




'''simple docstring'''







import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    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 			_a  (	unittest.TestCase ):
       __a				:     int   				=					MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       __a				:     Any   				=					TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              __a				:     Any   				=					{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              __a				:     Tuple   				=					{
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }
       def     A      (	self						:	Optional[int]   ,				lowercase						:	Optional[Any]   ,				lowercase						:	Optional[Any]   ,				lowercase						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		ZeroShotClassificationPipeline(
               model=_a   ,				tokenizer=_a   ,				candidate_labels=['''polics''', '''health''']				)
           return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
       def     A      (	self						:	List[Any]   ,				lowercase						:	Optional[int]   ,				lowercase						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		classifier('''Who are you voting for in 2020?'''   ,				candidate_labels='''politics'''				)
           self.assertEqual(_a   ,				{'''sequence''': ANY(_a				), '''labels''': [ANY(_a				)], '''scores''': [ANY(_a				)]}				)

           # No kwarg
           UpperCAmelCase			       =		classifier('''Who are you voting for in 2020?'''   ,				['''politics''']				)
           self.assertEqual(_a   ,				{'''sequence''': ANY(_a				), '''labels''': [ANY(_a				)], '''scores''': [ANY(_a				)]}				)

           UpperCAmelCase			       =		classifier('''Who are you voting for in 2020?'''   ,				candidate_labels=['''politics''']				)
           self.assertEqual(_a   ,				{'''sequence''': ANY(_a				), '''labels''': [ANY(_a				)], '''scores''': [ANY(_a				)]}				)

           UpperCAmelCase			       =		classifier('''Who are you voting for in 2020?'''   ,				candidate_labels='''politics, public health'''				)
           self.assertEqual(
               _a   ,				{'''sequence''': ANY(_a				), '''labels''': [ANY(_a				), ANY(_a				)], '''scores''': [ANY(_a				), ANY(_a				)]}				)
           self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']				)				)   ,				1.0				)

           UpperCAmelCase			       =		classifier('''Who are you voting for in 2020?'''   ,				candidate_labels=['''politics''', '''public health''']				)
           self.assertEqual(
               _a   ,				{'''sequence''': ANY(_a				), '''labels''': [ANY(_a				), ANY(_a				)], '''scores''': [ANY(_a				), ANY(_a				)]}				)
           self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']				)				)   ,				1.0				)

           UpperCAmelCase			       =		classifier(
               '''Who are you voting for in 2020?'''   ,				candidate_labels='''politics'''   ,				hypothesis_template='''This text is about {}'''				)
           self.assertEqual(_a   ,				{'''sequence''': ANY(_a				), '''labels''': [ANY(_a				)], '''scores''': [ANY(_a				)]}				)

           # https://github.com/huggingface/transformers/issues/13846
           UpperCAmelCase			       =		classifier(['''I am happy''']   ,				['''positive''', '''negative''']				)
           self.assertEqual(
               _a   ,				[
                   {'''sequence''': ANY(_a				), '''labels''': [ANY(_a				), ANY(_a				)], '''scores''': [ANY(_a				), ANY(_a				)]}
                   for i in range(1				)
               ]   ,				)
           UpperCAmelCase			       =		classifier(['''I am happy''', '''I am sad''']   ,				['''positive''', '''negative''']				)
           self.assertEqual(
               _a   ,				[
                   {'''sequence''': ANY(_a				), '''labels''': [ANY(_a				), ANY(_a				)], '''scores''': [ANY(_a				), ANY(_a				)]}
                   for i in range(2				)
               ]   ,				)

           with self.assertRaises(_a				):
               classifier(''''''   ,				candidate_labels='''politics'''				)

           with self.assertRaises(_a				):
               classifier(_a   ,				candidate_labels='''politics'''				)

           with self.assertRaises(_a				):
               classifier('''Who are you voting for in 2020?'''   ,				candidate_labels=''''''				)

           with self.assertRaises(_a				):
               classifier('''Who are you voting for in 2020?'''   ,				candidate_labels=_a				)

           with self.assertRaises(_a				):
               classifier(
                   '''Who are you voting for in 2020?'''   ,				candidate_labels='''politics'''   ,				hypothesis_template='''Not formatting template'''   ,				)

           with self.assertRaises(_a				):
               classifier(
                   '''Who are you voting for in 2020?'''   ,				candidate_labels='''politics'''   ,				hypothesis_template=_a   ,				)

           self.run_entailment_id(_a				)
       def     A      (	self						:	str   ,				lowercase						:	Pipeline				):



           '''simple docstring'''

           UpperCAmelCase			       =		zero_shot_classifier.model.config
           UpperCAmelCase			       =		config.labelaid
           UpperCAmelCase			       =		zero_shot_classifier.entailment_id

           UpperCAmelCase			       =		{'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
           self.assertEqual(zero_shot_classifier.entailment_id   ,				-1				)

           UpperCAmelCase			       =		{'entailment': 0, 'neutral': 1, 'contradiction': 2}
           self.assertEqual(zero_shot_classifier.entailment_id   ,				0				)

           UpperCAmelCase			       =		{'ENTAIL': 0, 'NON-ENTAIL': 1}
           self.assertEqual(zero_shot_classifier.entailment_id   ,				0				)

           UpperCAmelCase			       =		{'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
           self.assertEqual(zero_shot_classifier.entailment_id   ,				2				)

           UpperCAmelCase			       =		original_labelaid
           self.assertEqual(_a   ,				zero_shot_classifier.entailment_id				)
       @require_torch
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		pipeline(
               '''zero-shot-classification'''   ,				model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''   ,				framework='''pt'''   ,				)
           # There was a regression in 4.10 for this
           # Adding a test so we don't make the mistake again.
           # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
           zero_shot_classifier(
               '''Who are you voting for in 2020?''' * 100   ,				candidate_labels=['''politics''', '''public health''', '''science''']				)
       @require_torch
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		pipeline(
               '''zero-shot-classification'''   ,				model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''   ,				framework='''pt'''   ,				)
           UpperCAmelCase			       =		zero_shot_classifier(
               '''Who are you voting for in 2020?'''   ,				candidate_labels=['''politics''', '''public health''', '''science''']				)

           self.assertEqual(
               nested_simplify(_a				)   ,				{
                   '''sequence''': '''Who are you voting for in 2020?''',
                   '''labels''': ['''science''', '''public health''', '''politics'''],
                   '''scores''': [0.333, 0.333, 0.333],
               }   ,				)
       @require_tf
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		pipeline(
               '''zero-shot-classification'''   ,				model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''   ,				framework='''tf'''   ,				)
           UpperCAmelCase			       =		zero_shot_classifier(
               '''Who are you voting for in 2020?'''   ,				candidate_labels=['''politics''', '''public health''', '''science''']				)

           self.assertEqual(
               nested_simplify(_a				)   ,				{
                   '''sequence''': '''Who are you voting for in 2020?''',
                   '''labels''': ['''science''', '''public health''', '''politics'''],
                   '''scores''': [0.333, 0.333, 0.333],
               }   ,				)
       @slow
       @require_torch
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		pipeline('''zero-shot-classification'''   ,				model='''roberta-large-mnli'''   ,				framework='''pt'''				)
           UpperCAmelCase			       =		zero_shot_classifier(
               '''Who are you voting for in 2020?'''   ,				candidate_labels=['''politics''', '''public health''', '''science''']				)

           self.assertEqual(
               nested_simplify(_a				)   ,				{
                   '''sequence''': '''Who are you voting for in 2020?''',
                   '''labels''': ['''politics''', '''public health''', '''science'''],
                   '''scores''': [0.976, 0.015, 0.009],
               }   ,				)
           UpperCAmelCase			       =		zero_shot_classifier(
               '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
               ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
               ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
               ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
               ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
               ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
               ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
               ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
               ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
               ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
               ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
               ''' large and limited training data.'''   ,				candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']   ,				multi_label=_a   ,				)
           self.assertEqual(
               nested_simplify(_a				)   ,				{
                   '''sequence''': (
                       '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
                       ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
                       ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
                       ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
                       ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
                       ''' superior in quality while being more parallelizable and requiring significantly less time to'''
                       ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
                       ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
                       ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
                       ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
                       ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
                       ''' other tasks by applying it successfully to English constituency parsing both with large and'''
                       ''' limited training data.'''
                   ),
                   '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
                   '''scores''': [0.817, 0.713, 0.018, 0.018],
               }   ,				)







       @slow
       @require_tf
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		pipeline('''zero-shot-classification'''   ,				model='''roberta-large-mnli'''   ,				framework='''tf'''				)
           UpperCAmelCase			       =		zero_shot_classifier(
               '''Who are you voting for in 2020?'''   ,				candidate_labels=['''politics''', '''public health''', '''science''']				)

           self.assertEqual(
               nested_simplify(_a				)   ,				{
                   '''sequence''': '''Who are you voting for in 2020?''',
                   '''labels''': ['''politics''', '''public health''', '''science'''],
                   '''scores''': [0.976, 0.015, 0.009],
               }   ,				)
           UpperCAmelCase			       =		zero_shot_classifier(
               '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
               ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
               ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
               ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
               ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
               ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
               ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
               ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
               ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
               ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
               ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
               ''' large and limited training data.'''   ,				candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']   ,				multi_label=_a   ,				)
           self.assertEqual(
               nested_simplify(_a				)   ,				{
                   '''sequence''': (
                       '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
                       ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
                       ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
                       ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
                       ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
                       ''' superior in quality while being more parallelizable and requiring significantly less time to'''
                       ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
                       ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
                       ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
                       ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
                       ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
                       ''' other tasks by applying it successfully to English constituency parsing both with large and'''
                       ''' limited training data.'''
                   ),
                   '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
                   '''scores''': [0.817, 0.713, 0.018, 0.018],
               }   ,				)






'''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 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    str ,	__a      :    Optional[Any]=None ):






   """simple docstring"""

   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   _a				:	str			=   nn.Parameter(__a )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      _a				:	Any			=   nn.Parameter(__a )





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Optional[Any] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Tuple			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	Dict			=   np.asarray(weights[2] )

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





def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    Optional[int] ,	__a      :    List[str] ):






   """simple docstring"""

   _a				:	Dict			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	str			=   np.asarray(weights[2] )
   _a				:	int			=   np.asarray(weights[3] )

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





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Any ,	__a      :    Optional[Any] ):






   """simple docstring"""

   _a				:	List[str]			=   weights[0][0][0]
   _a				:	List[Any]			=   np.asarray(layer_norm_a[0] )
   _a				:	List[str]			=   np.asarray(layer_norm_a[1] )
   set_param(
       torch_block.attention.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # lsh weights + output
   _a				:	List[str]			=   weights[0][1]
   if len(__a ) < 4:
      set_layer_weights_in_torch_lsh(__a ,	torch_block.attention ,	__a )
   else:
      set_layer_weights_in_torch_local(__a ,	torch_block.attention ,	__a )

   # intermediate weighs
   _a				:	Optional[Any]			=   weights[2][0][1][2]

   # Chunked Feed Forward
   if len(__a ) == 4:
      _a				:	Union[str, Any]			=   intermediate_weights[2]

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

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

   # intermediate out
   _a				:	Optional[int]			=   np.asarray(intermediate_weights[4][0] )
   _a				:	int			=   np.asarray(intermediate_weights[4][1] )
   set_param(
       torch_block.feed_forward.output.dense ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)





def 					UpperCAmelCase_		(__a      :    Dict ,	__a      :    Dict ,	__a      :    List[Any] ):






   """simple docstring"""

   _a				:	Optional[int]			=   torch_model.reformer

   # word embeds
   _a				:	Tuple			=   np.asarray(weights[1] )
   set_param(
       torch_model_reformer.embeddings.word_embeddings ,	torch.tensor(__a ) ,	)

   if isinstance(weights[3] ,	__a ):
      _a				:	Any			=   torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights ) ):
         _a				:	List[Any]			=   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				:	Any			=   nn.Parameter(torch.tensor(__a ) )

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

   # output layer norm
   _a				:	Optional[Any]			=   np.asarray(weights[7][0] )
   _a				:	int			=   np.asarray(weights[7][1] )
   set_param(
       torch_model_reformer.encoder.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # output embeddings
   _a				:	List[str]			=   np.asarray(weights[9][0] )
   _a				:	int			=   np.asarray(weights[9][1] )
   set_param(
       torch_model.lm_head.decoder ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)





def 					UpperCAmelCase_		(__a      :    Tuple ,	__a      :    Optional[Any] ,	__a      :    Dict ):






   """simple docstring"""

   _a				:	List[Any]			=   ReformerConfig.from_json_file(__a )
   print(f"""Building PyTorch model from configuration: {config}""" )
   _a				:	int			=   ReformerModelWithLMHead(__a )

   with open(__a ,	'rb' ) as f:
      _a				:	Optional[Any]			=   pickle.load(__a )['weights']

   set_model_weights_in_torch(__a ,	__a ,	config.hidden_size )

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""" )
   torch.save(model.state_dict() ,	__a )


if __name__ == "__main__":
  __lowerCAmelCase         =    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."""
  )
  __lowerCAmelCase         =    parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



