size]   ,   self.type_sequence_label_size )
        a__  		=  ids_tensor([self.batch_size, self.seq_length]   ,   self.num_labels )
        a__  		=  ids_tensor([self.batch_size]   ,   self.num_choices )

      a__  		=  OpenAIGPTConfig(
          vocab_size=self.vocab_size   ,   n_embd=self.hidden_size   ,   n_layer=self.num_hidden_layers   ,   n_head=self.num_attention_heads   ,   n_positions=self.max_position_embeddings   ,   pad_token_id=self.pad_token_id   ,   )

      a__  		=  ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,   2 )

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
          token_type_ids,
          mc_token_ids,
          sequence_labels,
          token_labels,
          choice_labels,
      )




    def     __lowerCamelCase			(       self	: Optional[int]   ,   lowerCAmelCase	: Union[str, Any]   ,   lowerCAmelCase	: Dict   ,   lowerCAmelCase	: Optional[int]   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: Dict   ,   *lowerCAmelCase	: Optional[Any] )						->			Tuple:
      a__  		=  TFOpenAIGPTModel(config=lowerCAmelCase )
      a__  		=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
      a__  		=  model(lowerCAmelCase )

      a__  		=  [input_ids, input_mask]
      a__  		=  model(lowerCAmelCase )

      a__  		=  model(lowerCAmelCase )

      self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size) )




    def     __lowerCamelCase			(       self	: Any   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: Tuple   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: Optional[int]   ,   *lowerCAmelCase	: str )						->			List[Any]:
      a__  		=  TFOpenAIGPTLMHeadModel(config=lowerCAmelCase )
      a__  		=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
      a__  		=  model(lowerCAmelCase )
      self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.vocab_size) )




    def     __lowerCamelCase			(       self	: Any   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: str   ,   lowerCAmelCase	: List[Any]   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: List[Any]   ,   lowerCAmelCase	: Optional[int]   ,   *lowerCAmelCase	: Optional[Any] )						->			int:
      a__  		=  TFOpenAIGPTDoubleHeadsModel(config=lowerCAmelCase )

      a__  		=  tf.tile(tf.expand_dims(lowerCAmelCase   ,   1 )   ,   (1, self.num_choices, 1) )
      a__  		=  tf.tile(tf.expand_dims(lowerCAmelCase   ,   1 )   ,   (1, self.num_choices, 1) )
      a__  		=  tf.tile(tf.expand_dims(lowerCAmelCase   ,   1 )   ,   (1, self.num_choices, 1) )

      a__  		=  {
          "input_ids": multiple_choice_inputs_ids,
          "mc_token_ids": mc_token_ids,
          "attention_mask": multiple_choice_input_mask,
          "token_type_ids": multiple_choice_token_type_ids,
      }
      a__  		=  model(lowerCAmelCase )
      self.parent.assertEqual(
          result.logits.shape   ,   (self.batch_size, self.num_choices, self.seq_length, self.vocab_size) )
      self.parent.assertEqual(result.mc_logits.shape   ,   (self.batch_size, self.num_choices) )




    def     __lowerCamelCase			(       self	: Any   ,   lowerCAmelCase	: Optional[int]   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: Tuple   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: str   ,   *lowerCAmelCase	: Tuple )						->			Optional[int]:
      a__  		=  self.num_labels
      a__  		=  ids_tensor([self.batch_size]   ,   self.type_sequence_label_size )
      a__  		=  {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
          "labels": sequence_labels,
      }
      a__  		=  TFOpenAIGPTForSequenceClassification(lowerCAmelCase )
      a__  		=  model(lowerCAmelCase )
      self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels) )




    def     __lowerCamelCase			(       self	: Any )						->			List[str]:
      a__  		=  self.prepare_config_and_inputs()

      (
          (
          a__
      )		,(
          a__
      )		,(
          a__
      )		,(
          a__
      )		,(
          a__
      )		,(
          a__
      )		,(
          a__
      )		,(
          a__
      )		,(
          a__
      )		,
      )  		=  config_and_inputs

      a__  		=  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict







@require_tf
class        UpperCAmelCase_					(				_a  ,  _a  ,  unittest.TestCase     ):
    UpperCAmelCase_      :							Union[str, Any]    			=				(
        (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
        if is_tf_available()
        else ()
    )
    UpperCAmelCase_      :							Any    			=				(
        (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
    UpperCAmelCase_      :							Union[str, Any]    			=				(
        {
            'feature-extraction': TFOpenAIGPTModel,
            'text-classification': TFOpenAIGPTForSequenceClassification,
            'text-generation': TFOpenAIGPTLMHeadModel,
            'zero-shot': TFOpenAIGPTForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    UpperCAmelCase_      :							Dict    			=				False
    UpperCAmelCase_      :							List[str]    			=				False




    def     __lowerCamelCase			(       self	: Tuple   ,   lowerCAmelCase	: str   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: Dict   ,   lowerCAmelCase	: Dict   ,   lowerCAmelCase	: List[Any] )						->			Union[str, Any]:
      if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
        # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
        # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
        # tiny config could not be created.
        return True

      return False




    def     __lowerCamelCase			(       self	: Union[str, Any] )						->			List[str]:
      a__  		=  TFOpenAIGPTModelTester(self )
      a__  		=  ConfigTester(self   ,   config_class=lowerCAmelCase   ,   n_embd=37 )




    def     __lowerCamelCase			(       self	: Any )						->			Tuple:
      self.config_tester.run_common_tests()




    def     __lowerCamelCase			(       self	: int )						->			Optional[int]:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Any )						->			Any:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_openai_gpt_lm_head(*lowerCAmelCase )




    def     __lowerCamelCase			(       self	: int )						->			Tuple:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_openai_gpt_double_head(*lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Dict )						->			int:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase )




    @slow
    def     __lowerCamelCase			(       self	: str )						->			Dict:
      for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a__  		=  TFOpenAIGPTModel.from_pretrained(lowerCAmelCase )
        self.assertIsNotNone(lowerCAmelCase )







@require_tf
class        UpperCAmelCase_					(				unittest.TestCase     ):




    @slow
    def     __lowerCamelCase			(       self	: Union[str, Any] )						->			Optional[Any]:
      a__  		=  TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt" )
      a__  		=  tf.convert_to_tensor([[481, 4735, 544]]   ,   dtype=tf.intaa )  # the president is
      a__  		=  [
          481,
          4735,
          544,
          246,
          963,
          870,
          762,
          239,
          244,
          40477,
          244,
          249,
          719,
          881,
          487,
          544,
          240,
          244,
          603,
          481,
      ]  # the president is a very good man. " \n " i\'m sure he is, " said the

      a__  		=  model.generate(lowerCAmelCase   ,   do_sample=lowerCAmelCase )
      self.assertListEqual(output_ids[0].numpy().tolist()   ,   lowerCAmelCase )


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     =     logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     =     ["""UtteranceLevel"""]
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  torch.load(lowerCamelCase__			,					map_location="cpu"       )
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}"       )

  a__  		=  checkpoint["Downstream"]

  a__  		=  HubertConfig.from_pretrained(lowerCamelCase__       )
  a__  		=  HubertForSequenceClassification.from_pretrained(lowerCamelCase__			,					config=lowerCamelCase__       )
  a__  		=  WavaVecaFeatureExtractor.from_pretrained(
      lowerCamelCase__			,					return_attention_mask=lowerCamelCase__			,					do_normalize=lowerCamelCase__       )

  if hf_congfig.use_weighted_layer_sum:
    a__  		=  checkpoint["Featurizer"]["weights"]

  a__  		=  downstream_dict["projector.weight"]
  a__  		=  downstream_dict["projector.bias"]
  a__  		=  downstream_dict["model.post_net.linear.weight"]
  a__  		=  downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(lowerCamelCase__       )
  hf_model.save_pretrained(lowerCamelCase__       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     argparse.ArgumentParser()
     parser.add_argument(
         """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
     )
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
     parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
     _SCREAMING_SNAKE_CASE     =     parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

