
"""simple docstring"""

import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_PRETRAINING_MAPPING,
       MobileBertForMaskedLM,
       MobileBertForMultipleChoice,
       MobileBertForNextSentencePrediction,
       MobileBertForPreTraining,
       MobileBertForQuestionAnswering,
       MobileBertForSequenceClassification,
       MobileBertForTokenClassification,
       MobileBertModel,
   )
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_ ,							lowercase_=1_3 ,							lowercase_=7 ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=9_9 ,							lowercase_=6_4 ,							lowercase_=3_2 ,							lowercase_=5 ,							lowercase_=4 ,							lowercase_=3_7 ,							lowercase_="gelu" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=5_1_2 ,							lowercase_=1_6 ,							lowercase_=2 ,							lowercase_=0.0_2 ,							lowercase_=3 ,							lowercase_=4 ,							lowercase_=None ,							)    ->       List[Any]:
          UpperCAmelCase          =       parent
          UpperCAmelCase          =       batch_size
          UpperCAmelCase          =       seq_length
          UpperCAmelCase          =       is_training
          UpperCAmelCase          =       use_input_mask
          UpperCAmelCase          =       use_token_type_ids
          UpperCAmelCase          =       use_labels
          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       embedding_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       intermediate_size
          UpperCAmelCase          =       hidden_act
          UpperCAmelCase          =       hidden_dropout_prob
          UpperCAmelCase          =       attention_probs_dropout_prob
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       type_vocab_size
          UpperCAmelCase          =       type_sequence_label_size
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       num_labels
          UpperCAmelCase          =       num_choices
          UpperCAmelCase          =       scope




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size				)

          UpperCAmelCase          =       None
          if self.use_input_mask:
              UpperCAmelCase          =       random_attention_mask([self.batch_size, self.seq_length]				)

          UpperCAmelCase          =       None
          if self.use_token_type_ids:
              UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.type_vocab_size				)

          UpperCAmelCase          =       None
          UpperCAmelCase          =       None
          UpperCAmelCase          =       None
          if self.use_labels:
              UpperCAmelCase          =       ids_tensor([self.batch_size] ,							self.type_sequence_label_size				)
              UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.num_labels				)
              UpperCAmelCase          =       ids_tensor([self.batch_size] ,							self.num_choices				)

          UpperCAmelCase          =       self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




      def      a_   (				self				)    ->       str:
          return MobileBertConfig(
              vocab_size=self.vocab_size ,							hidden_size=self.hidden_size ,							num_hidden_layers=self.num_hidden_layers ,							num_attention_heads=self.num_attention_heads ,							intermediate_size=self.intermediate_size ,							embedding_size=self.embedding_size ,							hidden_act=self.hidden_act ,							hidden_dropout_prob=self.hidden_dropout_prob ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob ,							max_position_embeddings=self.max_position_embeddings ,							type_vocab_size=self.type_vocab_size ,							is_decoder=lowercase_ ,							initializer_range=self.initializer_range ,							)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Dict:
          UpperCAmelCase          =       MobileBertModel(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       model(lowercase_ ,							attention_mask=lowercase_ ,							token_type_ids=lowercase_				)
          UpperCAmelCase          =       model(lowercase_ ,							token_type_ids=lowercase_				)
          UpperCAmelCase          =       model(lowercase_				)

          self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)				)
          self.parent.assertEqual(result.pooler_output.shape ,							(self.batch_size, self.hidden_size)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       str:
          UpperCAmelCase          =       MobileBertForMaskedLM(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       model(lowercase_ ,							attention_mask=lowercase_ ,							token_type_ids=lowercase_ ,							labels=lowercase_				)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Union[str, Any]:
          UpperCAmelCase          =       MobileBertForNextSentencePrediction(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       model(
              lowercase_ ,							attention_mask=lowercase_ ,							token_type_ids=lowercase_ ,							labels=lowercase_ ,							)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, 2)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Any:
          UpperCAmelCase          =       MobileBertForPreTraining(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       model(
              lowercase_ ,							attention_mask=lowercase_ ,							token_type_ids=lowercase_ ,							labels=lowercase_ ,							next_sentence_label=lowercase_ ,							)
          self.parent.assertEqual(result.prediction_logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)				)
          self.parent.assertEqual(result.seq_relationship_logits.shape ,							(self.batch_size, 2)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       List[str]:
          UpperCAmelCase          =       MobileBertForQuestionAnswering(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       model(
              lowercase_ ,							attention_mask=lowercase_ ,							token_type_ids=lowercase_ ,							start_positions=lowercase_ ,							end_positions=lowercase_ ,							)
          self.parent.assertEqual(result.start_logits.shape ,							(self.batch_size, self.seq_length)				)
          self.parent.assertEqual(result.end_logits.shape ,							(self.batch_size, self.seq_length)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       List[Any]:
          UpperCAmelCase          =       self.num_labels
          UpperCAmelCase          =       MobileBertForSequenceClassification(lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       model(lowercase_ ,							attention_mask=lowercase_ ,							token_type_ids=lowercase_ ,							labels=lowercase_				)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_labels)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.num_labels
          UpperCAmelCase          =       MobileBertForTokenClassification(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       model(lowercase_ ,							attention_mask=lowercase_ ,							token_type_ids=lowercase_ ,							labels=lowercase_				)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.num_labels)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       int:
          UpperCAmelCase          =       self.num_choices
          UpperCAmelCase          =       MobileBertForMultipleChoice(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       input_ids.unsqueeze(1				).expand(-1 ,							self.num_choices ,							-1				).contiguous()
          UpperCAmelCase          =       token_type_ids.unsqueeze(1				).expand(-1 ,							self.num_choices ,							-1				).contiguous()
          UpperCAmelCase          =       input_mask.unsqueeze(1				).expand(-1 ,							self.num_choices ,							-1				).contiguous()
          UpperCAmelCase          =       model(
              lowercase_ ,							attention_mask=lowercase_ ,							token_type_ids=lowercase_ ,							labels=lowercase_ ,							)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_choices)				)




      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       self.prepare_config_and_inputs()
          (
              UpperCAmelCase
          )          =       config_and_inputs
          UpperCAmelCase          =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict

@require_torch
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						(
          (
              MobileBertModel,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE :				Dict				   =						(
          {
              "feature-extraction": MobileBertModel,
              "fill-mask": MobileBertForMaskedLM,
              "question-answering": MobileBertForQuestionAnswering,
              "text-classification": MobileBertForSequenceClassification,
              "token-classification": MobileBertForTokenClassification,
              "zero-shot": MobileBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __SCREAMING_SNAKE_CASE :				Dict				   =						True




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_=False				)    ->       int:
          UpperCAmelCase          =       super()._prepare_for_class(lowercase_ ,							lowercase_ ,							return_labels=lowercase_				)

          if return_labels:
              if model_class in get_values(lowercase_				):
                  UpperCAmelCase          =       torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.seq_length) ,							dtype=torch.long ,							device=lowercase_				)
                  UpperCAmelCase          =       torch.zeros(
                      self.model_tester.batch_size ,							dtype=torch.long ,							device=lowercase_				)
          return inputs_dict




      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       MobileBertModelTester(self				)
          UpperCAmelCase          =       ConfigTester(self ,							config_class=lowercase_ ,							hidden_size=3_7				)




      def      a_   (				self				)    ->       Any:
          self.config_tester.run_common_tests()




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_model(*lowercase_				)




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowercase_				)




      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowercase_				)




      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowercase_				)




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_pretraining(*lowercase_				)




      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_question_answering(*lowercase_				)




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowercase_				)




      def      a_   (				self				)    ->       Tuple:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_token_classification(*lowercase_				)




def      lowercase__    (       lowerCAmelCase					:       Any			)			->   List[str]:






    """simple docstring"""




    return torch.tensor(
        lowerCAmelCase			,					dtype=torch.long			,					device=lowerCAmelCase			,					)


SCREAMING_SNAKE_CASE_            =    1e-3







@require_torch
@require_sentencepiece
@require_tokenizers
class   _UpperCAmelCase				(		unittest.TestCase				):




      @slow
      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       MobileBertModel.from_pretrained('google/mobilebert-uncased'				).to(lowercase_				)
          UpperCAmelCase          =       _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]				)
          with torch.no_grad():
              UpperCAmelCase          =       model(lowercase_				)[0]
          UpperCAmelCase          =       torch.Size((1, 9, 5_1_2)				)
          self.assertEqual(output.shape ,							lowercase_				)
          UpperCAmelCase          =       torch.tensor(
              [
                  [
                      [-2.473_6526E07, 8.269_1656E04, 1.652_1838E05],
                      [-5.754_1704E-01, 3.905_6022E00, 4.401_1507E00],
                      [2.604_7359E00, 1.567_7652E00, -1.732_4188E-01],
                  ]
              ] ,							device=lowercase_ ,							)

          # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
          # ~1 difference, it's therefore not a good idea to measure using addition.
          # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
          # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
          UpperCAmelCase          =       torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE				)
          UpperCAmelCase          =       torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE				)

          self.assertTrue(lower_bound and upper_bound				)







'''simple docstring'''


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
      _UpperCamelCase 							=  argparse.ArgumentParser(
          description=(
              """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
              """ Distillation"""
          )
      )
      parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
      parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
      parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
      parser.add_argument("""--vocab_transform""", action="""store_true""")
      _UpperCamelCase 							=  parser.parse_args()

      if args.model_type == "roberta":
            _UpperCamelCase 							=  RobertaForMaskedLM.from_pretrained(args.model_name)
            _UpperCamelCase 							=  """roberta"""
      elif args.model_type == "gpt2":
            _UpperCamelCase 							=  GPTaLMHeadModel.from_pretrained(args.model_name)
            _UpperCamelCase 							=  """transformer"""

      _UpperCamelCase 							=  model.state_dict()
      _UpperCamelCase 							=  {}

      # Embeddings #
      if args.model_type == "gpt2":
            for param_name in ["wte.weight", "wpe.weight"]:
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.{param_name}"""]
      else:
            for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
                  _UpperCamelCase 							=  F"""{prefix}.embeddings.{w}.weight"""
                  _UpperCamelCase 							=  state_dict[param_name]
            for w in ["weight", "bias"]:
                  _UpperCamelCase 							=  F"""{prefix}.embeddings.LayerNorm.{w}"""
                  _UpperCamelCase 							=  state_dict[param_name]

    # Transformer Blocks #
      _UpperCamelCase 							=  0
      for teacher_idx in [0, 2, 4, 7, 9, 11]:
            if args.model_type == "gpt2":
                  for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                        for w in ["weight", "bias"]:
                              _UpperCamelCase 							=  state_dict[
                                  F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
                              ]
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
            else:
                  for layer in [
                      "attention.self.query",
                      "attention.self.key",
                      "attention.self.value",
                      "attention.output.dense",
                      "attention.output.LayerNorm",
                      "intermediate.dense",
                      "output.dense",
                      "output.LayerNorm",
                  ]:
                        for w in ["weight", "bias"]:
                              _UpperCamelCase 							=  state_dict[
                                  F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
                              ]
            std_idx += 1

      # Language Modeling Head ###s
      if args.model_type == "roberta":
            for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
                  _UpperCamelCase 							=  state_dict[F"""{layer}"""]
            if args.vocab_transform:
                  for w in ["weight", "bias"]:
                        _UpperCamelCase 							=  state_dict[F"""lm_head.dense.{w}"""]
                        _UpperCamelCase 							=  state_dict[F"""lm_head.layer_norm.{w}"""]
      elif args.model_type == "gpt2":
            for w in ["weight", "bias"]:
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.ln_f.{w}"""]
            _UpperCamelCase 							=  state_dict["""lm_head.weight"""]

      print(F"""N layers selected for distillation: {std_idx}""")
      print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

      print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
      torch.save(compressed_sd, args.dump_checkpoint)

