
"""simple docstring"""







import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
							import torch

							from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin




class  A__       :

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE="resnet50"       ,		_SCREAMING_SNAKE_CASE=3       ,		_SCREAMING_SNAKE_CASE=32       ,		_SCREAMING_SNAKE_CASE=3       ,		_SCREAMING_SNAKE_CASE=True       ,		_SCREAMING_SNAKE_CASE=True       ,		):
			__lowerCAmelCase			:						Optional[int]									=      parent
			__lowerCAmelCase			:						Optional[int]									=      out_indices if out_indices is not None else [4]
			__lowerCAmelCase			:						Optional[int]									=      stage_names
			__lowerCAmelCase			:						Optional[Any]									=      out_features
			__lowerCAmelCase			:						Any									=      backbone
			__lowerCAmelCase			:						List[Any]									=      batch_size
			__lowerCAmelCase			:						List[str]									=      image_size
			__lowerCAmelCase			:						str									=      num_channels
			__lowerCAmelCase			:						int									=      use_pretrained_backbone
			__lowerCAmelCase			:						Optional[Any]									=      is_training

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[int]									=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
			__lowerCAmelCase			:						str									=      self.get_config()

			return config, pixel_values

		def 							__lowerCamelCase							(      self ):
			return TimmBackboneConfig(
			    image_size=self.image_size       ,		num_channels=self.num_channels       ,		out_features=self.out_features       ,		out_indices=self.out_indices       ,		stage_names=self.stage_names       ,		use_pretrained_backbone=self.use_pretrained_backbone       ,		backbone=self.backbone       ,		)

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						List[Any]									=      TimmBackbone(config=_SCREAMING_SNAKE_CASE )
			model.to(_SCREAMING_SNAKE_CASE )
			model.eval()
			with torch.no_grad():
				__lowerCAmelCase			:						List[Any]									=      model(_SCREAMING_SNAKE_CASE )
			self.parent.assertEqual(
			    result.feature_map[-1].shape       ,		(self.batch_size, model.channels[-1], 14, 14)       ,		)







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      self.prepare_config_and_inputs()
			__lowerCAmelCase			,   __lowerCAmelCase			:						Optional[Any]									=      config_and_inputs
			__lowerCAmelCase			:						Union[str, Any]									=      {'pixel_values': pixel_values}
			return config, inputs_dict




@require_torch
@require_timm
class  A__       (	_lowerCamelCase       ,     _lowerCamelCase       ,     _lowerCamelCase       ,     unittest.TestCase):
		A_						:       Tuple				   =	(TimmBackbone,) if is_torch_available() else ()
		A_						:       Dict				   =	{'feature-extraction': TimmBackbone} if is_torch_available() else {}
		A_						:       Optional[int]				   =	False
		A_						:       List[str]				   =	False
		A_						:       str				   =	False
		A_						:       Dict				   =	False

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      TimmBackboneModelTester(self )
			__lowerCAmelCase			:						List[str]									=      ConfigTester(self       ,		config_class=_SCREAMING_SNAKE_CASE       ,		has_text_modality=_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			self.config_tester.create_and_test_config_to_json_string()
			self.config_tester.create_and_test_config_to_json_file()
			self.config_tester.create_and_test_config_from_and_save_pretrained()
			self.config_tester.create_and_test_config_with_num_labels()
			self.config_tester.check_config_can_be_init_without_params()
			self.config_tester.check_config_arguments_init()

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[Any]									=      'resnet18'
			__lowerCAmelCase			:						Optional[int]									=      'microsoft/resnet-18'

			__lowerCAmelCase			:						Optional[Any]									=      AutoBackbone.from_pretrained(_SCREAMING_SNAKE_CASE       ,		use_timm_backbone=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Tuple									=      AutoBackbone.from_pretrained(_SCREAMING_SNAKE_CASE )

			self.assertEqual(len(timm_model.out_features )       ,		len(transformers_model.out_features ) )
			self.assertEqual(len(timm_model.stage_names )       ,		len(transformers_model.stage_names ) )
			self.assertEqual(timm_model.channels       ,		transformers_model.channels )
			# Out indices are set to the last layer by default. For timm models, we don't know
			# the number of layers in advance, so we set it to (-1,), whereas for transformers
			# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
			self.assertEqual(timm_model.out_indices       ,		(-1,) )
			self.assertEqual(transformers_model.out_indices       ,		[len(timm_model.stage_names ) - 1] )

			__lowerCAmelCase			:						int									=      AutoBackbone.from_pretrained(_SCREAMING_SNAKE_CASE       ,		use_timm_backbone=_SCREAMING_SNAKE_CASE       ,		out_indices=[1, 2, 3] )
			__lowerCAmelCase			:						Optional[int]									=      AutoBackbone.from_pretrained(_SCREAMING_SNAKE_CASE       ,		out_indices=[1, 2, 3] )

			self.assertEqual(timm_model.out_indices       ,		transformers_model.out_indices )
			self.assertEqual(len(timm_model.out_features )       ,		len(transformers_model.out_features ) )
			self.assertEqual(timm_model.channels       ,		transformers_model.channels )

		@unittest.skip('TimmBackbone doesn\'t support feed forward chunking' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('TimmBackbone initialization is managed on the timm side' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('model weights aren\'t tied in TimmBackbone.' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('model weights aren\'t tied in TimmBackbone.' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('TimmBackbone doesn\'t support output_attentions.' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('Safetensors is not supported by timm.' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
		def 							__lowerCamelCase							(      self ):
			pass

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			:						List[str]									=      self.model_tester.prepare_config_and_inputs_for_common()

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

				__lowerCAmelCase			:						Optional[int]									=      ['pixel_values']
				self.assertListEqual(arg_names[:1]       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			:						int									=      self.model_tester.prepare_config_and_inputs_for_common()
			__lowerCAmelCase			:						Any									=      True
			__lowerCAmelCase			:						Optional[Any]									=      self.has_attentions

			# no need to test all models as different heads yield the same functionality
			__lowerCAmelCase			:						Any									=      self.all_model_classes[0]
			__lowerCAmelCase			:						Any									=      model_class(_SCREAMING_SNAKE_CASE )
			model.to(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Tuple									=      self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[str]									=      model(**_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      outputs[0][-1]

			# Encoder-/Decoder-only models
			__lowerCAmelCase			:						Dict									=      outputs.hidden_states[0]
			hidden_states.retain_grad()

			if self.has_attentions:
				__lowerCAmelCase			:						Tuple									=      outputs.attentions[0]
				attentions.retain_grad()

			output.flatten()[0].backward(retain_graph=_SCREAMING_SNAKE_CASE )

			self.assertIsNotNone(hidden_states.grad )

			if self.has_attentions:
				self.assertIsNotNone(attentions.grad )







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			:						Tuple									=      self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase			:						Optional[Any]									=      model_class(_SCREAMING_SNAKE_CASE )
				model.to(_SCREAMING_SNAKE_CASE )
				model.eval()
				__lowerCAmelCase			:						Optional[int]									=      model(**_SCREAMING_SNAKE_CASE )

				self.assertEqual(len(result.feature_maps )       ,		len(config.out_indices ) )
				self.assertEqual(len(model.channels )       ,		len(config.out_indices ) )

				# Check output of last stage is taken if out_features=None, out_indices=None
				__lowerCAmelCase			:						Optional[int]									=      copy.deepcopy(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						int									=      None
				__lowerCAmelCase			:						int									=      model_class(_SCREAMING_SNAKE_CASE )
				model.to(_SCREAMING_SNAKE_CASE )
				model.eval()
				__lowerCAmelCase			:						int									=      model(**_SCREAMING_SNAKE_CASE )

				self.assertEqual(len(result.feature_maps )       ,		1 )
				self.assertEqual(len(model.channels )       ,		1 )

				# Check backbone can be initialized with fresh weights
				__lowerCAmelCase			:						List[Any]									=      copy.deepcopy(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						List[str]									=      False
				__lowerCAmelCase			:						int									=      model_class(_SCREAMING_SNAKE_CASE )
				model.to(_SCREAMING_SNAKE_CASE )
				model.eval()
				__lowerCAmelCase			:						Tuple									=      model(**_SCREAMING_SNAKE_CASE )




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)


__UpperCAmelCase          =       {
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					List[str]        =  "codegen"
       UpperCAmelCase__				:					str        =  {
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }
       def __init__(			self,			SCREAMING_SNAKE_CASE_=5_0400,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=4096,			SCREAMING_SNAKE_CASE_=28,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=64,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_="gelu_new",			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=1e-5,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=False,			**SCREAMING_SNAKE_CASE_,			)     ->		Tuple:
          UpperCamelCase    :  Tuple							=	vocab_size
          UpperCamelCase    :  Optional[int]							=	n_ctx
          UpperCamelCase    :  Optional[int]							=	n_positions
          UpperCamelCase    :  List[str]							=	n_embd
          UpperCamelCase    :  Dict							=	n_layer
          UpperCamelCase    :  int							=	n_head
          UpperCamelCase    :  Union[str, Any]							=	n_inner
          UpperCamelCase    :  int							=	rotary_dim
          UpperCamelCase    :  Optional[Any]							=	activation_function
          UpperCamelCase    :  Optional[int]							=	resid_pdrop
          UpperCamelCase    :  Union[str, Any]							=	embd_pdrop
          UpperCamelCase    :  Optional[Any]							=	attn_pdrop
          UpperCamelCase    :  List[str]							=	layer_norm_epsilon
          UpperCamelCase    :  Union[str, Any]							=	initializer_range
          UpperCamelCase    :  str							=	use_cache

          UpperCamelCase    :  Dict							=	bos_token_id
          UpperCamelCase    :  Union[str, Any]							=	eos_token_id

          super().__init__(
              bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			tie_word_embeddings=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )






class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = "default",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			)     ->		List[str]:
          super().__init__(SCREAMING_SNAKE_CASE_,			task=SCREAMING_SNAKE_CASE_,			patching_specs=SCREAMING_SNAKE_CASE_,			use_past=SCREAMING_SNAKE_CASE_       )
          if not getattr(self._config,			'pad_token_id',			SCREAMING_SNAKE_CASE_       ):
             # TODO: how to do that better?
             UpperCamelCase    :  str							=	0
       @property
       def 					snake_case_       (			self       )     ->		Mapping[str, Mapping[int, str]]:
          UpperCamelCase    :  Tuple							=	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
          if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,			direction='inputs'       )
             UpperCamelCase    :  List[Any]							=	{0: 'batch', 1: 'past_sequence + sequence'}
          else:
             UpperCamelCase    :  Optional[int]							=	{0: 'batch', 1: 'sequence'}

          return common_inputs
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_layer
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_head
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Mapping[str, Any]:
          UpperCamelCase    :  Tuple							=	super(SCREAMING_SNAKE_CASE_,			self       ).generate_dummy_inputs(
              SCREAMING_SNAKE_CASE_,			batch_size=SCREAMING_SNAKE_CASE_,			seq_length=SCREAMING_SNAKE_CASE_,			is_pair=SCREAMING_SNAKE_CASE_,			framework=SCREAMING_SNAKE_CASE_       )

          # We need to order the input in the way they appears in the forward()
          UpperCamelCase    :  Optional[int]							=	OrderedDict({'input_ids': common_inputs['input_ids']}       )

          # Need to add the past_keys
          if self.use_past:
             if not is_torch_available():
                raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'       )
             else:
                import torch

                UpperCamelCase	,     UpperCamelCase    :  List[str]							=	common_inputs['input_ids'].shape
                # Not using the same length for past_key_values
                UpperCamelCase    :  List[Any]							=	seqlen + 2
                UpperCamelCase    :  List[str]							=	(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                UpperCamelCase    :  str							=	[
                    (torch.zeros(SCREAMING_SNAKE_CASE_       ), torch.zeros(SCREAMING_SNAKE_CASE_       )) for _ in range(self.num_layers       )
                ]

          UpperCamelCase    :  List[Any]							=	common_inputs['attention_mask']
          if self.use_past:
             UpperCamelCase    :  Optional[Any]							=	ordered_inputs['attention_mask'].dtype
             UpperCamelCase    :  List[Any]							=	torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			dtype=SCREAMING_SNAKE_CASE_       )],			dim=1       )

          return ordered_inputs






       @property
       def 					snake_case_       (			self       )     ->		int:
          return 13



