
import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
     from transformers.models.roberta.modeling_flax_roberta import (
         FlaxRobertaForCausalLM,
         FlaxRobertaForMaskedLM,
         FlaxRobertaForMultipleChoice,
         FlaxRobertaForQuestionAnswering,
         FlaxRobertaForSequenceClassification,
         FlaxRobertaForTokenClassification,
         FlaxRobertaModel,
     )




class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=13		,	lowerCamelCase=7		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=99		,	lowerCamelCase=32		,	lowerCamelCase=5		,	lowerCamelCase=4		,	lowerCamelCase=37		,	lowerCamelCase="gelu"		,	lowerCamelCase=0.1		,	lowerCamelCase=0.1		,	lowerCamelCase=5_12		,	lowerCamelCase=16		,	lowerCamelCase=2		,	lowerCamelCase=0.0_2		,	lowerCamelCase=4		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				seq_length
          snake_case__						=				is_training
          snake_case__						=				use_attention_mask
          snake_case__						=				use_token_type_ids
          snake_case__						=				use_labels
          snake_case__						=				vocab_size
          snake_case__						=				hidden_size
          snake_case__						=				num_hidden_layers
          snake_case__						=				num_attention_heads
          snake_case__						=				intermediate_size
          snake_case__						=				hidden_act
          snake_case__						=				hidden_dropout_prob
          snake_case__						=				attention_probs_dropout_prob
          snake_case__						=				max_position_embeddings
          snake_case__						=				type_vocab_size
          snake_case__						=				type_sequence_label_size
          snake_case__						=				initializer_range
          snake_case__						=				num_choices
      def A_			(			self ):
          snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size )

          snake_case__						=				None
          if self.use_attention_mask:
              snake_case__						=				random_attention_mask([self.batch_size, self.seq_length] )

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

          snake_case__						=				RobertaConfig(
              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		,	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=UpperCamelCase_		,	initializer_range=self.initializer_range		,	)

          return config, input_ids, token_type_ids, attention_mask
      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          snake_case__						=				config_and_inputs
          snake_case__						=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict



      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          snake_case__						=				config_and_inputs

          snake_case__						=				True
          snake_case__						=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
          snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	vocab_size=2 )

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )





@require_flax
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					unittest.TestCase ):
      _A						:		Union[str, Any]    	=     True

      _A						:		List[str]    	=     (
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      def A_			(			self ):
          snake_case__						=				FlaxRobertaModelTester(self )



      @slow
      def A_			(			self ):
          for model_class_name in self.all_model_classes:
              snake_case__						=				model_class_name.from_pretrained("roberta-base"		,	from_pt=UpperCamelCase_ )
              snake_case__						=				model(np.ones((1, 1) ) )
              self.assertIsNotNone(UpperCamelCase_ )








import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    return EnvironmentCommand()





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    return EnvironmentCommand(args.accelerate_config_file							)





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      @staticmethod
      def A_			(			lowerCamelCase ):
          snake_case__						=				parser.add_parser("env" )
          download_parser.set_defaults(func=lowerCamelCase )
          download_parser.add_argument(
              "--accelerate-config_file"		,	default=lowerCamelCase		,	help="The accelerate config file to use for the default values in the launching script."		,	)
          download_parser.set_defaults(func=lowerCamelCase )
      def __init__(			self		,	lowerCamelCase		,	*lowerCamelCase ):
          snake_case__						=				accelerate_config_file
      def A_			(			self ):
          snake_case__						=				"not installed"
          if is_safetensors_available():
              import safetensors

              snake_case__						=				safetensors.__version__
          elif importlib.util.find_spec("safetensors" ) is not None:
              import safetensors

              snake_case__						=				F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

          snake_case__						=				"not installed"
          snake_case__						=				snake_case__						=				"not found"
          if is_accelerate_available():
              import accelerate
              from accelerate.commands.config import default_config_file, load_config_from_file

              snake_case__						=				accelerate.__version__
              # Get the default from the config file.
              if self._accelerate_config_file is not None or os.path.isfile(lowerCamelCase ):
                  snake_case__						=				load_config_from_file(self._accelerate_config_file ).to_dict()

              snake_case__						=				(
                  "\n".join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
                  if isinstance(lowerCamelCase		,	lowerCamelCase )
                  else F"""\t{accelerate_config}"""
              )

          snake_case__						=				"not installed"
          snake_case__						=				"NA"
          if is_torch_available():
              import torch

              snake_case__						=				torch.__version__
              snake_case__						=				torch.cuda.is_available()

          snake_case__						=				"not installed"
          snake_case__						=				"NA"
          if is_tf_available():
              import tensorflow as tf

              snake_case__						=				tf.__version__
              try:
                  # deprecated in v2.1
                  snake_case__						=				tf.test.is_gpu_available()
              except AttributeError:
                  # returns list of devices, convert to bool
                  snake_case__						=				bool(tf.config.list_physical_devices("GPU" ) )

          snake_case__						=				"not installed"
          snake_case__						=				"not installed"
          snake_case__						=				"not installed"
          snake_case__						=				"NA"
          if is_flax_available():
              import flax
              import jax
              import jaxlib

              snake_case__						=				flax.__version__
              snake_case__						=				jax.__version__
              snake_case__						=				jaxlib.__version__
              snake_case__						=				jax.lib.xla_bridge.get_backend().platform

          snake_case__						=				{
              "`transformers` version": version,
              "Platform": platform.platform(),
              "Python version": platform.python_version(),
              "Huggingface_hub version": huggingface_hub.__version__,
              "Safetensors version": F"""{safetensors_version}""",
              "Accelerate version": F"""{accelerate_version}""",
              "Accelerate config": F"""{accelerate_config_str}""",
              "PyTorch version (GPU?)": F"""{pt_version} ({pt_cuda_available})""",
              "Tensorflow version (GPU?)": F"""{tf_version} ({tf_cuda_available})""",
              "Flax version (CPU?/GPU?/TPU?)": F"""{flax_version} ({jax_backend})""",
              "Jax version": F"""{jax_version}""",
              "JaxLib version": F"""{jaxlib_version}""",
              "Using GPU in script?": "<fill in>",
              "Using distributed or parallel set-up in script?": "<fill in>",
          }

          print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
          print(self.format_dict(lowerCamelCase ) )

          return info



      @staticmethod
      def A_			(			lowerCamelCase ):
          return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"



