
import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      import jax
      import jax.numpy as jnp
      import numpy as np

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
      import torch





@require_flax
class 							A__		:
      def __init__(				self   ,  __magic_name__   ,  __magic_name__=1_4   ,  __magic_name__=7   ,  __magic_name__=True   ,  __magic_name__=True   ,  __magic_name__=True   ,  __magic_name__=9_9   ,  __magic_name__=3_2   ,  __magic_name__=5   ,  __magic_name__=4   ,  __magic_name__=3_7   ,  __magic_name__="gelu"   ,  __magic_name__=0.1   ,  __magic_name__=0.1   ,  __magic_name__=5_1_2   ,  __magic_name__=0.02   ,  __magic_name__=None   ,  ):
       lowerCamelCase		:     Union[str, Any]          =				parent
       lowerCamelCase		:     List[str]          =				batch_size
       lowerCamelCase		:     List[Any]          =				seq_length
       lowerCamelCase		:     Optional[int]          =				is_training
       lowerCamelCase		:     str          =				use_input_mask
       lowerCamelCase		:     Optional[Any]          =				use_labels
       lowerCamelCase		:     Any          =				vocab_size
       lowerCamelCase		:     Optional[Any]          =				d_model
       lowerCamelCase		:     List[str]          =				num_hidden_layers
       lowerCamelCase		:     Union[str, Any]          =				num_attention_heads
       lowerCamelCase		:     int          =				ffn_dim
       lowerCamelCase		:     Union[str, Any]          =				activation_function
       lowerCamelCase		:     int          =				activation_dropout
       lowerCamelCase		:     str          =				attention_dropout
       lowerCamelCase		:     Optional[Any]          =				max_position_embeddings
       lowerCamelCase		:     List[Any]          =				initializer_range
       lowerCamelCase		:     int          =				None
       lowerCamelCase		:     str          =				0
       lowerCamelCase		:     Dict          =				2
       lowerCamelCase		:     Optional[Any]          =				1
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				np.clip(ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size				)   ,  3   ,  self.vocab_size				)

       lowerCamelCase		:     List[Any]          =				None
       if self.use_input_mask:
        lowerCamelCase		:     Dict          =				random_attention_mask([self.batch_size, self.seq_length]				)

       lowerCamelCase		:     Dict          =				XGLMConfig(
           vocab_size=self.vocab_size   ,  d_model=self.hidden_size   ,  num_layers=self.num_hidden_layers   ,  attention_heads=self.num_attention_heads   ,  ffn_dim=self.ffn_dim   ,  activation_function=self.activation_function   ,  activation_dropout=self.activation_dropout   ,  attention_dropout=self.attention_dropout   ,  max_position_embeddings=self.max_position_embeddings   ,  initializer_range=self.initializer_range   ,  use_cache=__magic_name__   ,  bos_token_id=self.bos_token_id   ,  eos_token_id=self.eos_token_id   ,  pad_token_id=self.pad_token_id   ,  )

       return (config, input_ids, input_mask)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[Any]          =				self.prepare_config_and_inputs()
       lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase		:     Optional[int]          =				config_and_inputs
       lowerCamelCase		:     Dict          =				{"""input_ids""": input_ids, """attention_mask""": attention_mask}
       return config, inputs_dict
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				self.prepare_config_and_inputs()

       lowerCamelCase		:     Optional[int]          =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
       lowerCamelCase		:     Optional[Any]          =				ids_tensor([self.batch_size, self.seq_length]   ,  vocab_size=2				)

       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     List[Any]          =				2_0
       lowerCamelCase		:     Optional[int]          =				model_class_name(__magic_name__				)

       lowerCamelCase		:     Optional[Any]          =				model.init_cache(input_ids.shape[0]   ,  __magic_name__				)
       lowerCamelCase		:     Union[str, Any]          =				jnp.ones((input_ids.shape[0], max_decoder_length)   ,  dtype="""i4"""				)

       lowerCamelCase		:     Union[str, Any]          =				jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1				)[None, :]   ,  (input_ids.shape[0], input_ids.shape[-1] - 1)				)
       lowerCamelCase		:     str          =				model(
           input_ids[:, :-1]   ,  attention_mask=__magic_name__   ,  past_key_values=__magic_name__   ,  position_ids=__magic_name__   ,  )

       lowerCamelCase		:     Optional[Any]          =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,  dtype="""i4"""				)
       lowerCamelCase		:     int          =				model(
           input_ids[:, -1:]   ,  attention_mask=__magic_name__   ,  past_key_values=outputs_cache.past_key_values   ,  position_ids=__magic_name__   ,  )

       lowerCamelCase		:     List[str]          =				model(__magic_name__				)

       lowerCamelCase		:     Optional[int]          =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
       self.parent.assertTrue(diff < 1e-3   ,  msg=F'''Max diff is {diff}'''				)






      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     str          =				2_0
       lowerCamelCase		:     Optional[int]          =				model_class_name(__magic_name__				)

       lowerCamelCase		:     Tuple          =				jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)]   ,  axis=-1   ,  )

       lowerCamelCase		:     Optional[Any]          =				model.init_cache(input_ids.shape[0]   ,  __magic_name__				)
       lowerCamelCase		:     int          =				jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1				)[None, :]   ,  (input_ids.shape[0], input_ids.shape[-1] - 1)				)

       lowerCamelCase		:     Optional[Any]          =				model(
           input_ids[:, :-1]   ,  attention_mask=__magic_name__   ,  past_key_values=__magic_name__   ,  position_ids=__magic_name__   ,  )
       lowerCamelCase		:     List[Any]          =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,  dtype="""i4"""				)
       lowerCamelCase		:     Union[str, Any]          =				model(
           input_ids[:, -1:]   ,  past_key_values=outputs_cache.past_key_values   ,  attention_mask=__magic_name__   ,  position_ids=__magic_name__   ,  )

       lowerCamelCase		:     str          =				model(__magic_name__   ,  attention_mask=__magic_name__				)
       lowerCamelCase		:     List[str]          =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
       self.parent.assertTrue(diff < 1e-3   ,  msg=F'''Max diff is {diff}'''				)





@require_sentencepiece
@require_flax
class 							A__		(     __SCREAMING_SNAKE_CASE  ,       __SCREAMING_SNAKE_CASE  ,       unittest.TestCase):
      _UpperCAmelCase    :   Optional[Any]   	=      (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
      _UpperCAmelCase    :   int   	=      (FlaxXGLMForCausalLM,) if is_flax_available() else ()
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[Any]          =				FlaxXGLMModelTester(self				)
      def 						UpperCamelCase__							(				self				):
       for model_class_name in self.all_model_classes:
        lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase		:     Union[str, Any]          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)
      def 						UpperCamelCase__							(				self				):
       for model_class_name in self.all_model_classes:
        lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase		:     Union[str, Any]          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward_with_attn_mask(
            __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)
      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[Any]          =				XGLMTokenizer.from_pretrained("""XGLM"""   ,  padding_side="""left"""				)
       lowerCamelCase		:     Any          =				tokenizer(["""Hello this is a long string""", """Hey"""]   ,  return_tensors="""np"""   ,  padding=__magic_name__   ,  truncation=__magic_name__				)

       lowerCamelCase		:     Tuple          =				FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""				)
       lowerCamelCase		:     Union[str, Any]          =				1
       lowerCamelCase		:     Union[str, Any]          =				False

       lowerCamelCase		:     Dict          =				jax.jit(model.generate				)

       lowerCamelCase		:     str          =				jit_generate(inputs["""input_ids"""]   ,  attention_mask=inputs["""attention_mask"""]				).sequences

       lowerCamelCase		:     List[Any]          =				tokenizer.batch_decode(__magic_name__   ,  skip_special_tokens=__magic_name__				)

       lowerCamelCase		:     Optional[int]          =				[
           """Hello this is a long string of questions, but I'm not sure if I'm""",
           """Hey, I'm a newbie to the forum and I'""",
       ]

       self.assertListEqual(__magic_name__   ,  __magic_name__				)
      @is_pt_flax_cross_test
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase      ,   lowerCamelCase		:     int          =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__				):
         # prepare inputs
         lowerCamelCase		:     str          =				self._prepare_for_class(__magic_name__   ,  __magic_name__				)
         lowerCamelCase		:     Any          =				{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

         # load corresponding PyTorch class
         lowerCamelCase		:     List[str]          =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
         lowerCamelCase		:     Dict          =				getattr(__magic_name__   ,  __magic_name__				)

         lowerCamelCase      ,   lowerCamelCase		:     Optional[Any]          =				pt_inputs["""input_ids"""].shape
         lowerCamelCase		:     Union[str, Any]          =				np.random.randint(0   ,  seq_length - 1   ,  size=(batch_size,)				)
         for batch_idx, start_index in enumerate(__magic_name__				):
          lowerCamelCase		:     Dict          =				0
          lowerCamelCase		:     Any          =				1
          lowerCamelCase		:     int          =				0
          lowerCamelCase		:     Dict          =				1
         lowerCamelCase		:     str          =				pt_model_class(__magic_name__				).eval()
         # Flax models don't use the `use_cache` option and cache is not returned as a default.
         # So we disable `use_cache` here for PyTorch model.
         lowerCamelCase		:     int          =				False
         lowerCamelCase		:     Optional[Any]          =				model_class(__magic_name__   ,  dtype=jnp.floataa				)
         lowerCamelCase		:     Tuple          =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,  __magic_name__				)
         lowerCamelCase		:     List[str]          =				fx_state

         with torch.no_grad():
          lowerCamelCase		:     Any          =				pt_model(**__magic_name__				).to_tuple()

         lowerCamelCase		:     Dict          =				fx_model(**__magic_name__				).to_tuple()
         self.assertEqual(len(__magic_name__				)   ,  len(__magic_name__				)   ,  """Output lengths differ between Flax and PyTorch"""				)
         for fx_output, pt_output in zip(__magic_name__   ,  __magic_name__				):
          self.assert_almost_equals(fx_output[:, -1]   ,  pt_output[:, -1].numpy()   ,  4e-2				)

         with tempfile.TemporaryDirectory() as tmpdirname:
          pt_model.save_pretrained(__magic_name__				)
          lowerCamelCase		:     Tuple          =				model_class.from_pretrained(__magic_name__   ,  from_pt=__magic_name__				)

         lowerCamelCase		:     str          =				fx_model_loaded(**__magic_name__				).to_tuple()
         self.assertEqual(
             len(__magic_name__				)   ,  len(__magic_name__				)   ,  """Output lengths differ between Flax and PyTorch"""				)
         for fx_output_loaded, pt_output in zip(__magic_name__   ,  __magic_name__				):
          self.assert_almost_equals(fx_output_loaded[:, -1]   ,  pt_output[:, -1].numpy()   ,  4e-2				)
      @is_pt_flax_cross_test
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase      ,   lowerCamelCase		:     List[str]          =				self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__				):
         # prepare inputs
         lowerCamelCase		:     List[str]          =				self._prepare_for_class(__magic_name__   ,  __magic_name__				)
         lowerCamelCase		:     Optional[Any]          =				{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

         # load corresponding PyTorch class
         lowerCamelCase		:     int          =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
         lowerCamelCase		:     Tuple          =				getattr(__magic_name__   ,  __magic_name__				)

         lowerCamelCase		:     Optional[Any]          =				pt_model_class(__magic_name__				).eval()
         lowerCamelCase		:     Dict          =				False
         lowerCamelCase		:     List[Any]          =				model_class(__magic_name__   ,  dtype=jnp.floataa				)

         lowerCamelCase		:     Tuple          =				load_flax_weights_in_pytorch_model(__magic_name__   ,  fx_model.params				)
         lowerCamelCase      ,   lowerCamelCase		:     Optional[Any]          =				pt_inputs["""input_ids"""].shape
         lowerCamelCase		:     Dict          =				np.random.randint(0   ,  seq_length - 1   ,  size=(batch_size,)				)
         for batch_idx, start_index in enumerate(__magic_name__				):
          lowerCamelCase		:     str          =				0
          lowerCamelCase		:     Optional[int]          =				1
          lowerCamelCase		:     List[Any]          =				0
          lowerCamelCase		:     Any          =				1

         # make sure weights are tied in PyTorch
         pt_model.tie_weights()

         with torch.no_grad():
          lowerCamelCase		:     List[Any]          =				pt_model(**__magic_name__				).to_tuple()

         lowerCamelCase		:     List[str]          =				fx_model(**__magic_name__				).to_tuple()
         self.assertEqual(len(__magic_name__				)   ,  len(__magic_name__				)   ,  """Output lengths differ between Flax and PyTorch"""				)
         for fx_output, pt_output in zip(__magic_name__   ,  __magic_name__				):
          self.assert_almost_equals(fx_output[:, -1]   ,  pt_output[:, -1].numpy()   ,  4e-2				)

         with tempfile.TemporaryDirectory() as tmpdirname:
          fx_model.save_pretrained(__magic_name__				)
          lowerCamelCase		:     Optional[Any]          =				pt_model_class.from_pretrained(__magic_name__   ,  from_flax=__magic_name__				)

         with torch.no_grad():
          lowerCamelCase		:     Tuple          =				pt_model_loaded(**__magic_name__				).to_tuple()

         self.assertEqual(
             len(__magic_name__				)   ,  len(__magic_name__				)   ,  """Output lengths differ between Flax and PyTorch"""				)
         for fx_output, pt_output in zip(__magic_name__   ,  __magic_name__				):
          self.assert_almost_equals(fx_output[:, -1]   ,  pt_output[:, -1].numpy()   ,  4e-2				)






      @slow
      def 						UpperCamelCase__							(				self				):
       for model_class_name in self.all_model_classes:
        lowerCamelCase		:     Optional[int]          =				model_class_name.from_pretrained("""facebook/xglm-564M"""				)
        lowerCamelCase		:     Union[str, Any]          =				model(np.ones((1, 1)				)				)
        self.assertIsNotNone(__magic_name__				)






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     Dict          =				args.serialization_dir
 lowerCamelCase		:     List[Any]          =				args.pruning_method
 lowerCamelCase		:     List[Any]          =				args.threshold

 lowerCamelCase		:     str          =				torch.load(os.path.join(lowerCamelCase,     """pytorch_model.bin"""							),     map_location="""cpu"""							)

 lowerCamelCase		:     Optional[int]          =				0  # Number of remaining (not pruned) params in the encoder
 lowerCamelCase		:     Dict          =				0  # Number of params in the encoder

 print("""name""".ljust(60,     """ """							),     """Remaining Weights %""",     """Remaining Weight"""							)
 for name, param in st.items():
  if "encoder" not in name:
   continue

  if "mask_scores" in name:
   if pruning_method == "topK":
    lowerCamelCase		:     List[str]          =				TopKBinarizer.apply(lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "sigmoied_threshold":
    lowerCamelCase		:     Optional[int]          =				ThresholdBinarizer.apply(lowerCamelCase,     lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "l0":
    lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				-0.1, 1.1
    lowerCamelCase		:     Any          =				torch.sigmoid(lowerCamelCase							)
    lowerCamelCase		:     str          =				s * (r - l) + l
    lowerCamelCase		:     str          =				s_bar.clamp(min=0.0,     max=1.0							)
    lowerCamelCase		:     Union[str, Any]          =				(mask > 0.0).sum().item()
   else:
    raise ValueError("""Unknown pruning method"""							)
   remaining_count += mask_ones
   print(name.ljust(60,     """ """							),     str(round(100 * mask_ones / param.numel(),     3							)							).ljust(20,     """ """							),     str(lowerCamelCase							)							)
  else:
   encoder_count += param.numel()
   if "bias" in name or "LayerNorm" in name:
    remaining_count += param.numel()

 print(""""""							)
 print("""Remaining Weights (global) %: """,     100 * remaining_count / encoder_count							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()

      parser.add_argument(
          """--pruning_method""",
          choices=["""l0""", """topK""", """sigmoied_threshold"""],
          type=str,
          required=True,
          help=(
              """Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"""
              """ pruning)"""
          ),
      )
      parser.add_argument(
          """--threshold""",
          type=float,
          required=False,
          help=(
              """For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
              """For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
              """Not needed for `l0`"""
          ),
      )
      parser.add_argument(
          """--serialization_dir""",
          type=str,
          required=True,
          help="""Folder containing the model that was previously fine-pruned""",
      )

      _lowerCamelCase        =parser.parse_args()

      main(args)




