

"""simple docstring"""




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     __UpperCamelCase				(     _a		):


		@staticmethod
		def       SCREAMING_SNAKE_CASE__     (__SCREAMING_SNAKE_CASE				:	Optional[int]):
							A    =      parser.add_parser("login"  ,  help="Log in using the same credentials as on huggingface.co")
							login_parser.set_defaults(func=lambda __SCREAMING_SNAKE_CASE: LoginCommand(lowerCamelCase__))
							A    =      parser.add_parser("whoami"  ,  help="Find out which huggingface.co account you are logged in as.")
							whoami_parser.set_defaults(func=lambda __SCREAMING_SNAKE_CASE: WhoamiCommand(lowerCamelCase__))
							A    =      parser.add_parser("logout"  ,  help="Log out")
							logout_parser.set_defaults(func=lambda __SCREAMING_SNAKE_CASE: LogoutCommand(lowerCamelCase__))

							# new system: git-based repo system
							A    =      parser.add_parser(
							    "repo"  ,  help="Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos."  ,  )
							A    =      repo_parser.add_subparsers(
							    help="Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands")
							A    =      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 __SCREAMING_SNAKE_CASE: RepoCreateCommand(lowerCamelCase__))





class     __UpperCamelCase				:
		SCREAMING_SNAKE_CASE              =					"\u001b[1m"
		SCREAMING_SNAKE_CASE              =					"\u001b[31m"
		SCREAMING_SNAKE_CASE              =					"\u001b[90m"
		SCREAMING_SNAKE_CASE              =					"\u001b[0m"


		@classmethod
		def       SCREAMING_SNAKE_CASE__     (cls				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	List[Any]):
							return F"""{cls._bold}{s}{cls._reset}"""


		@classmethod
		def       SCREAMING_SNAKE_CASE__     (cls				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	List[Any]):
							return F"""{cls._bold}{cls._red}{s}{cls._reset}"""


		@classmethod
		def       SCREAMING_SNAKE_CASE__     (cls				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]):
							return F"""{cls._gray}{s}{cls._reset}"""

def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					A    =      [max(len(str(lowercase__   )   ) for x in col   ) for col in zip(*lowercase__    ,							lowercase__   )]
					A    =      ("{{:{}}} " * len(lowercase__   )).format(*lowercase__   )
					A    =      []
					lines.append(row_format.format(*lowercase__   )   )
					lines.append(row_format.format(*["-" * w for w in col_widths]   )   )
					for row in rows:
										lines.append(row_format.format(*lowercase__   )   )
					return "\n".join(lowercase__   )





class     __UpperCamelCase				:


		def __init__(self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
							A    =      args





class     __UpperCamelCase				(     _a		):


		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
							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     __UpperCamelCase				(     _a		):


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
							print(
							    ANSI.red(
							        "WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use "
							        "`huggingface-cli whoami` instead."))
							A    =      HfFolder.get_token()
							if token is None:
												print("Not logged in")
												exit()
							try:
												A    =      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     __UpperCamelCase				(     _a		):


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							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     __UpperCamelCase				(     _a		):


		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							print(
							    ANSI.red(
							        "WARNING! Managing repositories through transformers-cli is deprecated. "
							        "Please use `huggingface-cli` instead."))
							A    =      HfFolder.get_token()
							if token is None:
												print("Not logged in")
												exit(1)
							try:
												A    =      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:
												A    =      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("")

							A    =      whoami(lowerCamelCase__)
							A    =      self.args.organization if self.args.organization is not None else user
							A    =      F"""{namespace}/{self.args.name}"""
							print(F"""You are about to create {ANSI.bold(lowerCamelCase__)}""")

							if not self.args.yes:
												A    =      input("Proceed? [Y/n] ").lower()
												if not (choice == "" or choice == "y" or choice == "yes"):
																	print("Abort")
																	exit()
							try:
												A    =      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("")








"""simple docstring"""




import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:





	class     __UpperCamelCase				:


			@staticmethod
			def       SCREAMING_SNAKE_CASE__     (*__SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  **__SCREAMING_SNAKE_CASE				:	Any):
								pass





@is_pipeline_test
@require_torch_or_tf
@require_vision
class     __UpperCamelCase				(     unittest.TestCase		):
		SCREAMING_SNAKE_CASE              =					MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
		SCREAMING_SNAKE_CASE              =					TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
							A    =      ImageClassificationPipeline(model=__SCREAMING_SNAKE_CASE  ,  image_processor=__SCREAMING_SNAKE_CASE  ,  top_k=2)
							A    =      [
							    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
							    "http://images.cocodataset.org/val2017/000000039769.jpg",
							]
							return image_classifier, examples


		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							A    =      image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png")

							self.assertEqual(
							    __SCREAMING_SNAKE_CASE  ,  [
							        {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							        {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							    ]  ,  )

							import datasets

							A    =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,  "image"  ,  split="test")

							# Accepts URL + PIL.Image + lists
							A    =      image_classifier(
							    [
							        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        # RGBA
							        dataset[0]["file"],
							        # LA
							        dataset[1]["file"],
							        # L
							        dataset[2]["file"],
							    ])
							self.assertEqual(
							    __SCREAMING_SNAKE_CASE  ,  [
							        [
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							        ],
							        [
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							        ],
							        [
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							        ],
							        [
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							        ],
							        [
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							        ],
							    ]  ,  )


		@require_torch
		def       SCREAMING_SNAKE_CASE__     (self				:	str):
							A    =      "hf-internal-testing/tiny-random-vit"
							A    =      pipeline("image-classification"  ,  model=__SCREAMING_SNAKE_CASE)

							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]  ,  )

							A    =      image_classifier(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]  ,  top_k=2  ,  )
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
							    ]  ,  )


		@require_tf
		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
							A    =      "hf-internal-testing/tiny-random-vit"
							A    =      pipeline("image-classification"  ,  model=__SCREAMING_SNAKE_CASE  ,  framework="tf")

							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]  ,  )

							A    =      image_classifier(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]  ,  top_k=2  ,  )
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
							    ]  ,  )


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							A    =      PreTrainedTokenizer()

							# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
							A    =      pipeline(
							    "image-classification"  ,  model="hf-internal-testing/tiny-random-vit"  ,  tokenizer=__SCREAMING_SNAKE_CASE)

							self.assertIs(image_classifier.tokenizer  ,  __SCREAMING_SNAKE_CASE)


		@slow
		@require_torch
		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							# Perceiver is not tested by `run_pipeline_test` properly.
							# That is because the type of feature_extractor and model preprocessor need to be kept
							# in sync, which is not the case in the current design
							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-conv")
							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
							        {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
							        {"score": 0.3_2_1, "label": "tiger cat"},
							        {"score": 0.0_5_0_2, "label": "Egyptian cat"},
							        {"score": 0.0_1_3_7, "label": "crib, cot"},
							        {"score": 0.0_0_7, "label": "radiator"},
							    ]  ,  )

							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-fourier")
							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
							        {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
							        {"score": 0.1_3_0_9, "label": "tiger cat"},
							        {"score": 0.0_7_2_2, "label": "Egyptian cat"},
							        {"score": 0.0_7_0_7, "label": "remote control, remote"},
							        {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
							    ]  ,  )

							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-learned")
							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
							        {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
							        {"score": 0.2_3_6_2, "label": "Egyptian cat"},
							        {"score": 0.1_8_5_6, "label": "tiger cat"},
							        {"score": 0.0_3_2_4, "label": "remote control, remote"},
							        {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
							    ]  ,  )





