


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
A   :					Tuple	=      logging.get_logger(__name__)

A   :					Dict	=      {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
A   :					List[str]	=      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]

def    lowercase_	(	_A    :					List[str]		):

      """simple docstring"""




      lowerCamelCase__	:       List[str]					=						{}
      with open(_A					,      "r"		) as file:
            for line_number, line in enumerate(_A		):
                  lowerCamelCase__	:       int					=						line.strip()
                  if line:
                        lowerCamelCase__	:       Any					=						line.split()
                        lowerCamelCase__	:       Any					=						line_number
                        lowerCamelCase__	:       List[Any]					=						words[0]
                        lowerCamelCase__	:       int					=						value
      return result

def    lowercase_	(	_A    :					Tuple					,      _A    :					Optional[Any]					,      _A    :					Dict					,      _A    :					List[Any]					,      _A    :					Tuple		):

      """simple docstring"""




      for attribute in key.split("."		):
            lowerCamelCase__	:       Any					=						getattr(_A					,      _A		)

      lowerCamelCase__	:       str					=						None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(_A		):
                  lowerCamelCase__	:       int					=						PARAM_MAPPING[full_name.split("."		)[-1]]
                  lowerCamelCase__	:       Tuple					=						"param"

      if weight_type is not None and weight_type != "param":
            lowerCamelCase__	:       Union[str, Any]					=						getattr(_A					,      _A		).shape
      elif weight_type is not None and weight_type == "param":
            lowerCamelCase__	:       Tuple					=						hf_pointer
            for attribute in hf_param_name.split("."		):
                  lowerCamelCase__	:       Tuple					=						getattr(_A					,      _A		)
            lowerCamelCase__	:       List[str]					=						shape_pointer.shape

            # let's reduce dimension
            lowerCamelCase__	:       List[str]					=						value[0]
      else:
            lowerCamelCase__	:       int					=						hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
                F" {value.shape} for {full_name}"		)

      if weight_type == "weight":
            lowerCamelCase__	:       Dict					=						value
      elif weight_type == "weight_g":
            lowerCamelCase__	:       Union[str, Any]					=						value
      elif weight_type == "weight_v":
            lowerCamelCase__	:       Union[str, Any]					=						value
      elif weight_type == "bias":
            lowerCamelCase__	:       int					=						value
      elif weight_type == "param":
            for attribute in hf_param_name.split("."		):
                  lowerCamelCase__	:       Any					=						getattr(_A					,      _A		)
            lowerCamelCase__	:       int					=						value
      else:
            lowerCamelCase__	:       Dict					=						value

      logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."		)

def    lowercase_	(	_A    :					List[Any]					,      _A    :					Any					,      _A    :					List[Any]					,      _A    :					List[Any]					,      _A    :					Tuple		):

      """simple docstring"""




      lowerCamelCase__	:       Union[str, Any]					=						None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(_A		):
                  lowerCamelCase__	:       List[Any]					=						PARAM_MAPPING[full_name.split("."		)[-1]]
                  lowerCamelCase__	:       int					=						"param"

      if weight_type is not None and weight_type != "param":
            lowerCamelCase__	:       Union[str, Any]					=						".".join([key, weight_type]		)
      elif weight_type is not None and weight_type == "param":
            lowerCamelCase__	:       Union[str, Any]					=						".".join([key, hf_param_name]		)
      else:
            lowerCamelCase__	:       str					=						key

      lowerCamelCase__	:       List[str]					=						value if "lm_head" in full_key else value[0]


A   :					int	=      {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}

def    lowercase_	(	_A    :					Optional[int]					,      _A    :					Any					,      _A    :					Optional[Any]=None					,      _A    :					Tuple=None		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						False
      for key, mapped_key in MAPPING.items():
            lowerCamelCase__	:       Dict					=						"wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
            if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
                  lowerCamelCase__	:       Tuple					=						True
                  if "*" in mapped_key:
                        lowerCamelCase__	:       Any					=						name.split(_A		)[0].split("."		)[-2]
                        lowerCamelCase__	:       Optional[int]					=						mapped_key.replace("*"					,      _A		)
                  if "weight_g" in name:
                        lowerCamelCase__	:       Union[str, Any]					=						"weight_g"
                  elif "weight_v" in name:
                        lowerCamelCase__	:       List[Any]					=						"weight_v"
                  elif "bias" in name:
                        lowerCamelCase__	:       List[Any]					=						"bias"
                  elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        lowerCamelCase__	:       Optional[Any]					=						"weight"
                  else:
                        lowerCamelCase__	:       Tuple					=						None
                  if hf_dict is not None:
                        rename_dict(_A					,      _A					,      _A					,      _A					,      _A		)
                  else:
                        set_recursively(_A					,      _A					,      _A					,      _A					,      _A		)
                  return is_used
      return is_used

def    lowercase_	(	_A    :					Dict					,      _A    :					int					,      _A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						[]
      lowerCamelCase__	:       Union[str, Any]					=						fairseq_model.state_dict()

      lowerCamelCase__	:       Optional[int]					=						hf_model.wavaveca.feature_extractor

      for name, value in fairseq_dict.items():
            lowerCamelCase__	:       Union[str, Any]					=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      _A					,      _A					,      _A					,      _A					,      hf_model.config.feat_extract_norm == "group"					,      )
                  lowerCamelCase__	:       Optional[int]					=						True
            else:
                  lowerCamelCase__	:       Optional[int]					=						load_wavaveca_layer(_A					,      _A					,      _A		)
            if not is_used:
                  unused_weights.append(_A		)

      logger.warning(F"Unused weights: {unused_weights}"		)

def    lowercase_	(	_A    :					Union[str, Any]					,      _A    :					Dict					,      _A    :					Optional[Any]					,      _A    :					List[Any]					,      _A    :					Union[str, Any]		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						full_name.split("conv_layers."		)[-1]
      lowerCamelCase__	:       Optional[Any]					=						name.split("."		)
      lowerCamelCase__	:       Any					=						int(items[0]		)
      lowerCamelCase__	:       int					=						int(items[1]		)

      if type_id == 0:
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            F"{full_name} has size {value.shape}, but"
                            F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."		)
                  lowerCamelCase__	:       Tuple					=						value
                  logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            F"{full_name} has size {value.shape}, but"
                            F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."		)
                  lowerCamelCase__	:       Tuple					=						value
                  logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            F"{full_name} has size {value.shape}, but"
                            F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."		)
                  lowerCamelCase__	:       Union[str, Any]					=						value
                  logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            F"{full_name} has size {value.shape}, but"
                            F" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."		)
                  lowerCamelCase__	:       Any					=						value
                  logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
      else:
            unused_weights.append(_A		)

@torch.no_grad()
def    lowercase_	(	_A    :					int					,      _A    :					Any					,      _A    :					Any=None					,      _A    :					List[Any]=None					,      _A    :					Tuple=True					,      _A    :					List[Any]=False		):

      """simple docstring"""




      if config_path is not None:
            lowerCamelCase__	:       Optional[Any]					=						WavaVecaConfig.from_pretrained(_A		)
      else:
            lowerCamelCase__	:       str					=						WavaVecaConfig()

      if is_seq_class:
            lowerCamelCase__	:       str					=						read_txt_into_dict(_A		)
            lowerCamelCase__	:       List[Any]					=						idalabel
            lowerCamelCase__	:       List[Any]					=						WavaVecaForSequenceClassification(_A		)
            lowerCamelCase__	:       Optional[int]					=						WavaVecaFeatureExtractor(
                feature_size=1					,      sampling_rate=16000					,      padding_value=0					,      do_normalize=_A					,      return_attention_mask=_A					,      )
            feature_extractor.save_pretrained(_A		)

      elif is_finetuned:
            if dict_path:
                  lowerCamelCase__	:       Dict					=						Dictionary.load(_A		)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  lowerCamelCase__	:       List[Any]					=						target_dict.pad_index
                  lowerCamelCase__	:       str					=						target_dict.bos_index
                  lowerCamelCase__	:       str					=						target_dict.eos_index
                  lowerCamelCase__	:       Tuple					=						len(target_dict.symbols		)
                  lowerCamelCase__	:       Optional[Any]					=						os.path.join(_A					,      "vocab.json"		)
                  if not os.path.isdir(_A		):
                        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_A		)		)
                        return
                  os.makedirs(_A					,      exist_ok=_A		)
                  lowerCamelCase__	:       List[str]					=						target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  lowerCamelCase__	:       str					=						0
                  lowerCamelCase__	:       Dict					=						1
                  with open(_A					,      "w"					,      encoding="utf-8"		) as vocab_handle:
                        json.dump(_A					,      _A		)
                  lowerCamelCase__	:       Any					=						WavaVecaCTCTokenizer(
                      _A					,      unk_token=target_dict.unk_word					,      pad_token=target_dict.pad_word					,      bos_token=target_dict.bos_word					,      eos_token=target_dict.eos_word					,      word_delimiter_token="|"					,      do_lower_case=_A					,      )
                  lowerCamelCase__	:       Dict					=						True if config.feat_extract_norm == "layer" else False
                  lowerCamelCase__	:       List[Any]					=						WavaVecaFeatureExtractor(
                      feature_size=1					,      sampling_rate=16000					,      padding_value=0					,      do_normalize=_A					,      return_attention_mask=_A					,      )
                  lowerCamelCase__	:       Any					=						WavaVecaProcessor(feature_extractor=_A					,      tokenizer=_A		)
                  processor.save_pretrained(_A		)

            lowerCamelCase__	:       int					=						WavaVecaForCTC(_A		)
      else:
            lowerCamelCase__	:       Optional[Any]					=						WavaVecaForPreTraining(_A		)

      if is_finetuned or is_seq_class:
            lowerCamelCase__	:       Dict					=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]					,      arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
      else:
            lowerCamelCase__	:       Dict					=						argparse.Namespace(task="audio_pretraining"		)
            lowerCamelCase__	:       Any					=						fairseq.tasks.setup_task(_A		)

            lowerCamelCase__	:       str					=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,      task=_A		)

      lowerCamelCase__	:       List[str]					=						model[0].eval()

      recursively_load_weights(_A					,      _A					,      not is_finetuned		)

      hf_wavavec.save_pretrained(_A		)


if __name__ == "__main__":
    A   :					List[Any]	=      argparse.ArgumentParser()
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
    parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    parser.add_argument(
        "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    parser.add_argument(
        "--is_seq_class",
        action="store_true",
        help="Whether the model to convert is a fine-tuned sequence classification model or not",
    )
    A   :					Optional[Any]	=      parser.parse_args()

    A   :					Any	=      not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OpenAIGPTConfig,
        OpenAIGPTDoubleHeadsModel,
        OpenAIGPTForSequenceClassification,
        OpenAIGPTLMHeadModel,
        OpenAIGPTModel,
    )







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Dict      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Optional[int]=13      ,   __lowerCamelCase							:    List[str]=7      ,   __lowerCamelCase							:    Tuple=True      ,   __lowerCamelCase							:    Optional[int]=True      ,   __lowerCamelCase							:    List[str]=True      ,   __lowerCamelCase							:    Union[str, Any]=99      ,   __lowerCamelCase							:    List[Any]=32      ,   __lowerCamelCase							:    List[Any]=5      ,   __lowerCamelCase							:    Optional[Any]=4      ,   __lowerCamelCase							:    Optional[int]=37      ,   __lowerCamelCase							:    List[str]="gelu"      ,   __lowerCamelCase							:    List[str]=0.1      ,   __lowerCamelCase							:    int=0.1      ,   __lowerCamelCase							:    List[str]=512      ,   __lowerCamelCase							:    Optional[Any]=16      ,   __lowerCamelCase							:    Optional[Any]=2      ,   __lowerCamelCase							:    str=0.0_2      ,   __lowerCamelCase							:    List[str]=3      ,   __lowerCamelCase							:    Tuple=4      ,   __lowerCamelCase							:    Optional[int]=None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						parent
             lowerCamelCase__	:       int					=						batch_size
             lowerCamelCase__	:       List[Any]					=						seq_length
             lowerCamelCase__	:       Union[str, Any]					=						is_training
             lowerCamelCase__	:       Any					=						use_token_type_ids
             lowerCamelCase__	:       Union[str, Any]					=						use_labels
             lowerCamelCase__	:       List[str]					=						vocab_size
             lowerCamelCase__	:       Union[str, Any]					=						hidden_size
             lowerCamelCase__	:       List[Any]					=						num_hidden_layers
             lowerCamelCase__	:       Optional[Any]					=						num_attention_heads
             lowerCamelCase__	:       Any					=						intermediate_size
             lowerCamelCase__	:       str					=						hidden_act
             lowerCamelCase__	:       str					=						hidden_dropout_prob
             lowerCamelCase__	:       Any					=						attention_probs_dropout_prob
             lowerCamelCase__	:       List[str]					=						max_position_embeddings
             lowerCamelCase__	:       Optional[int]					=						type_vocab_size
             lowerCamelCase__	:       List[Any]					=						type_sequence_label_size
             lowerCamelCase__	:       List[str]					=						initializer_range
             lowerCamelCase__	:       List[str]					=						num_labels
             lowerCamelCase__	:       List[Any]					=						num_choices
             lowerCamelCase__	:       Optional[Any]					=						scope
             lowerCamelCase__	:       List[Any]					=						self.vocab_size - 1





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size )

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

             lowerCamelCase__	:       Any					=						None
             lowerCamelCase__	:       str					=						None
             lowerCamelCase__	:       str					=						None
             if self.use_labels:
                   lowerCamelCase__	:       Dict					=						ids_tensor([self.batch_size]      ,   self.type_sequence_label_size )
                   lowerCamelCase__	:       Dict					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.num_labels )
                   lowerCamelCase__	:       Dict					=						ids_tensor([self.batch_size]      ,   self.num_choices )

             lowerCamelCase__	:       Union[str, Any]					=						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      ,   )

             lowerCamelCase__	:       Optional[int]					=						ids_tensor([self.num_hidden_layers, self.num_attention_heads]      ,   2 )

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





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    int      ,   *__lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						OpenAIGPTModel(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()

             lowerCamelCase__	:       Tuple					=						model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   head_mask=__lowerCamelCase )
             lowerCamelCase__	:       str					=						model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						model(__lowerCamelCase )

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





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Any      ,   *__lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						OpenAIGPTLMHeadModel(__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()

             lowerCamelCase__	:       List[str]					=						model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase )
             self.parent.assertEqual(result.loss.shape      ,   () )
             self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size) )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Optional[int]      ,   *__lowerCamelCase							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						OpenAIGPTDoubleHeadsModel(__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()

             lowerCamelCase__	:       Optional[Any]					=						model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase )
             self.parent.assertEqual(result.loss.shape      ,   () )
             self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size) )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    List[Any]      ,   *__lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						self.num_labels
             lowerCamelCase__	:       Tuple					=						OpenAIGPTForSequenceClassification(__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()

             lowerCamelCase__	:       Dict					=						ids_tensor([self.batch_size]      ,   self.type_sequence_label_size )
             lowerCamelCase__	:       List[str]					=						model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase )
             self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels) )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						self.prepare_config_and_inputs()
             (
                 (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     
             )	:       Any					=						config_and_inputs
             lowerCamelCase__	:       Union[str, Any]					=						{
                 "input_ids": input_ids,
                 "token_type_ids": token_type_ids,
                 "head_mask": head_mask,
             }

             return config, inputs_dict







@require_torch
class     _lowercase				(							lowercase__							,						lowercase__							,						lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    (
           (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
           if is_torch_available()
           else ()
       )
       A__      		=    (
           (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
       )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
       A__      		=    (
           {
               "feature-extraction": OpenAIGPTModel,
               "text-classification": OpenAIGPTForSequenceClassification,
               "text-generation": OpenAIGPTLMHeadModel,
               "zero-shot": OpenAIGPTForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             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]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Tuple=False ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						super()._prepare_for_class(__lowerCamelCase      ,   __lowerCamelCase      ,   return_labels=__lowerCamelCase )

             if return_labels:
                   if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                         lowerCamelCase__	:       Optional[Any]					=						torch.zeros(
                             (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)      ,   dtype=torch.long      ,   device=__lowerCamelCase      ,   )
                         lowerCamelCase__	:       Tuple					=						inputs_dict["labels"]
                         lowerCamelCase__	:       Any					=						inputs_dict["labels"]
                         lowerCamelCase__	:       Any					=						torch.zeros(
                             (self.model_tester.batch_size, self.model_tester.num_choices)      ,   dtype=torch.long      ,   device=__lowerCamelCase      ,   )
                         lowerCamelCase__	:       Union[str, Any]					=						torch.zeros(
                             self.model_tester.batch_size      ,   dtype=torch.long      ,   device=__lowerCamelCase )
             return inputs_dict





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						OpenAIGPTModelTester(self )
             lowerCamelCase__	:       Union[str, Any]					=						ConfigTester(self      ,   config_class=__lowerCamelCase      ,   n_embd=37 )





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_openai_gpt_model(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_lm_head_model(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_double_lm_head_model(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__lowerCamelCase )





       @slow
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__	:       Any					=						OpenAIGPTModel.from_pretrained(__lowerCamelCase )
                   self.assertIsNotNone(__lowerCamelCase )







@require_torch
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						OpenAIGPTLMHeadModel.from_pretrained("openai-gpt" )
             model.to(__lowerCamelCase )
             lowerCamelCase__	:       int					=						torch.tensor([[481, 4735, 544]]      ,   dtype=torch.long      ,   device=__lowerCamelCase )  # the president is
             lowerCamelCase__	:       Union[str, Any]					=						[
                 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

             lowerCamelCase__	:       int					=						model.generate(__lowerCamelCase      ,   do_sample=__lowerCamelCase )
             self.assertListEqual(output_ids[0].tolist()      ,   __lowerCamelCase )


