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.
A_      =      {"LayoutLMv2Config", "LayoutLMv3Config"}






@is_pipeline_test
class      __lowercase					(    unittest.TestCase    ):
						lowercase					 =     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
						lowercase					 =     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

						@require_torch
						def        __a		(	self				:      int   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		pipeline(
									    task='''text-classification'''							,							model='''hf-internal-testing/tiny-random-distilbert'''							,							framework='''pt'''   )

									lowercase							=		text_classifier('''This is great !'''   )
									self.assertEqual(nested_simplify(__lowerCamelCase   )							,							[{'''label''': '''LABEL_0''', '''score''': 0.504}]   )

									lowercase							=		text_classifier('''This is great !'''							,							top_k=2   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]   )

									lowercase							=		text_classifier(['''This is great !''', '''This is bad''']							,							top_k=2   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[
									        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
									        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
									    ]							,							)

									lowercase							=		text_classifier('''This is great !'''							,							top_k=1   )
									self.assertEqual(nested_simplify(__lowerCamelCase   )							,							[{'''label''': '''LABEL_0''', '''score''': 0.504}]   )

									# Legacy behavior
									lowercase							=		text_classifier('''This is great !'''							,							return_all_scores=__lowerCamelCase   )
									self.assertEqual(nested_simplify(__lowerCamelCase   )							,							[{'''label''': '''LABEL_0''', '''score''': 0.504}]   )

									lowercase							=		text_classifier('''This is great !'''							,							return_all_scores=__lowerCamelCase   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[[{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]]   )

									lowercase							=		text_classifier(['''This is great !''', '''Something else''']							,							return_all_scores=__lowerCamelCase   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[
									        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
									        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
									    ]							,							)

									lowercase							=		text_classifier(['''This is great !''', '''Something else''']							,							return_all_scores=__lowerCamelCase   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[
									        {'''label''': '''LABEL_0''', '''score''': 0.504},
									        {'''label''': '''LABEL_0''', '''score''': 0.504},
									    ]							,							)

						@require_torch
						def        __a		(	self				:      int   )							->		Optional[int]:


									'''simple docstring'''

									import torch

									lowercase							=		pipeline(
									    task='''text-classification'''							,							model='''hf-internal-testing/tiny-random-distilbert'''							,							framework='''pt'''							,							device=torch.device('''cpu'''   )							,							)

									lowercase							=		text_classifier('''This is great !'''   )
									self.assertEqual(nested_simplify(__lowerCamelCase   )							,							[{'''label''': '''LABEL_0''', '''score''': 0.504}]   )

						@require_tf
						def        __a		(	self				:      Optional[Any]   )							->		Dict:


									'''simple docstring'''

									lowercase							=		pipeline(
									    task='''text-classification'''							,							model='''hf-internal-testing/tiny-random-distilbert'''							,							framework='''tf'''   )

									lowercase							=		text_classifier('''This is great !'''   )
									self.assertEqual(nested_simplify(__lowerCamelCase   )							,							[{'''label''': '''LABEL_0''', '''score''': 0.504}]   )

						@slow
						@require_torch
						def        __a		(	self				:      str   )							->		int:


									'''simple docstring'''

									lowercase							=		pipeline('''text-classification'''   )

									lowercase							=		text_classifier('''This is great !'''   )
									self.assertEqual(nested_simplify(__lowerCamelCase   )							,							[{'''label''': '''POSITIVE''', '''score''': 1.0}]   )
									lowercase							=		text_classifier('''This is bad !'''   )
									self.assertEqual(nested_simplify(__lowerCamelCase   )							,							[{'''label''': '''NEGATIVE''', '''score''': 1.0}]   )
									lowercase							=		text_classifier('''Birds are a type of animal'''   )
									self.assertEqual(nested_simplify(__lowerCamelCase   )							,							[{'''label''': '''POSITIVE''', '''score''': 0.988}]   )

						@slow
						@require_tf
						def        __a		(	self				:      List[str]   )							->		str:


									'''simple docstring'''

									lowercase							=		pipeline('''text-classification'''							,							framework='''tf'''   )

									lowercase							=		text_classifier('''This is great !'''   )
									self.assertEqual(nested_simplify(__lowerCamelCase   )							,							[{'''label''': '''POSITIVE''', '''score''': 1.0}]   )
									lowercase							=		text_classifier('''This is bad !'''   )
									self.assertEqual(nested_simplify(__lowerCamelCase   )							,							[{'''label''': '''NEGATIVE''', '''score''': 1.0}]   )
									lowercase							=		text_classifier('''Birds are a type of animal'''   )
									self.assertEqual(nested_simplify(__lowerCamelCase   )							,							[{'''label''': '''POSITIVE''', '''score''': 0.988}]   )

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Optional[Any]   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		TextClassificationPipeline(model=__lowerCamelCase							,							tokenizer=__lowerCamelCase   )
									return text_classifier, ["HuggingFace is in", "This is another test"]



						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		text_classifier.model
									# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
									lowercase							=		'''HuggingFace is in'''
									lowercase							=		text_classifier(__lowerCamelCase   )

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

									lowercase							=		['''HuggingFace is in ''', '''Paris is in France''']
									lowercase							=		text_classifier(__lowerCamelCase   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[{'''label''': ANY(__lowerCamelCase   ), '''score''': ANY(__lowerCamelCase   )}, {'''label''': ANY(__lowerCamelCase   ), '''score''': ANY(__lowerCamelCase   )}]							,							)
									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
									lowercase							=		text_classifier(__lowerCamelCase							,							top_k=__lowerCamelCase   )
									lowercase							=		len(model.config.idalabel.values()   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							[[{'''label''': ANY(__lowerCamelCase   ), '''score''': ANY(__lowerCamelCase   )}] * N, [{'''label''': ANY(__lowerCamelCase   ), '''score''': ANY(__lowerCamelCase   )}] * N]							,							)

									lowercase							=		{'''text''': '''HuggingFace is in ''', '''text_pair''': '''Paris is in France'''}
									lowercase							=		text_classifier(__lowerCamelCase   )
									self.assertEqual(
									    nested_simplify(__lowerCamelCase   )							,							{'''label''': ANY(__lowerCamelCase   ), '''score''': ANY(__lowerCamelCase   )}							,							)
									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.
									lowercase							=		[['''HuggingFace is in ''', '''Paris is in France''']]
									with self.assertRaises(__lowerCamelCase   ):
												text_classifier(__lowerCamelCase   )

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






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
A_      =      logging.get_logger(__name__)






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







			lowercase							=		torch.load(UpperCAmelCase,		map_location='''cpu'''       )
			if "model" in sd.keys():
						lowercase							=		torch.load(UpperCAmelCase,		map_location='''cpu'''       )['''model''']

			# pop unnecessary weights
			lowercase							=		[
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(UpperCAmelCase       )

			lowercase							=		{
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowercase							=		sd.pop(UpperCAmelCase       )

			lowercase							=		list(sd.keys()       )
			for key in keys:
						if ".qkv_proj." in key:
									lowercase							=		sd[key]
									# We split QKV in separate Q,K,V

									lowercase							=		key.replace('''.qkv_proj.''',		'''.q_proj.'''       )
									lowercase							=		key.replace('''.qkv_proj.''',		'''.k_proj.'''       )
									lowercase							=		key.replace('''.qkv_proj.''',		'''.v_proj.'''       )

									lowercase							=		value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowercase		,lowercase		,lowercase							=		torch.split(UpperCAmelCase,		depth // 3,		dim=0       )

									lowercase							=		q
									lowercase							=		k
									lowercase							=		v
									del sd[key]

			return sd






@torch.no_grad()
def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None       )->       int:



			"""simple docstring"""







			lowercase							=		load_checkpoint(UpperCAmelCase       )

			if config is not None:
						lowercase							=		OPTConfig.from_pretrained(UpperCAmelCase       )
			else:
						lowercase							=		OPTConfig()

			lowercase							=		OPTModel(UpperCAmelCase       ).half().eval()
			model.load_state_dict(UpperCAmelCase       )

			# Check results
			Path(UpperCAmelCase       ).mkdir(exist_ok=UpperCAmelCase       )
			model.save_pretrained(UpperCAmelCase       )


if __name__ == "__main__":
						A_      =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--fairseq_path",
						    type=str,
						    help=(
						        "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
						        " https://huggingface.co/models?other=opt_metasq"
						    ),
						)
						parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
						A_      =      parser.parse_args()
						convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





