





'''simple docstring'''




# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any					,				__snake_case    :					Tuple=0     )	->	Any:
     # Format the message.
     if name is None:
          __A		:					Union[str, Any]						=			None
     else:
          __A		:					List[Any]						=			'.' * max(0					,				spaces - 2     ) + '# {:' + str(50 - spaces     ) + 's}'
          __A		:					Dict						=			fmt.format(__snake_case     )

     # Print and recurse (if needed).
     if isinstance(__snake_case					,				__snake_case     ):
          if msg is not None:
               print(__snake_case     )
          for k in val.keys():
               recursive_print(__snake_case					,				val[k]					,				spaces + 2     )
     elif isinstance(__snake_case					,				torch.Tensor     ):
          print(__snake_case					,				':'					,				val.size()     )
     else:
          print(__snake_case					,				':'					,				__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					List[str]					,				__snake_case    :					Optional[Any]					,				__snake_case    :					List[str]					,				__snake_case    :					List[str]     )	->	Union[str, Any]:
     # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
     # for compatibility with later versions of NVIDIA Megatron-LM.
     # The inverse operation is performed inside Megatron-LM to read checkpoints:
     # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
     # If param is the weight tensor of the self-attention block, the returned tensor
     # will have to be transposed one more time to be read by HuggingFace GPT2.
     __A		:					Optional[Any]						=			param.size()
     if checkpoint_version == 1.0:
          # version 1.0 stores [num_heads * hidden_size * num_splits, :]
          __A		:					Optional[Any]						=			(num_heads, hidden_size, num_splits) + input_shape[1:]
          __A		:					List[str]						=			param.view(*__snake_case     )
          __A		:					Tuple						=			param.transpose(0					,				2     )
          __A		:					Dict						=			param.transpose(1					,				2     ).contiguous()
     elif checkpoint_version >= 2.0:
          # other versions store [num_heads * num_splits * hidden_size, :]
          __A		:					Dict						=			(num_heads, num_splits, hidden_size) + input_shape[1:]
          __A		:					str						=			param.view(*__snake_case     )
          __A		:					Optional[Any]						=			param.transpose(0					,				1     ).contiguous()
     __A		:					Dict						=			param.view(*__snake_case     )
     return param






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]					,				__snake_case    :					str					,				__snake_case    :					Dict     )	->	Union[str, Any]:
     # The converted output model.
     __A		:					str						=			{}

     # old versions did not store training args
     __A		:					List[str]						=			input_state_dict.get('args'					,				__snake_case     )
     if ds_args is not None:
          # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
          # from pprint import pprint
          # pprint(vars(ds_args))

          __A		:					Tuple						=			ds_args.padded_vocab_size
          __A		:					str						=			ds_args.max_position_embeddings
          __A		:					str						=			ds_args.hidden_size
          __A		:					Any						=			ds_args.num_layers
          __A		:					List[Any]						=			ds_args.num_attention_heads
          __A		:					Union[str, Any]						=			ds_args.ffn_hidden_size
          # pprint(config)

     # The number of heads.
     __A		:					List[Any]						=			config.n_head
     # The hidden_size per head.
     __A		:					List[str]						=			config.n_embd // config.n_head
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
          __A		:					Optional[Any]						=			input_state_dict['checkpoint_version']
     else:
          __A		:					List[str]						=			0.0

     # The model.
     __A		:					Any						=			input_state_dict['model']
     # The language model.
     __A		:					Tuple						=			model['language_model']
     # The embeddings.
     __A		:					Any						=			lm['embedding']

     # The word embeddings.
     __A		:					int						=			embeddings['word_embeddings']['weight']
     # Truncate the embedding table to vocab_size rows.
     __A		:					Union[str, Any]						=			word_embeddings[: config.vocab_size, :]
     __A		:					Any						=			word_embeddings

     # The position embeddings.
     __A		:					List[Any]						=			embeddings['position_embeddings']['weight']
     # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
     __A		:					Dict						=			pos_embeddings.size(0     )
     if n_positions != config.n_positions:
          raise ValueError(
              f'pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'     )
     # Store the position embeddings.
     __A		:					Any						=			pos_embeddings

     # The transformer.
     __A		:					Any						=			lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

     # The regex to extract layer names.
     __A		:					Optional[Any]						=			re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'     )

     # The simple map of names for "automated" rules.
     __A		:					int						=			{
         'attention.dense': '.attn.c_proj.',
         'self_attention.dense': '.attn.c_proj.',
         'mlp.dense_h_to_4h': '.mlp.c_fc.',
         'mlp.dense_4h_to_h': '.mlp.c_proj.',
     }

     # Extract the layers.
     for key, val in transformer.items():
          # Match the name.
          __A		:					str						=			layer_re.match(__snake_case     )

          # Stop if that's not a layer
          if m is None:
               break

          # The index of the layer.
          __A		:					Any						=			int(m.group(1     )     )
          # The name of the operation.
          __A		:					List[Any]						=			m.group(2     )
          # Is it a weight or a bias?
          __A		:					str						=			m.group(3     )

          # The name of the layer.
          __A		:					Dict						=			f'transformer.h.{layer_idx}'

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('layernorm'     ):
               __A		:					str						=			'ln_1' if op_name.startswith('input'     ) else 'ln_2'
               __A		:					Union[str, Any]						=			val

          # Transpose the QKV matrix.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "weight":
               # Insert a tensor of 1x1xDxD bias.
               __A		:					Dict						=			torch.tril(torch.ones((n_positions, n_positions)					,				dtype=torch.floataa     )     ).view(
                   1					,				1					,				__snake_case					,				__snake_case     )
               __A		:					Union[str, Any]						=			causal_mask

               # Insert a "dummy" tensor for masked_bias.
               __A		:					Any						=			torch.tensor(-1e4					,				dtype=torch.floataa     )
               __A		:					List[Any]						=			masked_bias

               __A		:					List[Any]						=			fix_query_key_value_ordering(__snake_case					,				__snake_case					,				3					,				__snake_case					,				__snake_case     )
               # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
               __A		:					List[Any]						=			out_val.transpose(0					,				1     ).contiguous()
               # Store.
               __A		:					List[Any]						=			out_val

          # Transpose the bias.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "bias":
               __A		:					Any						=			fix_query_key_value_ordering(__snake_case					,				__snake_case					,				3					,				__snake_case					,				__snake_case     )
               # Store. No change of shape.
               __A		:					Optional[Any]						=			out_val

          # Transpose the weights.
          elif weight_or_bias == "weight":
               __A		:					Union[str, Any]						=			megatron_to_transformers[op_name]
               __A		:					Union[str, Any]						=			val.transpose(0					,				1     )

          # Copy the bias.
          elif weight_or_bias == "bias":
               __A		:					Optional[Any]						=			megatron_to_transformers[op_name]
               __A		:					Tuple						=			val

    # DEBUG.
     assert config.n_layer == layer_idx + 1

     # The final layernorm.
     __A		:					List[Any]						=			transformer['final_layernorm.weight']
     __A		:					int						=			transformer['final_layernorm.bias']

     # For LM head, transformers' wants the matrix to weight embeddings.
     __A		:					Union[str, Any]						=			word_embeddings

     # It should be done!
     return output_state_dict






def 			_lowerCAmelCase     (    )	->	Optional[Any]:
     # Create the argument parser.
     __A		:					List[Any]						=			argparse.ArgumentParser()
     parser.add_argument('--print-checkpoint-structure'					,				action='store_true'     )
     parser.add_argument(
         'path_to_checkpoint'					,				type=__snake_case					,				help='Path to the checkpoint file (.zip archive or direct .pt file)'					,				)
     parser.add_argument(
         '--config_file'					,				default=''					,				type=__snake_case					,				help='An optional config json file describing the pre-trained model.'					,				)
     __A		:					Optional[Any]						=			parser.parse_args()

     # Extract the basename.
     __A		:					List[Any]						=			os.path.dirname(args.path_to_checkpoint     )

     # Load the model.
     # the .zip is very optional, let's keep it for backward compatibility
     print(f'Extracting PyTorch state dictionary from {args.path_to_checkpoint}'     )
     if args.path_to_checkpoint.endswith('.zip'     ):
          with zipfile.ZipFile(args.path_to_checkpoint					,				'r'     ) as checkpoint:
               with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'     ) as pytorch_dict:
                    __A		:					Optional[Any]						=			torch.load(__snake_case					,				map_location='cpu'     )
     else:
          __A		:					List[Any]						=			torch.load(args.path_to_checkpoint					,				map_location='cpu'     )

     __A		:					Dict						=			input_state_dict.get('args'					,				__snake_case     )

     # Read the config, or default to the model released by NVIDIA.
     if args.config_file == "":
          if ds_args is not None:
               if ds_args.bias_gelu_fusion:
                    __A		:					int						=			'gelu_fast'
               elif ds_args.openai_gelu:
                    __A		:					Dict						=			'gelu_new'
               else:
                    __A		:					int						=			'gelu'
          else:
               # in the very early days this used to be "gelu_new"
               __A		:					Optional[Any]						=			'gelu_new'

          # Spell out all parameters in case the defaults change.
          __A		:					str						=			GPTaConfig(
              vocab_size=5_02_57					,				n_positions=10_24					,				n_embd=10_24					,				n_layer=24					,				n_head=16					,				n_inner=40_96					,				activation_function=__snake_case					,				resid_pdrop=0.1					,				embd_pdrop=0.1					,				attn_pdrop=0.1					,				layer_norm_epsilon=1e-5					,				initializer_range=0.02					,				summary_type='cls_index'					,				summary_use_proj=__snake_case					,				summary_activation=__snake_case					,				summary_proj_to_labels=__snake_case					,				summary_first_dropout=0.1					,				scale_attn_weights=__snake_case					,				use_cache=__snake_case					,				bos_token_id=5_02_56					,				eos_token_id=5_02_56					,				)
     else:
          __A		:					int						=			GPTaConfig.from_json_file(args.config_file     )

     __A		:					Optional[Any]						=			['GPT2LMHeadModel']

     # Convert.
     print('Converting'     )
     __A		:					str						=			convert_megatron_checkpoint(__snake_case					,				__snake_case					,				__snake_case     )

     # Print the structure of converted state dict.
     if args.print_checkpoint_structure:
          recursive_print(__snake_case					,				__snake_case     )

     # Add tokenizer class info to config
     # see https://github.com/huggingface/transformers/issues/13906)
     if ds_args is not None:
          __A		:					List[Any]						=			ds_args.tokenizer_type
          if tokenizer_type == "GPT2BPETokenizer":
               __A		:					Tuple						=			'gpt2'
          elif tokenizer_type == "PretrainedFromHF":
               __A		:					Optional[int]						=			ds_args.tokenizer_name_or_path
          else:
               raise ValueError(f'Unrecognized tokenizer_type {tokenizer_type}'     )
     else:
          __A		:					int						=			'gpt2'

     __A		:					Optional[int]						=			AutoTokenizer.from_pretrained(__snake_case     )
     __A		:					List[str]						=			type(__snake_case     ).__name__
     __A		:					int						=			tokenizer_class

     # Store the config to file.
     print('Saving config'     )
     config.save_pretrained(__snake_case     )

     # Save tokenizer based on args
     print(f'Adding {tokenizer_class} tokenizer files'     )
     tokenizer.save_pretrained(__snake_case     )

     # Store the state_dict to file.
     __A		:					Dict						=			os.path.join(__snake_case					,				'pytorch_model.bin'     )
     print(f'Saving checkpoint to "{output_checkpoint_file}"'     )
     torch.save(__snake_case					,				__snake_case     )


####################################################################################################

if __name__ == "__main__":
  main()

####################################################################################################






'''simple docstring'''




import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import YolosForObjectDetection, YolosModel
  from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=13		,      _UpperCAmelCase=[30, 30]		,      _UpperCAmelCase=2		,      _UpperCAmelCase=3		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=32		,      _UpperCAmelCase=5		,      _UpperCAmelCase=4		,      _UpperCAmelCase=37		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=10		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=3		,      _UpperCAmelCase=None		,      _UpperCAmelCase=8		,      _UpperCAmelCase=10		,      ):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			parent
          __A		:					Tuple						=			batch_size
          __A		:					List[str]						=			image_size
          __A		:					Dict						=			patch_size
          __A		:					Optional[Any]						=			num_channels
          __A		:					Tuple						=			is_training
          __A		:					Dict						=			use_labels
          __A		:					List[Any]						=			hidden_size
          __A		:					Tuple						=			num_hidden_layers
          __A		:					int						=			num_attention_heads
          __A		:					Optional[int]						=			intermediate_size
          __A		:					Tuple						=			hidden_act
          __A		:					Any						=			hidden_dropout_prob
          __A		:					Optional[Any]						=			attention_probs_dropout_prob
          __A		:					List[Any]						=			type_sequence_label_size
          __A		:					List[Any]						=			initializer_range
          __A		:					Optional[int]						=			num_labels
          __A		:					List[Any]						=			scope
          __A		:					Any						=			n_targets
          __A		:					Union[str, Any]						=			num_detection_tokens
          # we set the expected sequence length (which is used in several tests)
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
          __A		:					List[str]						=			(image_size[1] // patch_size) * (image_size[0] // patch_size)
          __A		:					int						=			num_patches + 1 + self.num_detection_tokens


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

          __A		:					Tuple						=			None
          if self.use_labels:
               # labels is a list of Dict (each Dict being the labels for a given example in the batch)
               __A		:					List[Any]						=			[]
               for i in range(self.batch_size):
                    __A		:					Optional[int]						=			{}
                    __A		:					Union[str, Any]						=			torch.randint(
                        high=self.num_labels		,      size=(self.n_targets,)		,      device=_UpperCAmelCase)
                    __A		:					str						=			torch.rand(self.n_targets		,      4		,      device=_UpperCAmelCase)
                    labels.append(_UpperCAmelCase)

          __A		:					Any						=			self.get_config()

          return config, pixel_values, labels


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return YolosConfig(
              image_size=self.image_size		,      patch_size=self.patch_size		,      num_channels=self.num_channels		,      hidden_size=self.hidden_size		,      num_hidden_layers=self.num_hidden_layers		,      num_attention_heads=self.num_attention_heads		,      intermediate_size=self.intermediate_size		,      hidden_act=self.hidden_act		,      hidden_dropout_prob=self.hidden_dropout_prob		,      attention_probs_dropout_prob=self.attention_probs_dropout_prob		,      is_decoder=_UpperCAmelCase		,      initializer_range=self.initializer_range		,      num_detection_tokens=self.num_detection_tokens		,      num_labels=self.num_labels		,      )


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			YolosModel(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					Dict						=			model(_UpperCAmelCase)
          self.parent.assertEqual(
              result.last_hidden_state.shape		,      (self.batch_size, self.expected_seq_len, self.hidden_size))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			YolosForObjectDetection(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()

          __A		:					str						=			model(pixel_values=_UpperCAmelCase)
          __A		:					List[str]						=			model(_UpperCAmelCase)

          self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
          self.parent.assertEqual(result.pred_boxes.shape		,      (self.batch_size, self.num_detection_tokens, 4))

          __A		:					Union[str, Any]						=			model(pixel_values=_UpperCAmelCase		,      labels=_UpperCAmelCase)

          self.parent.assertEqual(result.loss.shape		,      ())
          self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
          self.parent.assertEqual(result.pred_boxes.shape		,      (self.batch_size, self.num_detection_tokens, 4))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			self.prepare_config_and_inputs()
          __A					,__A					,__A		:					Tuple						=			config_and_inputs
          __A		:					Tuple						=			{'pixel_values': pixel_values}
          return config, inputs_dict




@require_torch
class 					SCREAMING_SNAKE_CASE       (a__   ,						a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     lowerCAmelCase						     =			(
         {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
     )

     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=False):

          '''simple docstring'''






          __A		:					Optional[Any]						=			super()._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase		,      return_labels=_UpperCAmelCase)

          if return_labels:
               if model_class.__name__ == "YolosForObjectDetection":
                    __A		:					Any						=			[]
                    for i in range(self.model_tester.batch_size):
                         __A		:					Tuple						=			{}
                         __A		:					Tuple						=			torch.ones(
                             size=(self.model_tester.n_targets,)		,      device=_UpperCAmelCase		,      dtype=torch.long)
                         __A		:					Optional[Any]						=			torch.ones(
                             self.model_tester.n_targets		,      4		,      device=_UpperCAmelCase		,      dtype=torch.float)
                         labels.append(_UpperCAmelCase)
                    __A		:					str						=			labels

          return inputs_dict


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			YolosModelTester(self)
          __A		:					Dict						=			ConfigTester(self		,      config_class=_UpperCAmelCase		,      has_text_modality=_UpperCAmelCase		,      hidden_size=37)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               self.assertIsInstance(model.get_input_embeddings()		,      (nn.Module))
               __A		:					Any						=			model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(_UpperCAmelCase		,      nn.Linear))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A		:					List[Any]						=			model_class(_UpperCAmelCase)
               __A		:					str						=			inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __A		:					int						=			[*signature.parameters.keys()]

               __A		:					List[str]						=			['pixel_values']
               self.assertListEqual(arg_names[:1]		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					Dict						=			self.model_tester.prepare_config_and_inputs_for_common()
          __A		:					Optional[int]						=			True

          # in YOLOS, the seq_len is different
          __A		:					Dict						=			self.model_tester.expected_seq_len
          for model_class in self.all_model_classes:
               __A		:					Dict						=			True
               __A		:					Dict						=			False
               __A		:					Union[str, Any]						=			True
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()
               with torch.no_grad():
                    __A		:					Any						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))
               __A		:					Union[str, Any]						=			outputs.attentions
               self.assertEqual(len(_UpperCAmelCase)		,      self.model_tester.num_hidden_layers)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               __A		:					List[Any]						=			True
               __A		:					List[str]						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()
               with torch.no_grad():
                    __A		:					List[Any]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))
               __A		:					Optional[Any]						=			outputs.attentions
               self.assertEqual(len(_UpperCAmelCase)		,      self.model_tester.num_hidden_layers)

               self.assertListEqual(
                   list(attentions[0].shape[-3:])		,      [self.model_tester.num_attention_heads, seq_len, seq_len]		,      )
               __A		:					str						=			len(_UpperCAmelCase)

               # Check attention is always last and order is fine
               __A		:					Dict						=			True
               __A		:					Dict						=			True
               __A		:					Dict						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()
               with torch.no_grad():
                    __A		:					List[str]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))

               __A		:					Union[str, Any]						=			1
               self.assertEqual(out_len + added_hidden_states		,      len(_UpperCAmelCase))

               __A		:					Optional[Any]						=			outputs.attentions

               self.assertEqual(len(_UpperCAmelCase)		,      self.model_tester.num_hidden_layers)
               self.assertListEqual(
                   list(self_attentions[0].shape[-3:])		,      [self.model_tester.num_attention_heads, seq_len, seq_len]		,      )


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          def check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()

               with torch.no_grad():
                    __A		:					List[str]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))

               __A		:					Optional[Any]						=			outputs.hidden_states

               __A		:					List[str]						=			getattr(
                   self.model_tester		,      'expected_num_hidden_layers'		,      self.model_tester.num_hidden_layers + 1)
               self.assertEqual(len(_UpperCAmelCase)		,      _UpperCAmelCase)

               # YOLOS has a different seq_length
               __A		:					Dict						=			self.model_tester.expected_seq_len

               self.assertListEqual(
                   list(hidden_states[0].shape[-2:])		,      [seq_length, self.model_tester.hidden_size]		,      )

          __A					,__A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A		:					List[str]						=			True
               check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __A		:					Optional[int]						=			True

               check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_object_detection(*_UpperCAmelCase)


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A		:					List[Any]						=			YolosModel.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)






def 			_lowerCAmelCase     (    )	->	int:
     __A		:					Dict						=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
     return image







@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     @cached_property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return AutoImageProcessor.from_pretrained('hustvl/yolos-small') if is_vision_available() else None


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			YolosForObjectDetection.from_pretrained('hustvl/yolos-small').to(_UpperCAmelCase)

          __A		:					Any						=			self.default_image_processor
          __A		:					str						=			prepare_img()
          __A		:					int						=			image_processor(images=_UpperCAmelCase		,      return_tensors='pt').to(_UpperCAmelCase)

          # forward pass
          with torch.no_grad():
               __A		:					str						=			model(inputs.pixel_values)

          # verify outputs
          __A		:					Tuple						=			torch.Size((1, 100, 92))
          self.assertEqual(outputs.logits.shape		,      _UpperCAmelCase)

          __A		:					Dict						=			torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]		,      device=_UpperCAmelCase		,      )
          __A		:					int						=			torch.tensor(
              [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]		,      device=_UpperCAmelCase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		,      _UpperCAmelCase		,      atol=1e-4))
          self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]		,      _UpperCAmelCase		,      atol=1e-4))

          # verify postprocessing
          __A		:					List[str]						=			image_processor.post_process_object_detection(
              _UpperCAmelCase		,      threshold=0.3		,      target_sizes=[image.size[::-1]])[0]
          __A		:					Optional[int]						=			torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]).to(_UpperCAmelCase)
          __A		:					Union[str, Any]						=			[75, 75, 17, 63, 17]
          __A		:					Any						=			torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]).to(_UpperCAmelCase)

          self.assertEqual(len(results['scores'])		,      5)
          self.assertTrue(torch.allclose(results['scores']		,      _UpperCAmelCase		,      atol=1e-4))
          self.assertSequenceEqual(results['labels'].tolist()		,      _UpperCAmelCase)
          self.assertTrue(torch.allclose(results['boxes'][0, :]		,      _UpperCAmelCase))