






"""simple docstring"""



import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Union[str, Any]=None      )		->Optional[Any]:
						# set parameter of one layer
						assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
						A__						:  List[str]		  =  nn.Parameter(UpperCAmelCase__      )
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
												A__						:  Tuple		  =  nn.Parameter(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Any      )		->str:
						# set torch weights for 1-to-1 comparison
						A__						:  Optional[Any]		  =  np.asarray(weights[0]      )
						A__						:  Tuple		  =  np.asarray(weights[1]      )
						A__						:  Dict		  =  np.asarray(weights[2]      )

						set_param(
						    torch_layer.self_attention.query_key,						torch.tensor(UpperCAmelCase__      ).transpose(1,						2      ).contiguous().view(-1,						UpperCAmelCase__      ),						)
						set_param(
						    torch_layer.self_attention.value,						torch.tensor(UpperCAmelCase__      ).transpose(1,						2      ).contiguous().view(-1,						UpperCAmelCase__      ),						)
						set_param(
						    torch_layer.output.dense,						torch.tensor(UpperCAmelCase__      ).view(-1,						UpperCAmelCase__      ).contiguous().transpose(0,						1      ),						)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Union[str, Any]      )		->Optional[int]:
						# set torch weights for 1-to-1 comparison
						A__						:  List[str]		  =  np.asarray(weights[0]      )
						A__						:  int		  =  np.asarray(weights[1]      )
						A__						:  Optional[int]		  =  np.asarray(weights[2]      )
						A__						:  List[str]		  =  np.asarray(weights[3]      )

						set_param(
						    torch_layer.self_attention.query,						torch.tensor(UpperCAmelCase__      ).transpose(1,						2      ).contiguous().view(-1,						UpperCAmelCase__      ),						)
						set_param(
						    torch_layer.self_attention.key,						torch.tensor(UpperCAmelCase__      ).transpose(1,						2      ).contiguous().view(-1,						UpperCAmelCase__      ),						)
						set_param(
						    torch_layer.self_attention.value,						torch.tensor(UpperCAmelCase__      ).transpose(1,						2      ).contiguous().view(-1,						UpperCAmelCase__      ),						)
						set_param(
						    torch_layer.output.dense,						torch.tensor(UpperCAmelCase__      ).view(-1,						UpperCAmelCase__      ).contiguous().transpose(0,						1      ),						)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Optional[Any]      )		->List[Any]:
						# layernorm 1
						A__						:  List[Any]		  =  weights[0][0][0]
						A__						:  List[str]		  =  np.asarray(layer_norm_a[0]      )
						A__						:  Dict		  =  np.asarray(layer_norm_a[1]      )
						set_param(
						    torch_block.attention.layer_norm,						torch.tensor(UpperCAmelCase__      ),						torch.tensor(UpperCAmelCase__      ),						)

						# lsh weights + output
						A__						:  Dict		  =  weights[0][1]
						if len(UpperCAmelCase__      ) < 4:
												set_layer_weights_in_torch_lsh(UpperCAmelCase__,						torch_block.attention,						UpperCAmelCase__      )
						else:
												set_layer_weights_in_torch_local(UpperCAmelCase__,						torch_block.attention,						UpperCAmelCase__      )

						# intermediate weighs
						A__						:  int		  =  weights[2][0][1][2]

						# Chunked Feed Forward
						if len(UpperCAmelCase__      ) == 4:
												A__						:  str		  =  intermediate_weights[2]

						# layernorm 2
						A__						:  Any		  =  np.asarray(intermediate_weights[0][0]      )
						A__						:  Any		  =  np.asarray(intermediate_weights[0][1]      )
						set_param(
						    torch_block.feed_forward.layer_norm,						torch.tensor(UpperCAmelCase__      ),						torch.tensor(UpperCAmelCase__      ),						)

						# intermediate dense
						A__						:  str		  =  np.asarray(intermediate_weights[1][0]      )
						A__						:  Any		  =  np.asarray(intermediate_weights[1][1]      )
						set_param(
						    torch_block.feed_forward.dense.dense,						torch.tensor(UpperCAmelCase__      ).transpose(0,						1      ).contiguous(),						torch.tensor(UpperCAmelCase__      ),						)

						# intermediate out
						A__						:  str		  =  np.asarray(intermediate_weights[4][0]      )
						A__						:  Dict		  =  np.asarray(intermediate_weights[4][1]      )
						set_param(
						    torch_block.feed_forward.output.dense,						torch.tensor(UpperCAmelCase__      ).transpose(0,						1      ).contiguous(),						torch.tensor(UpperCAmelCase__      ),						)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					List[Any]      )		->Tuple:
						# reformer model
						A__						:  str		  =  torch_model.reformer

						# word embeds
						A__						:  Union[str, Any]		  =  np.asarray(weights[1]      )
						set_param(
						    torch_model_reformer.embeddings.word_embeddings,						torch.tensor(UpperCAmelCase__      ),						)

						if isinstance(weights[3],						UpperCAmelCase__      ):
												A__						:  List[str]		  =  torch_model_reformer.embeddings.position_embeddings
												for emb_idx in range(len(position_embeddings.weights      )      ):
																		A__						:  Tuple		  =  np.asarray(weights[3][emb_idx][0]      )
																		assert (
																		    position_embeddings.weights[emb_idx].shape == emb_weights.shape
																		), F'{position_embeddings[emb_idx]} emb does not match'
																		A__						:  int		  =  nn.Parameter(torch.tensor(UpperCAmelCase__      )      )

						A__						:  int		  =  weights[5]
						assert len(torch_model_reformer.encoder.layers      ) * 4 == len(
						    UpperCAmelCase__      ), "HF and trax model do not have the same number of layers"
						for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers      ):
												A__						:  Any		  =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
												set_block_weights_in_torch(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )

						# output layer norm
						A__						:  Union[str, Any]		  =  np.asarray(weights[7][0]      )
						A__						:  Dict		  =  np.asarray(weights[7][1]      )
						set_param(
						    torch_model_reformer.encoder.layer_norm,						torch.tensor(UpperCAmelCase__      ),						torch.tensor(UpperCAmelCase__      ),						)

						# output embeddings
						A__						:  Union[str, Any]		  =  np.asarray(weights[9][0]      )
						A__						:  int		  =  np.asarray(weights[9][1]      )
						set_param(
						    torch_model.lm_head.decoder,						torch.tensor(UpperCAmelCase__      ).transpose(0,						1      ).contiguous(),						torch.tensor(UpperCAmelCase__      ),						)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					List[str]      )		->List[str]:
						# Initialise PyTorch model
						A__						:  Union[str, Any]		  =  ReformerConfig.from_json_file(UpperCAmelCase__      )
						print(F'Building PyTorch model from configuration: {config}'      )
						A__						:  List[Any]		  =  ReformerModelWithLMHead(UpperCAmelCase__      )

						with open(UpperCAmelCase__,						"""rb"""      ) as f:
												A__						:  Union[str, Any]		  =  pickle.load(UpperCAmelCase__      )["""weights"""]

						set_model_weights_in_torch(UpperCAmelCase__,						UpperCAmelCase__,						config.hidden_size      )

						# Save pytorch-model
						print(F'Save PyTorch model to {pytorch_dump_path}'      )
						torch.save(model.state_dict(),						UpperCAmelCase__      )


if __name__ == "__main__":
						A_    =							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--trax_model_pkl_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 Reformer model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						A_    =							parser.parse_args()
						convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)














"""simple docstring"""



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 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):
							snake_case_     =							MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							snake_case_     =							TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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


							@require_torch
							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Tuple		  =  pipeline(
													    task="""text-classification"""			,    model="""hf-internal-testing/tiny-random-distilbert"""			,    framework="""pt"""							)

													A__						:  Dict		  =  text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """LABEL_0""", """score""": 0.504}]							)

													A__						:  Optional[int]		  =  text_classifier("""This is great !"""			,    top_k=2							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]							)

													A__						:  Any		  =  text_classifier(["""This is great !""", """This is bad"""]			,    top_k=2							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [
													        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
													        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
													    ]			,    )

													A__						:  Optional[Any]		  =  text_classifier("""This is great !"""			,    top_k=1							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """LABEL_0""", """score""": 0.504}]							)

													# Legacy behavior
													A__						:  str		  =  text_classifier("""This is great !"""			,    return_all_scores=snake_case							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """LABEL_0""", """score""": 0.504}]							)

													A__						:  int		  =  text_classifier("""This is great !"""			,    return_all_scores=snake_case							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]]							)

													A__						:  str		  =  text_classifier(["""This is great !""", """Something else"""]			,    return_all_scores=snake_case							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [
													        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
													        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
													    ]			,    )

													A__						:  Optional[int]		  =  text_classifier(["""This is great !""", """Something else"""]			,    return_all_scores=snake_case							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    [
													        {"""label""": """LABEL_0""", """score""": 0.504},
													        {"""label""": """LABEL_0""", """score""": 0.504},
													    ]			,    )


							@require_torch
							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													import torch

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

													A__						:  List[str]		  =  text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """LABEL_0""", """score""": 0.504}]							)


							@require_tf
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													A__						:  str		  =  pipeline(
													    task="""text-classification"""			,    model="""hf-internal-testing/tiny-random-distilbert"""			,    framework="""tf"""							)

													A__						:  Optional[Any]		  =  text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """LABEL_0""", """score""": 0.504}]							)


							@slow
							@require_torch
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  pipeline("""text-classification"""							)

													A__						:  Optional[int]		  =  text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """POSITIVE""", """score""": 1.0}]							)
													A__						:  Optional[Any]		  =  text_classifier("""This is bad !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """NEGATIVE""", """score""": 1.0}]							)
													A__						:  Dict		  =  text_classifier("""Birds are a type of animal"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """POSITIVE""", """score""": 0.988}]							)


							@slow
							@require_tf
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  pipeline("""text-classification"""			,    framework="""tf"""							)

													A__						:  Dict		  =  text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """POSITIVE""", """score""": 1.0}]							)
													A__						:  Optional[int]		  =  text_classifier("""This is bad !"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """NEGATIVE""", """score""": 1.0}]							)
													A__						:  Tuple		  =  text_classifier("""Birds are a type of animal"""							)
													self.assertEqual(nested_simplify(snake_case							)			,    [{"""label""": """POSITIVE""", """score""": 0.988}]							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Any			,    snake_case      :						List[Any]			,    snake_case      :						Any							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  TextClassificationPipeline(model=snake_case			,    tokenizer=snake_case							)
													return text_classifier, ["HuggingFace is in", "This is another test"]


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						Union[str, Any]			,    snake_case      :						Dict							):
													'''simple docstring'''





													A__						:  List[str]		  =  text_classifier.model
													# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
													A__						:  int		  =  """HuggingFace is in"""
													A__						:  List[Any]		  =  text_classifier(snake_case							)

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

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

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

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






