



"""simple docstring"""






import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class 			__snake_case			(      nn.Module):
  snake_case__							:  int
  snake_case__							:  int
  snake_case__							:  float         =		0.0
  snake_case__							:  int         =		1
  snake_case__							:  int         =		1
  snake_case__							:  bool         =		True
  snake_case__							:  bool         =		False
  snake_case__							:  bool         =		False
  snake_case__							:  bool         =		False
  snake_case__							:  jnp.dtype         =		jnp.floataa




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					[]
         _lowerCamelCase		:							Optional[Any]            =					[]

         for i in range(self.num_layers						):
                _lowerCamelCase		:							Optional[int]            =					self.in_channels if i == 0 else self.out_channels

                _lowerCamelCase		:							Union[str, Any]            =					FlaxResnetBlockaD(
                    in_channels=__lowerCAmelCase		,   out_channels=self.out_channels		,   dropout_prob=self.dropout		,   dtype=self.dtype		,   )
                resnets.append(__lowerCAmelCase						)

                _lowerCamelCase		:							List[str]            =					FlaxTransformeraDModel(
                    in_channels=self.out_channels		,   n_heads=self.num_attention_heads		,   d_head=self.out_channels // self.num_attention_heads		,   depth=1		,   use_linear_projection=self.use_linear_projection		,   only_cross_attention=self.only_cross_attention		,   use_memory_efficient_attention=self.use_memory_efficient_attention		,   dtype=self.dtype		,   )
                attentions.append(__lowerCAmelCase						)

         _lowerCamelCase		:							Optional[Any]            =					resnets
         _lowerCamelCase		:							List[Any]            =					attentions

         if self.add_downsample:
                _lowerCamelCase		:							str            =					FlaxDownsampleaD(self.out_channels		,   dtype=self.dtype						)




  def __call__(					self	:		str		,   __lowerCAmelCase	:		Dict		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		Any		,   __lowerCAmelCase	:		List[Any]=True						):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					()

         for resnet, attn in zip(self.resnets		,   self.attentions						):
                _lowerCamelCase		:							Optional[int]            =					resnet(__lowerCAmelCase		,   __lowerCAmelCase		,   deterministic=__lowerCAmelCase						)
                _lowerCamelCase		:							str            =					attn(__lowerCAmelCase		,   __lowerCAmelCase		,   deterministic=__lowerCAmelCase						)
                output_states += (hidden_states,)

         if self.add_downsample:
                _lowerCamelCase		:							Optional[Any]            =					self.downsamplers_a(__lowerCAmelCase						)
                output_states += (hidden_states,)

         return hidden_states, output_states






class 			__snake_case			(      nn.Module):
  snake_case__							:  int
  snake_case__							:  int
  snake_case__							:  float         =		0.0
  snake_case__							:  int         =		1
  snake_case__							:  bool         =		True
  snake_case__							:  jnp.dtype         =		jnp.floataa




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					[]

         for i in range(self.num_layers						):
                _lowerCamelCase		:							Any            =					self.in_channels if i == 0 else self.out_channels

                _lowerCamelCase		:							Union[str, Any]            =					FlaxResnetBlockaD(
                    in_channels=__lowerCAmelCase		,   out_channels=self.out_channels		,   dropout_prob=self.dropout		,   dtype=self.dtype		,   )
                resnets.append(__lowerCAmelCase						)
         _lowerCamelCase		:							Dict            =					resnets

         if self.add_downsample:
                _lowerCamelCase		:							Tuple            =					FlaxDownsampleaD(self.out_channels		,   dtype=self.dtype						)




  def __call__(					self	:		str		,   __lowerCAmelCase	:		List[Any]		,   __lowerCAmelCase	:		Optional[Any]		,   __lowerCAmelCase	:		Tuple=True						):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					()

         for resnet in self.resnets:
                _lowerCamelCase		:							int            =					resnet(__lowerCAmelCase		,   __lowerCAmelCase		,   deterministic=__lowerCAmelCase						)
                output_states += (hidden_states,)

         if self.add_downsample:
                _lowerCamelCase		:							Union[str, Any]            =					self.downsamplers_a(__lowerCAmelCase						)
                output_states += (hidden_states,)

         return hidden_states, output_states






class 			__snake_case			(      nn.Module):
  snake_case__							:  int
  snake_case__							:  int
  snake_case__							:  int
  snake_case__							:  float         =		0.0
  snake_case__							:  int         =		1
  snake_case__							:  int         =		1
  snake_case__							:  bool         =		True
  snake_case__							:  bool         =		False
  snake_case__							:  bool         =		False
  snake_case__							:  bool         =		False
  snake_case__							:  jnp.dtype         =		jnp.floataa




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					[]
         _lowerCamelCase		:							List[str]            =					[]

         for i in range(self.num_layers						):
                _lowerCamelCase		:							List[str]            =					self.in_channels if (i == self.num_layers - 1) else self.out_channels
                _lowerCamelCase		:							str            =					self.prev_output_channel if i == 0 else self.out_channels

                _lowerCamelCase		:							Any            =					FlaxResnetBlockaD(
                    in_channels=resnet_in_channels + res_skip_channels		,   out_channels=self.out_channels		,   dropout_prob=self.dropout		,   dtype=self.dtype		,   )
                resnets.append(__lowerCAmelCase						)

                _lowerCamelCase		:							Dict            =					FlaxTransformeraDModel(
                    in_channels=self.out_channels		,   n_heads=self.num_attention_heads		,   d_head=self.out_channels // self.num_attention_heads		,   depth=1		,   use_linear_projection=self.use_linear_projection		,   only_cross_attention=self.only_cross_attention		,   use_memory_efficient_attention=self.use_memory_efficient_attention		,   dtype=self.dtype		,   )
                attentions.append(__lowerCAmelCase						)

         _lowerCamelCase		:							Optional[Any]            =					resnets
         _lowerCamelCase		:							Optional[Any]            =					attentions

         if self.add_upsample:
                _lowerCamelCase		:							str            =					FlaxUpsampleaD(self.out_channels		,   dtype=self.dtype						)




  def __call__(					self	:		List[Any]		,   __lowerCAmelCase	:		List[Any]		,   __lowerCAmelCase	:		List[Any]		,   __lowerCAmelCase	:		Tuple		,   __lowerCAmelCase	:		Dict		,   __lowerCAmelCase	:		Any=True						):






         """simple docstring"""






         for resnet, attn in zip(self.resnets		,   self.attentions						):
                # pop res hidden states
                _lowerCamelCase		:							List[Any]            =					res_hidden_states_tuple[-1]
                _lowerCamelCase		:							Any            =					res_hidden_states_tuple[:-1]
                _lowerCamelCase		:							Union[str, Any]            =					jnp.concatenate((hidden_states, res_hidden_states)		,   axis=-1						)

                _lowerCamelCase		:							List[str]            =					resnet(__lowerCAmelCase		,   __lowerCAmelCase		,   deterministic=__lowerCAmelCase						)
                _lowerCamelCase		:							Tuple            =					attn(__lowerCAmelCase		,   __lowerCAmelCase		,   deterministic=__lowerCAmelCase						)

         if self.add_upsample:
                _lowerCamelCase		:							List[Any]            =					self.upsamplers_a(__lowerCAmelCase						)

         return hidden_states






class 			__snake_case			(      nn.Module):
  snake_case__							:  int
  snake_case__							:  int
  snake_case__							:  int
  snake_case__							:  float         =		0.0
  snake_case__							:  int         =		1
  snake_case__							:  bool         =		True
  snake_case__							:  jnp.dtype         =		jnp.floataa




  def 		SCREAMING_SNAKE_CASE							(					self	:		int						):






         """simple docstring"""






         _lowerCamelCase		:							Dict            =					[]

         for i in range(self.num_layers						):
                _lowerCamelCase		:							Optional[int]            =					self.in_channels if (i == self.num_layers - 1) else self.out_channels
                _lowerCamelCase		:							Optional[int]            =					self.prev_output_channel if i == 0 else self.out_channels

                _lowerCamelCase		:							Union[str, Any]            =					FlaxResnetBlockaD(
                    in_channels=resnet_in_channels + res_skip_channels		,   out_channels=self.out_channels		,   dropout_prob=self.dropout		,   dtype=self.dtype		,   )
                resnets.append(__lowerCAmelCase						)

         _lowerCamelCase		:							Tuple            =					resnets

         if self.add_upsample:
                _lowerCamelCase		:							str            =					FlaxUpsampleaD(self.out_channels		,   dtype=self.dtype						)




  def __call__(					self	:		Any		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		Optional[Any]		,   __lowerCAmelCase	:		List[str]		,   __lowerCAmelCase	:		Optional[Any]=True						):






         """simple docstring"""






         for resnet in self.resnets:
                # pop res hidden states
                _lowerCamelCase		:							Tuple            =					res_hidden_states_tuple[-1]
                _lowerCamelCase		:							Union[str, Any]            =					res_hidden_states_tuple[:-1]
                _lowerCamelCase		:							List[Any]            =					jnp.concatenate((hidden_states, res_hidden_states)		,   axis=-1						)

                _lowerCamelCase		:							List[Any]            =					resnet(__lowerCAmelCase		,   __lowerCAmelCase		,   deterministic=__lowerCAmelCase						)

         if self.add_upsample:
                _lowerCamelCase		:							int            =					self.upsamplers_a(__lowerCAmelCase						)

         return hidden_states






class 			__snake_case			(      nn.Module):
  snake_case__							:  int
  snake_case__							:  float         =		0.0
  snake_case__							:  int         =		1
  snake_case__							:  int         =		1
  snake_case__							:  bool         =		False
  snake_case__							:  bool         =		False
  snake_case__							:  jnp.dtype         =		jnp.floataa




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]						):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					[
             FlaxResnetBlockaD(
                 in_channels=self.in_channels		,   out_channels=self.in_channels		,   dropout_prob=self.dropout		,   dtype=self.dtype		,   )
         ]

         _lowerCamelCase		:							Dict            =					[]

         for _ in range(self.num_layers						):
                _lowerCamelCase		:							int            =					FlaxTransformeraDModel(
                    in_channels=self.in_channels		,   n_heads=self.num_attention_heads		,   d_head=self.in_channels // self.num_attention_heads		,   depth=1		,   use_linear_projection=self.use_linear_projection		,   use_memory_efficient_attention=self.use_memory_efficient_attention		,   dtype=self.dtype		,   )
                attentions.append(__lowerCAmelCase						)

                _lowerCamelCase		:							Union[str, Any]            =					FlaxResnetBlockaD(
                    in_channels=self.in_channels		,   out_channels=self.in_channels		,   dropout_prob=self.dropout		,   dtype=self.dtype		,   )
                resnets.append(__lowerCAmelCase						)

         _lowerCamelCase		:							Any            =					resnets
         _lowerCamelCase		:							Tuple            =					attentions




  def __call__(					self	:		Any		,   __lowerCAmelCase	:		Optional[Any]		,   __lowerCAmelCase	:		Union[str, Any]		,   __lowerCAmelCase	:		Union[str, Any]		,   __lowerCAmelCase	:		List[Any]=True						):






         """simple docstring"""






         _lowerCamelCase		:							int            =					self.resnets[0](__lowerCAmelCase		,   __lowerCAmelCase						)
         for attn, resnet in zip(self.attentions		,   self.resnets[1:]						):
                _lowerCamelCase		:							Any            =					attn(__lowerCAmelCase		,   __lowerCAmelCase		,   deterministic=__lowerCAmelCase						)
                _lowerCamelCase		:							Tuple            =					resnet(__lowerCAmelCase		,   __lowerCAmelCase		,   deterministic=__lowerCAmelCase						)

         return hidden_states






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE							    =							logging.getLogger()




def  a   ():
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""-f"""   )
   __a					=  parser.parse_args()
   return args.f


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  logging.StreamHandler(sys.stdout    )
        logger.addHandler(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  get_gpu_count()

        if n_gpu > 1:
           pass
           # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
           # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
           # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
           # cmd = [sys.executable] + distributed_args + args
           # execute_subprocess_async(cmd, env=self.get_env())
           # XXX: test the results - need to save them first into .json file
        else:
           args.insert(0  ,       """run_glue_deebert.py"""    )
           with patch.object(__A  ,       """argv"""  ,       __A    ):
              __a					=  run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(__A  ,       0.666    )


     @slow
     @require_torch_non_multi_gpu
     def 	snake_case_  (	self    ):
        __a					=  """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
        self.run_and_check(__A    )

        __a					=  """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(__A    )

        __a					=  """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(__A    )



