


import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification

def    lowercase_	(	_A    :					Dict		):

      """simple docstring"""




      lowerCamelCase__	:       Dict					=						[]
      embed.append(
          (
              F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
              F"stage{idx}.patch_embed.proj.weight",
          )		)
      embed.append(
          (
              F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
              F"stage{idx}.patch_embed.proj.bias",
          )		)
      embed.append(
          (
              F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
              F"stage{idx}.patch_embed.norm.weight",
          )		)
      embed.append(
          (
              F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
              F"stage{idx}.patch_embed.norm.bias",
          )		)
      return embed

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

      """simple docstring"""




      lowerCamelCase__	:       int					=						[]
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
              F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
              F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
              F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
              F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
              F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
              F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
              F"stage{idx}.blocks.{cnt}.attn.proj.weight",
          )		)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
              F"stage{idx}.blocks.{cnt}.attn.proj.bias",
          )		)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight")		)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias")		)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight")		)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias")		)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight")		)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias")		)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight")		)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias")		)
      return attention_weights

def    lowercase_	(	_A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       Optional[int]					=						[]
      token.append((F"cvt.encoder.stages.{idx}.cls_token", "stage2.cls_token")		)
      return token

def    lowercase_	(	):

      """simple docstring"""




      lowerCamelCase__	:       str					=						[]
      head.append(("layernorm.weight", "norm.weight")		)
      head.append(("layernorm.bias", "norm.bias")		)
      head.append(("classifier.weight", "head.weight")		)
      head.append(("classifier.bias", "head.bias")		)
      return head

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

      """simple docstring"""




      lowerCamelCase__	:       Any					=						"imagenet-1k-id2label.json"
      lowerCamelCase__	:       Optional[int]					=						1000

      lowerCamelCase__	:       int					=						"huggingface/label-files"
      lowerCamelCase__	:       List[str]					=						num_labels
      lowerCamelCase__	:       Any					=						json.load(open(cached_download(hf_hub_url(_A					,      _A					,      repo_type="dataset"		)		)					,      "r"		)		)
      lowerCamelCase__	:       Dict					=						{int(_A		): v for k, v in idalabel.items()}

      lowerCamelCase__	:       str					=						idalabel
      lowerCamelCase__	:       List[str]					=						{v: k for k, v in idalabel.items()}

      lowerCamelCase__	:       Union[str, Any]					=						CvtConfig(num_labels=_A					,      idalabel=_A					,      labelaid=_A		)

      # For depth size 13 (13 = 1+2+10)
      if cvt_model.rsplit("/"					,      1		)[-1][4:6] == "13":
            lowerCamelCase__	:       Any					=						[1, 2, 10]

      # For depth size 21 (21 = 1+4+16)
      elif cvt_model.rsplit("/"					,      1		)[-1][4:6] == "21":
            lowerCamelCase__	:       List[str]					=						[1, 4, 16]

      # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
      else:
            lowerCamelCase__	:       List[Any]					=						[2, 2, 20]
            lowerCamelCase__	:       str					=						[3, 12, 16]
            lowerCamelCase__	:       Tuple					=						[192, 768, 1024]

      lowerCamelCase__	:       Optional[Any]					=						CvtForImageClassification(_A		)
      lowerCamelCase__	:       Union[str, Any]					=						AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"		)
      lowerCamelCase__	:       Optional[Any]					=						image_size
      lowerCamelCase__	:       Union[str, Any]					=						torch.load(_A					,      map_location=torch.device("cpu"		)		)

      lowerCamelCase__	:       Any					=						OrderedDict()
      lowerCamelCase__	:       Dict					=						[]

      for idx in range(len(config.depth		)		):
            if config.cls_token[idx]:
                  lowerCamelCase__	:       Union[str, Any]					=						list_of_state_dict + cls_token(_A		)
            lowerCamelCase__	:       Tuple					=						list_of_state_dict + embeddings(_A		)
            for cnt in range(config.depth[idx]		):
                  lowerCamelCase__	:       List[Any]					=						list_of_state_dict + attention(_A					,      _A		)

      lowerCamelCase__	:       List[str]					=						list_of_state_dict + final()
      for gg in list_of_state_dict:
            print(_A		)
      for i in range(len(_A		)		):
            lowerCamelCase__	:       Any					=						original_weights[list_of_state_dict[i][1]]

      model.load_state_dict(_A		)
      model.save_pretrained(_A		)
      image_processor.save_pretrained(_A		)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    A   :					List[str]	=      argparse.ArgumentParser()
    parser.add_argument(
        "--cvt_model",
        default="cvt-w24",
        type=str,
        help="Name of the cvt model you'd like to convert.",
    )
    parser.add_argument(
        "--image_size",
        default=384,
        type=int,
        help="Input Image Size",
    )
    parser.add_argument(
        "--cvt_file_name",
        default=r"cvtmodels\CvT-w24-384x384-IN-22k.pth",
        type=str,
        help="Input Image Size",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )

    A   :					List[str]	=      parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






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 )


