





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 							__SCREAMING_SNAKE_CASE       :






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






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
														UpperCamelCase__					  =      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	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      TimmBackbone(config=__lowerCAmelCase	)
														model.to(__lowerCAmelCase	)
														model.eval()
														with torch.no_grad():
																					UpperCamelCase__					  =      model(__lowerCAmelCase	)
														self.parent.assertEqual(
														    result.feature_map[-1].shape	,			(self.batch_size, model.channels[-1], 14, 14)	,			)







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.prepare_config_and_inputs()
														UpperCamelCase__  ,  UpperCamelCase__					  =      config_and_inputs
														UpperCamelCase__					  =      {"""pixel_values""": pixel_values}
														return config, inputs_dict






@require_torch
@require_timm
class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a ,		_a ,		unittest.TestCase							):
							snake_case  :   Any      =							(TimmBackbone,) if is_torch_available() else ()
							snake_case  :   Tuple      =							{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
							snake_case  :   Optional[int]      =							False
							snake_case  :   Union[str, Any]      =							False
							snake_case  :   Dict      =							False
							snake_case  :   Tuple      =							False






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      TimmBackboneModelTester(self	)
														UpperCamelCase__					  =      ConfigTester(self	,			config_class=__lowerCAmelCase	,			has_text_modality=__lowerCAmelCase	)






							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	):
														UpperCamelCase__					  =      """resnet18"""
														UpperCamelCase__					  =      """microsoft/resnet-18"""

														UpperCamelCase__					  =      AutoBackbone.from_pretrained(__lowerCAmelCase	,			use_timm_backbone=__lowerCAmelCase	)
														UpperCamelCase__					  =      AutoBackbone.from_pretrained(__lowerCAmelCase	)

														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]	)

														UpperCamelCase__					  =      AutoBackbone.from_pretrained(__lowerCAmelCase	,			use_timm_backbone=__lowerCAmelCase	,			out_indices=[1, 2, 3]	)
														UpperCamelCase__					  =      AutoBackbone.from_pretrained(__lowerCAmelCase	,			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	):
														UpperCamelCase__  ,  UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()

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

																					UpperCamelCase__					  =      ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]	,			__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__  ,  UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()
														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      self.has_attentions

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

														UpperCamelCase__					  =      self._prepare_for_class(__lowerCAmelCase	,			__lowerCAmelCase	)
														UpperCamelCase__					  =      model(**__lowerCAmelCase	)
														UpperCamelCase__					  =      outputs[0][-1]

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

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

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

														self.assertIsNotNone(hidden_states.grad	)

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







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__  ,  UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					UpperCamelCase__					  =      model_class(__lowerCAmelCase	)
																					model.to(__lowerCAmelCase	)
																					model.eval()
																					UpperCamelCase__					  =      model(**__lowerCAmelCase	)

																					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
																					UpperCamelCase__					  =      copy.deepcopy(__lowerCAmelCase	)
																					UpperCamelCase__					  =      None
																					UpperCamelCase__					  =      model_class(__lowerCAmelCase	)
																					model.to(__lowerCAmelCase	)
																					model.eval()
																					UpperCamelCase__					  =      model(**__lowerCAmelCase	)

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

																					# Check backbone can be initialized with fresh weights
																					UpperCamelCase__					  =      copy.deepcopy(__lowerCAmelCase	)
																					UpperCamelCase__					  =      False
																					UpperCamelCase__					  =      model_class(__lowerCAmelCase	)
																					model.to(__lowerCAmelCase	)
																					model.eval()
																					UpperCamelCase__					  =      model(**__lowerCAmelCase	)











# Copyright 2023 The HuggingFace Inc. team. 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.
import re

from ..utils import cached_file


# docstyle-ignore
UpperCamelCase__           =			"\nHuman: <<task>>\n\nAssistant: "


UpperCamelCase__           =			"huggingface-tools/default-prompts"
UpperCamelCase__           =			{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}





def      _UpperCamelCase  (a__ :List[Any] ,      a__ :Optional[Any] ,      a__ :List[str]="run"							):






							"""simple docstring"""





							if prompt_or_repo_id is None:
														UpperCamelCase__					  =      DEFAULT_PROMPTS_REPO

							# prompt is considered a repo ID when it does not contain any kind of space
							if re.search("""\\s""" ,      a__							) is not None:
														return prompt_or_repo_id

							UpperCamelCase__					  =      cached_file(
							    a__ ,      PROMPT_FILES[mode] ,      repo_type="""dataset""" ,      user_agent={"""agent""": agent_name}							)
							with open(a__ ,      """r""" ,      encoding="""utf-8"""							) as f:
														return f.read()




