




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__A        =    []
for i in range(6):
					# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
					rename_keys.append(
					    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
					)
					rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
					rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
					rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
					rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
					rename_keys.append(
					    (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
					)
					rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
					rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
					rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
					# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
					)
					rename_keys.append(
					    (
					        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
					        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
					    )
					)
					rename_keys.append(
					    (
					        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
					        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
					    )
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

					# q, k, v projections in self/cross-attention in decoder for conditional DETR
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
					)
					# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
					)

					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
					)
					# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
					)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      List[Any]      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase		       =    state_dict.pop(UpperCamelCase__      )
				__lowerCamelCase		       =    val







def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												__lowerCamelCase		       =    key.replace('backbone.0.body' ,     'backbone.conv_encoder.model'      )
												__lowerCamelCase		       =    value
								else:
												__lowerCamelCase		       =    value

				return new_state_dict







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      Dict=False      )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    ''
				if is_panoptic:
								__lowerCamelCase		       =    'conditional_detr.'

				# first: transformer encoder
				for i in range(6      ):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								__lowerCamelCase		       =    state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""      )
								__lowerCamelCase		       =    state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""      )
								# next, add query, keys and values (in that order) to the state dict
								__lowerCamelCase		       =    in_proj_weight[:256, :]
								__lowerCamelCase		       =    in_proj_bias[:256]
								__lowerCamelCase		       =    in_proj_weight[256:512, :]
								__lowerCamelCase		       =    in_proj_bias[256:512]
								__lowerCamelCase		       =    in_proj_weight[-256:, :]
								__lowerCamelCase		       =    in_proj_bias[-256:]







def 			lowerCamelCase_		(       )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase		       =    'http://images.cocodataset.org/val2017/000000039769.jpg'
				__lowerCamelCase		       =    Image.open(requests.get(UpperCamelCase__ ,     stream=UpperCamelCase__      ).raw      )

				return im







@torch.no_grad()
def 			lowerCamelCase_		(       UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      str      )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								__lowerCamelCase		       =    'resnet101'
				if "dc5" in model_name:
								__lowerCamelCase		       =    True
				__lowerCamelCase		       =    'panoptic' in model_name
				if is_panoptic:
								__lowerCamelCase		       =    250
				else:
								__lowerCamelCase		       =    91
								__lowerCamelCase		       =    'huggingface/label-files'
								__lowerCamelCase		       =    'coco-detection-id2label.json'
								__lowerCamelCase		       =    json.load(open(hf_hub_download(UpperCamelCase__ ,     UpperCamelCase__ ,     repo_type='dataset'      ) ,     'r'      )      )
								__lowerCamelCase		       =    {int(UpperCamelCase__      ): v for k, v in idalabel.items()}
								__lowerCamelCase		       =    idalabel
								__lowerCamelCase		       =    {v: k for k, v in idalabel.items()}

				# load image processor
				__lowerCamelCase		       =    'coco_panoptic' if is_panoptic else 'coco_detection'
				__lowerCamelCase		       =    ConditionalDetrImageProcessor(format=UpperCamelCase__      )

				# prepare image
				__lowerCamelCase		       =    prepare_img()
				__lowerCamelCase		       =    image_processor(images=UpperCamelCase__ ,     return_tensors='pt'      )
				__lowerCamelCase		       =    encoding['pixel_values']

				logger.info(F"""Converting model {model_name}..."""      )

				# load original model from torch hub
				__lowerCamelCase		       =    torch.hub.load('DeppMeng/ConditionalDETR' ,     UpperCamelCase__ ,     pretrained=UpperCamelCase__      ).eval()
				__lowerCamelCase		       =    conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												__lowerCamelCase		       =    'conditional_detr.' + src
								rename_key(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
				__lowerCamelCase		       =    rename_backbone_keys(UpperCamelCase__      )
				# query, key and value matrices need special treatment
				read_in_q_k_v(UpperCamelCase__ ,     is_panoptic=UpperCamelCase__      )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				__lowerCamelCase		       =    'conditional_detr.model.' if is_panoptic else 'model.'
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('conditional_detr'      )
												    and not key.startswith('class_labels_classifier'      )
												    and not key.startswith('bbox_predictor'      )
												):
																__lowerCamelCase		       =    state_dict.pop(UpperCamelCase__      )
																__lowerCamelCase		       =    val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																__lowerCamelCase		       =    state_dict.pop(UpperCamelCase__      )
																__lowerCamelCase		       =    val
												elif key.startswith('bbox_attention'      ) or key.startswith('mask_head'      ):
																continue
												else:
																__lowerCamelCase		       =    state_dict.pop(UpperCamelCase__      )
																__lowerCamelCase		       =    val
								else:
												if not key.startswith('class_labels_classifier'      ) and not key.startswith('bbox_predictor'      ):
																__lowerCamelCase		       =    state_dict.pop(UpperCamelCase__      )
																__lowerCamelCase		       =    val
    # finally, create HuggingFace model and load state dict
				__lowerCamelCase		       =    ConditionalDetrForSegmentation(UpperCamelCase__      ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase__      )
				model.load_state_dict(UpperCamelCase__      )
				model.eval()
				model.push_to_hub(repo_id=UpperCamelCase__ ,     organization='DepuMeng' ,     commit_message='Add model'      )
				# verify our conversion
				__lowerCamelCase		       =    conditional_detr(UpperCamelCase__      )
				__lowerCamelCase		       =    model(UpperCamelCase__      )
				assert torch.allclose(outputs.logits ,     original_outputs['pred_logits'] ,     atol=1E-4      )
				assert torch.allclose(outputs.pred_boxes ,     original_outputs['pred_boxes'] ,     atol=1E-4      )
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks ,     original_outputs['pred_masks'] ,     atol=1E-4      )

				# Save model and image processor
				logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""      )
				Path(UpperCamelCase__      ).mkdir(exist_ok=UpperCamelCase__      )
				model.save_pretrained(UpperCamelCase__      )
				image_processor.save_pretrained(UpperCamelCase__      )


if __name__ == "__main__":
					__A        =    argparse.ArgumentParser()

					parser.add_argument(
					    "--model_name",
					    default="conditional_detr_resnet50",
					    type=str,
					    help="Name of the CONDITIONAL_DETR model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
					)
					__A        =    parser.parse_args()
					convert_conditional_detr_checkpoint(args.model_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     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  lowercase=13						,  lowercase=7						,  lowercase=True						,  lowercase=True						,  lowercase=True						,  lowercase=99						,  lowercase=32						,  lowercase=5						,  lowercase=4						,  lowercase=37						,  lowercase="gelu"						,  lowercase=0.1						,  lowercase=0.1						,  lowercase=512						,  lowercase=16						,  lowercase=2						,  lowercase=0.0_2						,  lowercase=3						,  lowercase=4						,  lowercase=None						,  )				->       List[str]:
					lowerCamelCase_						=			parent
					lowerCamelCase_						=			batch_size
					lowerCamelCase_						=			seq_length
					lowerCamelCase_						=			is_training
					lowerCamelCase_						=			use_token_type_ids
					lowerCamelCase_						=			use_labels
					lowerCamelCase_						=			vocab_size
					lowerCamelCase_						=			hidden_size
					lowerCamelCase_						=			num_hidden_layers
					lowerCamelCase_						=			num_attention_heads
					lowerCamelCase_						=			intermediate_size
					lowerCamelCase_						=			hidden_act
					lowerCamelCase_						=			hidden_dropout_prob
					lowerCamelCase_						=			attention_probs_dropout_prob
					lowerCamelCase_						=			max_position_embeddings
					lowerCamelCase_						=			type_vocab_size
					lowerCamelCase_						=			type_sequence_label_size
					lowerCamelCase_						=			initializer_range
					lowerCamelCase_						=			num_labels
					lowerCamelCase_						=			num_choices
					lowerCamelCase_						=			scope
					lowerCamelCase_						=			self.vocab_size - 1



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					lowerCamelCase_						=			ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size )

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

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

					lowerCamelCase_						=			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_						=			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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  *lowercase )				->       Dict:
					lowerCamelCase_						=			OpenAIGPTModel(config=lowercase )
					model.to(lowercase )
					model.eval()

					lowerCamelCase_						=			model(lowercase						,  token_type_ids=lowercase						,  head_mask=lowercase )
					lowerCamelCase_						=			model(lowercase						,  token_type_ids=lowercase )
					lowerCamelCase_						=			model(lowercase )

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  *lowercase )				->       int:
					lowerCamelCase_						=			OpenAIGPTLMHeadModel(lowercase )
					model.to(lowercase )
					model.eval()

					lowerCamelCase_						=			model(lowercase						,  token_type_ids=lowercase						,  labels=lowercase )
					self.parent.assertEqual(result.loss.shape						,  () )
					self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.vocab_size) )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  *lowercase )				->       Dict:
					lowerCamelCase_						=			OpenAIGPTDoubleHeadsModel(lowercase )
					model.to(lowercase )
					model.eval()

					lowerCamelCase_						=			model(lowercase						,  token_type_ids=lowercase						,  labels=lowercase )
					self.parent.assertEqual(result.loss.shape						,  () )
					self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.vocab_size) )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  *lowercase )				->       int:
					lowerCamelCase_						=			self.num_labels
					lowerCamelCase_						=			OpenAIGPTForSequenceClassification(lowercase )
					model.to(lowercase )
					model.eval()

					lowerCamelCase_						=			ids_tensor([self.batch_size]						,  self.type_sequence_label_size )
					lowerCamelCase_						=			model(lowercase						,  token_type_ids=lowercase						,  labels=lowercase )
					self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			self.prepare_config_and_inputs()
					(
					    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    
					)						=			config_and_inputs
					lowerCamelCase_						=			{
					    "input_ids": input_ids,
					    "token_type_ids": token_type_ids,
					    "head_mask": head_mask,
					}

					return config, inputs_dict




@require_torch
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_			,       snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = (
	    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
	    if is_torch_available()
	    else ()
	)
	lowerCAmelCase__         = (
	    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
	lowerCAmelCase__         = (
	    {
	        'feature-extraction': OpenAIGPTModel,
	        'text-classification': OpenAIGPTForSequenceClassification,
	        'text-generation': OpenAIGPTLMHeadModel,
	        'zero-shot': OpenAIGPTForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       int:
					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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase=False )				->       Any:
					lowerCamelCase_						=			super()._prepare_for_class(lowercase						,  lowercase						,  return_labels=lowercase )

					if return_labels:
									if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
													lowerCamelCase_						=			torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)						,  dtype=torch.long						,  device=lowercase						,  )
													lowerCamelCase_						=			inputs_dict["labels"]
													lowerCamelCase_						=			inputs_dict["labels"]
													lowerCamelCase_						=			torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.num_choices)						,  dtype=torch.long						,  device=lowercase						,  )
													lowerCamelCase_						=			torch.zeros(
													    self.model_tester.batch_size						,  dtype=torch.long						,  device=lowercase )
					return inputs_dict



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			OpenAIGPTModelTester(self )
					lowerCamelCase_						=			ConfigTester(self						,  config_class=lowercase						,  n_embd=37 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					self.config_tester.run_common_tests()



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_openai_gpt_model(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_lm_head_model(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_double_lm_head_model(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase )



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_						=			OpenAIGPTModel.from_pretrained(lowercase )
									self.assertIsNotNone(lowercase )




@require_torch
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			OpenAIGPTLMHeadModel.from_pretrained("openai-gpt" )
					model.to(lowercase )
					lowerCamelCase_						=			torch.tensor([[481, 4735, 544]]						,  dtype=torch.long						,  device=lowercase )  # the president is
					lowerCamelCase_						=			[
					    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_						=			model.generate(lowercase						,  do_sample=lowercase )
					self.assertListEqual(output_ids[0].tolist()						,  lowercase )


