




import subprocess
from argparse import ArgumentParser
from typing import List, Union

from huggingface_hub.hf_api import HfFolder, create_repo, whoami
from requests.exceptions import HTTPError

from . import BaseTransformersCLICommand


class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	@staticmethod
	def 				lowerCamelCase__    (				lowerCAmelCase		:	ArgumentParser  )  ->					List[Any]:







						"""simple docstring"""







						__UpperCamelCase				:  str								=			parser.add_parser("""login"""				,    help="""Log in using the same credentials as on huggingface.co"""  )
						login_parser.set_defaults(func=lambda lowerCAmelCase  : LoginCommand(lowerCAmelCase  )  )
						__UpperCamelCase				:  str								=			parser.add_parser("""whoami"""				,    help="""Find out which huggingface.co account you are logged in as."""  )
						whoami_parser.set_defaults(func=lambda lowerCAmelCase  : WhoamiCommand(lowerCAmelCase  )  )
						__UpperCamelCase				:  Optional[Any]								=			parser.add_parser("""logout"""				,    help="""Log out"""  )
						logout_parser.set_defaults(func=lambda lowerCAmelCase  : LogoutCommand(lowerCAmelCase  )  )

						# new system: git-based repo system
						__UpperCamelCase				:  Union[str, Any]								=			parser.add_parser(
						    """repo"""				,    help="""Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos."""				,    )
						__UpperCamelCase				:  Tuple								=			repo_parser.add_subparsers(
						    help="""Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands"""  )
						__UpperCamelCase				:  Optional[Any]								=			repo_subparsers.add_parser(
						    """create"""				,    help="""Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co"""  )
						repo_create_parser.add_argument(
						    """name"""				,    type=lowerCAmelCase				,    help="""Name for your model's repo. Will be namespaced under your username to build the model id."""				,    )
						repo_create_parser.add_argument("""--organization"""				,    type=lowerCAmelCase				,    help="""Optional: organization namespace."""  )
						repo_create_parser.add_argument("""-y"""				,    """--yes"""				,    action="""store_true"""				,    help="""Optional: answer Yes to the prompt"""  )
						repo_create_parser.set_defaults(func=lambda lowerCAmelCase  : RepoCreateCommand(lowerCAmelCase  )  )






class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


	__magic_name__      :					Optional[int]			  =		'\u001b[1m'
	__magic_name__      :					int			  =		'\u001b[31m'
	__magic_name__      :					List[str]			  =		'\u001b[90m'
	__magic_name__      :					Optional[Any]			  =		'\u001b[0m'






	@classmethod
	def 				lowerCamelCase__    (				cls		:	List[str]				,    lowerCAmelCase		:	Any  )  ->					Optional[Any]:







						"""simple docstring"""







						return F'''{cls._bold}{s}{cls._reset}'''






	@classmethod
	def 				lowerCamelCase__    (				cls		:	List[str]				,    lowerCAmelCase		:	List[Any]  )  ->					List[str]:







						"""simple docstring"""







						return F'''{cls._bold}{cls._red}{s}{cls._reset}'''






	@classmethod
	def 				lowerCamelCase__    (				cls		:	Dict				,    lowerCAmelCase		:	Dict  )  ->					Any:







						"""simple docstring"""







						return F'''{cls._gray}{s}{cls._reset}'''






def 							A__   (snake_case      :		List[List[Union[str, int]]]		,  snake_case      :		List[str]							)    ->	str:
					__UpperCamelCase				:  Union[str, Any]								=			[max(len(str(snake_case							)							) for x in col							) for col in zip(*snake_case		,  snake_case							)]
					__UpperCamelCase				:  Union[str, Any]								=			("""{{:{}}} """ * len(snake_case							)).format(*snake_case							)
					__UpperCamelCase				:  Any								=			[]
					lines.append(row_format.format(*snake_case							)							)
					lines.append(row_format.format(*["""-""" * w for w in col_widths]							)							)
					for row in rows:
										lines.append(row_format.format(*snake_case							)							)
					return "\n".join(snake_case							)


class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


	def __init__(				self		:	str				,    lowerCAmelCase		:	int  )  ->					int:







						"""simple docstring"""







						__UpperCamelCase				:  Optional[Any]								=			args






class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	def 				lowerCamelCase__    (				self		:	Optional[Any]  )  ->					Optional[Any]:







						"""simple docstring"""







						print(
						    ANSI.red(
						        """ERROR! `huggingface-cli login` uses an outdated login mechanism """
						        """that is not compatible with the Hugging Face Hub backend anymore. """
						        """Please use `huggingface-cli login instead."""  )  )






class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					Any:







						"""simple docstring"""







						print(
						    ANSI.red(
						        """WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use """
						        """`huggingface-cli whoami` instead."""  )  )
						__UpperCamelCase				:  int								=			HfFolder.get_token()
						if token is None:
											print("""Not logged in"""  )
											exit()
						try:
											__UpperCamelCase			, __UpperCamelCase				:  Optional[Any]								=			whoami(lowerCAmelCase  )
											print(lowerCAmelCase  )
											if orgs:
																print(ANSI.bold("""orgs: """  )				,    """,""".join(lowerCAmelCase  )  )
						except HTTPError as e:
											print(lowerCAmelCase  )
											print(ANSI.red(e.response.text  )  )
											exit(1  )






class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	def 				lowerCamelCase__    (				self		:	Union[str, Any]  )  ->					str:







						"""simple docstring"""







						print(
						    ANSI.red(
						        """ERROR! `transformers-cli logout` uses an outdated logout mechanism """
						        """that is not compatible with the Hugging Face Hub backend anymore. """
						        """Please use `huggingface-cli logout instead."""  )  )






class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	def 				lowerCamelCase__    (				self		:	str  )  ->					Any:







						"""simple docstring"""







						print(
						    ANSI.red(
						        """WARNING! Managing repositories through transformers-cli is deprecated. """
						        """Please use `huggingface-cli` instead."""  )  )
						__UpperCamelCase				:  Tuple								=			HfFolder.get_token()
						if token is None:
											print("""Not logged in"""  )
											exit(1  )
						try:
											__UpperCamelCase				:  str								=			subprocess.check_output(["""git""", """--version"""]  ).decode("""utf-8"""  )
											print(ANSI.gray(stdout.strip()  )  )
						except FileNotFoundError:
											print("""Looks like you do not have git installed, please install."""  )

						try:
											__UpperCamelCase				:  Tuple								=			subprocess.check_output(["""git-lfs""", """--version"""]  ).decode("""utf-8"""  )
											print(ANSI.gray(stdout.strip()  )  )
						except FileNotFoundError:
											print(
											    ANSI.red(
											        """Looks like you do not have git-lfs installed, please install."""
											        """ You can install from https://git-lfs.github.com/."""
											        """ Then run `git lfs install` (you only have to do this once)."""  )  )
						print(""""""  )

						__UpperCamelCase			, __UpperCamelCase				:  List[str]								=			whoami(lowerCAmelCase  )
						__UpperCamelCase				:  int								=			self.args.organization if self.args.organization is not None else user
						__UpperCamelCase				:  Optional[Any]								=			F'''{namespace}/{self.args.name}'''
						print(F'''You are about to create {ANSI.bold(lowerCAmelCase  )}'''  )

						if not self.args.yes:
											__UpperCamelCase				:  Tuple								=			input("""Proceed? [Y/n] """  ).lower()
											if not (choice == "" or choice == "y" or choice == "yes"):
																print("""Abort"""  )
																exit()
						try:
											__UpperCamelCase				:  Optional[int]								=			create_repo(lowerCAmelCase				,    name=self.args.name				,    organization=self.args.organization  )
						except HTTPError as e:
											print(lowerCAmelCase  )
											print(ANSI.red(e.response.text  )  )
											exit(1  )
						print("""\nYour repo now lives at:"""  )
						print(F'''  {ANSI.bold(lowerCAmelCase  )}'''  )
						print("""\nYou can clone it locally with the command below, and commit/push as usual."""  )
						print(F'''\n  git clone {url}'''  )
						print(""""""  )












import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, 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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import EfficientNetForImageClassification, EfficientNetModel
	from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor


class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


	def __init__(				self		:	Optional[int]				,    lowerCAmelCase		:	List[Any]				,    lowerCAmelCase		:	int=13				,    lowerCAmelCase		:	Union[str, Any]=32				,    lowerCAmelCase		:	List[Any]=3				,    lowerCAmelCase		:	Tuple=[3, 3, 5]				,    lowerCAmelCase		:	str=[32, 16, 24]				,    lowerCAmelCase		:	Dict=[16, 24, 40]				,    lowerCAmelCase		:	Optional[Any]=[1, 1, 2]				,    lowerCAmelCase		:	int=[1, 1, 2]				,    lowerCAmelCase		:	Any=[1, 6, 6]				,    lowerCAmelCase		:	str=True				,    lowerCAmelCase		:	Tuple=True				,    lowerCAmelCase		:	Dict=37				,    lowerCAmelCase		:	Any="gelu"				,    lowerCAmelCase		:	List[str]=10				,    )  ->					Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase				:  List[Any]								=			parent
						__UpperCamelCase				:  Any								=			batch_size
						__UpperCamelCase				:  List[str]								=			image_size
						__UpperCamelCase				:  int								=			num_channels
						__UpperCamelCase				:  Tuple								=			kernel_sizes
						__UpperCamelCase				:  Union[str, Any]								=			in_channels
						__UpperCamelCase				:  int								=			out_channels
						__UpperCamelCase				:  List[str]								=			strides
						__UpperCamelCase				:  Optional[Any]								=			num_block_repeats
						__UpperCamelCase				:  Tuple								=			expand_ratios
						__UpperCamelCase				:  Optional[int]								=			is_training
						__UpperCamelCase				:  Dict								=			hidden_act
						__UpperCamelCase				:  Optional[int]								=			num_labels
						__UpperCamelCase				:  List[Any]								=			use_labels






	def 				lowerCamelCase__    (				self		:	Optional[Any]  )  ->					Tuple:







						"""simple docstring"""







						__UpperCamelCase				:  Tuple								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

						__UpperCamelCase				:  int								=			None
						if self.use_labels:
											__UpperCamelCase				:  str								=			ids_tensor([self.batch_size]				,    self.num_labels  )

						__UpperCamelCase				:  int								=			self.get_config()
						return config, pixel_values, labels






	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					Any:







						"""simple docstring"""







						return EfficientNetConfig(
						    num_channels=self.num_channels				,    kernel_sizes=self.kernel_sizes				,    in_channels=self.in_channels				,    out_channels=self.out_channels				,    strides=self.strides				,    num_block_repeats=self.num_block_repeats				,    expand_ratios=self.expand_ratios				,    hidden_act=self.hidden_act				,    num_labels=self.num_labels				,    )






	def 				lowerCamelCase__    (				self		:	Optional[int]				,    lowerCAmelCase		:	int				,    lowerCAmelCase		:	Tuple				,    lowerCAmelCase		:	Optional[Any]  )  ->					Union[str, Any]:







						"""simple docstring"""







						__UpperCamelCase				:  int								=			EfficientNetModel(config=lowerCAmelCase  )
						model.to(lowerCAmelCase  )
						model.eval()
						__UpperCamelCase				:  Union[str, Any]								=			model(lowerCAmelCase  )
						# expected last hidden states: B, C, H // 4, W // 4
						self.parent.assertEqual(
						    result.last_hidden_state.shape				,    (self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)				,    )






	def 				lowerCamelCase__    (				self		:	int				,    lowerCAmelCase		:	Union[str, Any]				,    lowerCAmelCase		:	Optional[Any]				,    lowerCAmelCase		:	str  )  ->					Optional[int]:







						"""simple docstring"""







						__UpperCamelCase				:  List[Any]								=			EfficientNetForImageClassification(lowerCAmelCase  )
						model.to(lowerCAmelCase  )
						model.eval()
						__UpperCamelCase				:  Optional[int]								=			model(lowerCAmelCase				,    labels=lowerCAmelCase  )
						self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)  )






	def 				lowerCamelCase__    (				self		:	Optional[Any]  )  ->					Optional[int]:







						"""simple docstring"""







						__UpperCamelCase				:  Optional[int]								=			self.prepare_config_and_inputs()
						__UpperCamelCase			, __UpperCamelCase			, __UpperCamelCase				:  str								=			config_and_inputs
						__UpperCamelCase				:  Optional[Any]								=			{"""pixel_values""": pixel_values}
						return config, inputs_dict






@require_torch
class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase  ,     _UpperCamelCase  ,     unittest.TestCase      ):



	"""simple docstring"""


	__magic_name__      :					List[Any]			  =		(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
	__magic_name__      :					Tuple			  =		(
	    {'feature-extraction': EfficientNetModel, 'image-classification': EfficientNetForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__magic_name__      :					Union[str, Any]			  =		False
	__magic_name__      :					Dict			  =		False
	__magic_name__      :					str			  =		False
	__magic_name__      :					Union[str, Any]			  =		False
	__magic_name__      :					int			  =		False






	def 				lowerCamelCase__    (				self		:	Dict  )  ->					Optional[int]:







						"""simple docstring"""







						__UpperCamelCase				:  List[str]								=			EfficientNetModelTester(self  )
						__UpperCamelCase				:  Optional[Any]								=			ConfigTester(
						    self				,    config_class=lowerCAmelCase				,    has_text_modality=lowerCAmelCase				,    hidden_size=37  )






	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					int:







						"""simple docstring"""







						self.create_and_test_config_common_properties()
						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		:	Optional[Any]  )  ->					Dict:







						"""simple docstring"""







						return






	@unittest.skip(reason="""EfficientNet does not use inputs_embeds"""  )
	def 				lowerCamelCase__    (				self		:	str  )  ->					List[str]:







						"""simple docstring"""







						pass






	@unittest.skip(reason="""EfficientNet does not support input and output embeddings"""  )
	def 				lowerCamelCase__    (				self		:	Optional[Any]  )  ->					Any:







						"""simple docstring"""







						pass






	@unittest.skip(reason="""EfficientNet does not use feedforward chunking"""  )
	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					Optional[int]:







						"""simple docstring"""







						pass






	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					List[str]:







						"""simple docstring"""







						__UpperCamelCase			, __UpperCamelCase				:  Dict								=			self.model_tester.prepare_config_and_inputs_for_common()

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

											__UpperCamelCase				:  Union[str, Any]								=			["""pixel_values"""]
											self.assertListEqual(arg_names[:1]				,    lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	Any  )  ->					Optional[int]:







						"""simple docstring"""







						__UpperCamelCase				:  Optional[int]								=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					List[Any]:







						"""simple docstring"""







						def check_hidden_states_output(lowerCAmelCase		:	Optional[Any]				,    lowerCAmelCase		:	str				,    lowerCAmelCase		:	Any  ):
											__UpperCamelCase				:  int								=			model_class(lowerCAmelCase  )
											model.to(lowerCAmelCase  )
											model.eval()

											with torch.no_grad():
																__UpperCamelCase				:  Tuple								=			model(**self._prepare_for_class(lowerCAmelCase				,    lowerCAmelCase  )  )

											__UpperCamelCase				:  Any								=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
											__UpperCamelCase				:  Union[str, Any]								=			sum(config.num_block_repeats  ) * 4
											self.assertEqual(len(lowerCAmelCase  )				,    lowerCAmelCase  )

											# EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]  )				,    [self.model_tester.image_size // 2, self.model_tester.image_size // 2]				,    )

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

						for model_class in self.all_model_classes:
											__UpperCamelCase				:  Union[str, Any]								=			True
											check_hidden_states_output(lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase  )

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

											check_hidden_states_output(lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					List[Any]:







						"""simple docstring"""







						__UpperCamelCase				:  Optional[Any]								=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase  )






	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					Dict:







						"""simple docstring"""







						pass






	@slow
	def 				lowerCamelCase__    (				self		:	str  )  ->					Any:







						"""simple docstring"""







						for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__UpperCamelCase				:  Union[str, Any]								=			EfficientNetModel.from_pretrained(lowerCAmelCase  )
											self.assertIsNotNone(lowerCAmelCase  )






	@is_pipeline_test
	@require_vision
	@slow
	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					Union[str, Any]:







						"""simple docstring"""







						super().test_pipeline_image_classification()






def 							A__   ()    ->	Union[str, Any]:
					__UpperCamelCase				:  List[str]								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
					return image







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



	"""simple docstring"""


	@cached_property
	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					Union[str, Any]:







						"""simple docstring"""







						return AutoImageProcessor.from_pretrained("""google/efficientnet-b7"""  ) if is_vision_available() else None






	@slow
	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					Dict:







						"""simple docstring"""







						__UpperCamelCase				:  Optional[Any]								=			EfficientNetForImageClassification.from_pretrained("""google/efficientnet-b7"""  ).to(lowerCAmelCase  )

						__UpperCamelCase				:  Any								=			self.default_image_processor
						__UpperCamelCase				:  Any								=			prepare_img()
						__UpperCamelCase				:  Optional[Any]								=			image_processor(images=lowerCAmelCase				,    return_tensors="""pt"""  ).to(lowerCAmelCase  )

						# forward pass
						with torch.no_grad():
											__UpperCamelCase				:  Tuple								=			model(**lowerCAmelCase  )

						# verify the logits
						__UpperCamelCase				:  Optional[int]								=			torch.Size((1, 1000)  )
						self.assertEqual(outputs.logits.shape				,    lowerCAmelCase  )

						__UpperCamelCase				:  int								=			torch.tensor([0.00_01, 0.00_02, 0.00_02]  ).to(lowerCAmelCase  )
						self.assertTrue(torch.allclose(outputs.logits[0, :3]				,    lowerCAmelCase				,    atol=1E-4  )  )






