



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.
_lowercase     							=						{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class 				__A					( unittest.TestCase      ):
			UpperCamelCase					:List[Any]     =			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
			UpperCamelCase					:Union[str, Any]     =			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

			if model_mapping is not None:
						UpperCamelCase					:Dict     =			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
			if tf_model_mapping is not None:
						UpperCamelCase					:List[str]     =			{
						    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
						}





			@require_torch
			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[Any]								=		pipeline(
						    task="""text-classification"""							,    model="""hf-internal-testing/tiny-random-distilbert"""							,    framework="""pt"""				)

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

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

						lowerCamelCase__ :      List[str]								=		text_classifier(["""This is great !""", """This is bad"""]							,    top_k=2				)
						self.assertEqual(
						    nested_simplify(__magic_name__				)							,    [
						        [{"""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}],
						    ]							,    )

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

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

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

						lowerCamelCase__ :      Union[str, Any]								=		text_classifier(["""This is great !""", """Something else"""]							,    return_all_scores=__magic_name__				)
						self.assertEqual(
						    nested_simplify(__magic_name__				)							,    [
						        [{"""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}],
						    ]							,    )

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





			@require_torch
			def        _snake_case    (self				):
						import torch

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

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





			@require_tf
			def        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		pipeline(
						    task="""text-classification"""							,    model="""hf-internal-testing/tiny-random-distilbert"""							,    framework="""tf"""				)

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





			@slow
			@require_torch
			def        _snake_case    (self				):
						lowerCamelCase__ :      str								=		pipeline("""text-classification"""				)

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





			@slow
			@require_tf
			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[Any]								=		pipeline("""text-classification"""							,    framework="""tf"""				)

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





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      List[str]								=		TextClassificationPipeline(model=__magic_name__							,    tokenizer=__magic_name__				)
						return text_classifier, ["HuggingFace is in", "This is another test"]





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Dict								=		text_classifier.model
						# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
						lowerCamelCase__ :      int								=		"""HuggingFace is in"""
						lowerCamelCase__ :      Optional[Any]								=		text_classifier(__magic_name__				)

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

						lowerCamelCase__ :      List[Any]								=		["""HuggingFace is in """, """Paris is in France"""]
						lowerCamelCase__ :      Dict								=		text_classifier(__magic_name__				)
						self.assertEqual(
						    nested_simplify(__magic_name__				)							,    [{"""label""": ANY(__magic_name__				), """score""": ANY(__magic_name__				)}, {"""label""": ANY(__magic_name__				), """score""": ANY(__magic_name__				)}]							,    )
						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
						lowerCamelCase__ :      Any								=		text_classifier(__magic_name__							,    top_k=__magic_name__				)
						lowerCamelCase__ :      Dict								=		len(model.config.idalabel.values()				)
						self.assertEqual(
						    nested_simplify(__magic_name__				)							,    [[{"""label""": ANY(__magic_name__				), """score""": ANY(__magic_name__				)}] * N, [{"""label""": ANY(__magic_name__				), """score""": ANY(__magic_name__				)}] * N]							,    )

						lowerCamelCase__ :      Tuple								=		{"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
						lowerCamelCase__ :      Dict								=		text_classifier(__magic_name__				)
						self.assertEqual(
						    nested_simplify(__magic_name__				)							,    {"""label""": ANY(__magic_name__				), """score""": ANY(__magic_name__				)}							,    )
						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.
						lowerCamelCase__ :      Union[str, Any]								=		[["""HuggingFace is in """, """Paris is in France"""]]
						with self.assertRaises(__magic_name__				):
									text_classifier(__magic_name__				)

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








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Optional[Any] )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		TaConfig.from_json_file(UpperCamelCase )
			print(f"Building PyTorch model from configuration: {config}" )
			lowerCamelCase__ :      Optional[int]								=		TaForConditionalGeneration(UpperCamelCase )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )

			# Save pytorch-model
			print(f"Save PyTorch model to {pytorch_dump_path}" )
			model.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
				_lowercase     							=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
				)
				parser.add_argument(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				_lowercase     							=						parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



