
import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
snake_case					:						Any				 =    {"""LayoutLMv2Config""", """LayoutLMv3Config"""}





@is_pipeline_test
class  _snake_case   (	unittest.TestCase							):
			UpperCamelCase__        =       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
			UpperCamelCase__        =       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

			if model_mapping is not None:
						UpperCamelCase__        =       {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
			if tf_model_mapping is not None:
						UpperCamelCase__        =       {
						    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
						}







			@require_torch
			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						List[str]        =	pipeline(
				    task="text-classification"		,  model="hf-internal-testing/tiny-random-distilbert"		,  framework="pt"						)

				__magic_name__						:						Union[str, Any]        =	text_classifier("This is great !"						)
				self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": "LABEL_0", "score": 0.5_04}]						)

				__magic_name__						:						Union[str, Any]        =	text_classifier("This is great !"		,  top_k=2						)
				self.assertEqual(
				    nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]						)

				__magic_name__						:						Tuple        =	text_classifier(["This is great !", "This is bad"]		,  top_k=2						)
				self.assertEqual(
				    nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [
				        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
				        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
				    ]		,  )

				__magic_name__						:						str        =	text_classifier("This is great !"		,  top_k=1						)
				self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": "LABEL_0", "score": 0.5_04}]						)

				# Legacy behavior
				__magic_name__						:						List[Any]        =	text_classifier("This is great !"		,  return_all_scores=__SCREAMING_SNAKE_CASE						)
				self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": "LABEL_0", "score": 0.5_04}]						)

				__magic_name__						:						Optional[Any]        =	text_classifier("This is great !"		,  return_all_scores=__SCREAMING_SNAKE_CASE						)
				self.assertEqual(
				    nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]						)

				__magic_name__						:						Tuple        =	text_classifier(["This is great !", "Something else"]		,  return_all_scores=__SCREAMING_SNAKE_CASE						)
				self.assertEqual(
				    nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [
				        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
				        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
				    ]		,  )

				__magic_name__						:						str        =	text_classifier(["This is great !", "Something else"]		,  return_all_scores=__SCREAMING_SNAKE_CASE						)
				self.assertEqual(
				    nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [
				        {"label": "LABEL_0", "score": 0.5_04},
				        {"label": "LABEL_0", "score": 0.5_04},
				    ]		,  )







			@require_torch
			def      SCREAMING_SNAKE_CASE       (							self						):
				import torch

				__magic_name__						:						Tuple        =	pipeline(
				    task="text-classification"		,  model="hf-internal-testing/tiny-random-distilbert"		,  framework="pt"		,  device=torch.device("cpu"						)		,  )

				__magic_name__						:						Optional[int]        =	text_classifier("This is great !"						)
				self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": "LABEL_0", "score": 0.5_04}]						)







			@require_tf
			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						List[str]        =	pipeline(
				    task="text-classification"		,  model="hf-internal-testing/tiny-random-distilbert"		,  framework="tf"						)

				__magic_name__						:						Union[str, Any]        =	text_classifier("This is great !"						)
				self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": "LABEL_0", "score": 0.5_04}]						)







			@slow
			@require_torch
			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Any        =	pipeline("text-classification"						)

				__magic_name__						:						Optional[Any]        =	text_classifier("This is great !"						)
				self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": "POSITIVE", "score": 1.0}]						)
				__magic_name__						:						Optional[int]        =	text_classifier("This is bad !"						)
				self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": "NEGATIVE", "score": 1.0}]						)
				__magic_name__						:						List[str]        =	text_classifier("Birds are a type of animal"						)
				self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": "POSITIVE", "score": 0.9_88}]						)







			@slow
			@require_tf
			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						List[Any]        =	pipeline("text-classification"		,  framework="tf"						)

				__magic_name__						:						Any        =	text_classifier("This is great !"						)
				self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": "POSITIVE", "score": 1.0}]						)
				__magic_name__						:						List[str]        =	text_classifier("This is bad !"						)
				self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": "NEGATIVE", "score": 1.0}]						)
				__magic_name__						:						Dict        =	text_classifier("Birds are a type of animal"						)
				self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": "POSITIVE", "score": 0.9_88}]						)







			def      SCREAMING_SNAKE_CASE       (							self		,  _a		,  _a		,  _a						):
				__magic_name__						:						Union[str, Any]        =	TextClassificationPipeline(model=__SCREAMING_SNAKE_CASE		,  tokenizer=__SCREAMING_SNAKE_CASE						)
				return text_classifier, ["HuggingFace is in", "This is another test"]







			def      SCREAMING_SNAKE_CASE       (							self		,  _a		,  _a						):
				__magic_name__						:						Optional[Any]        =	text_classifier.model
				# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
				__magic_name__						:						List[Any]        =	"HuggingFace is in"
				__magic_name__						:						Tuple        =	text_classifier(__SCREAMING_SNAKE_CASE						)

				self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": ANY(__SCREAMING_SNAKE_CASE						), "score": ANY(__SCREAMING_SNAKE_CASE						)}]						)
				self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()						)

				__magic_name__						:						List[str]        =	["HuggingFace is in ", "Paris is in France"]
				__magic_name__						:						int        =	text_classifier(__SCREAMING_SNAKE_CASE						)
				self.assertEqual(
				    nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": ANY(__SCREAMING_SNAKE_CASE						), "score": ANY(__SCREAMING_SNAKE_CASE						)}, {"label": ANY(__SCREAMING_SNAKE_CASE						), "score": ANY(__SCREAMING_SNAKE_CASE						)}]		,  )
				self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()						)
				self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()						)

				# Forcing to get all results with `top_k=None`
				# This is NOT the legacy format
				__magic_name__						:						Union[str, Any]        =	text_classifier(__SCREAMING_SNAKE_CASE		,  top_k=__SCREAMING_SNAKE_CASE						)
				__magic_name__						:						Any        =	len(model.config.idalabel.values()						)
				self.assertEqual(
				    nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [[{"label": ANY(__SCREAMING_SNAKE_CASE						), "score": ANY(__SCREAMING_SNAKE_CASE						)}] * N, [{"label": ANY(__SCREAMING_SNAKE_CASE						), "score": ANY(__SCREAMING_SNAKE_CASE						)}] * N]		,  )

				__magic_name__						:						int        =	{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
				__magic_name__						:						Union[str, Any]        =	text_classifier(__SCREAMING_SNAKE_CASE						)
				self.assertEqual(
				    nested_simplify(__SCREAMING_SNAKE_CASE						)		,  {"label": ANY(__SCREAMING_SNAKE_CASE						), "score": ANY(__SCREAMING_SNAKE_CASE						)}		,  )
				self.assertTrue(outputs["label"] in model.config.idalabel.values()						)

				# This might be used a text pair, but tokenizer + pipe interaction
				# makes it hard to understand that it's not using the pair properly
				# https://github.com/huggingface/transformers/issues/17305
				# We disabled this usage instead as it was outputting wrong outputs.
				__magic_name__						:						Union[str, Any]        =	[["HuggingFace is in ", "Paris is in France"]]
				with self.assertRaises(__SCREAMING_SNAKE_CASE						):
					text_classifier(__SCREAMING_SNAKE_CASE						)

				# This used to be valid for doing text pairs
				# We're keeping it working because of backward compatibility
				__magic_name__						:						Union[str, Any]        =	text_classifier([[["HuggingFace is in ", "Paris is in France"]]]						)
				self.assertEqual(
				    nested_simplify(__SCREAMING_SNAKE_CASE						)		,  [{"label": ANY(__SCREAMING_SNAKE_CASE						), "score": ANY(__SCREAMING_SNAKE_CASE						)}]		,  )
				self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()						)

# Copyright 2021 The HuggingFace 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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def 	__A		(      ):



 """simple docstring"""


 __a							=	ArgumentParser("Accelerate CLI tool"				,					usage="accelerate <command> [<args>]"				,					allow_abbrev=_A       )
 __a							=	parser.add_subparsers(help="accelerate command helpers"       )

 # Register commands
 get_config_parser(subparsers=_A       )
 env_command_parser(subparsers=_A       )
 launch_command_parser(subparsers=_A       )
 tpu_command_parser(subparsers=_A       )
 test_command_parser(subparsers=_A       )

 # Let's go
 __a							=	parser.parse_args()

 if not hasattr(_A				,					"func"       ):
  parser.print_help()
  exit(1       )

 # Run
 args.func(_A       )


if __name__ == "__main__":
  main()


