
import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     __magic_name__            =  '''platform'''
     import jax
     import jax.numpy as jnp
     import numpy as np

     from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class 					_SCREAMING_SNAKE_CASE  :
      _A						:		Tuple    	=     PegasusConfig
      _A						:		Tuple    	=     {}
      _A						:		int    	=     "gelu"
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=13		,	lowerCamelCase=7		,	lowerCamelCase=True		,	lowerCamelCase=False		,	lowerCamelCase=99		,	lowerCamelCase=32		,	lowerCamelCase=5		,	lowerCamelCase=4		,	lowerCamelCase=37		,	lowerCamelCase=0.1		,	lowerCamelCase=0.1		,	lowerCamelCase=20		,	lowerCamelCase=2		,	lowerCamelCase=1		,	lowerCamelCase=0		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				seq_length
          snake_case__						=				is_training
          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_dropout_prob
          snake_case__						=				attention_probs_dropout_prob
          snake_case__						=				max_position_embeddings
          snake_case__						=				eos_token_id
          snake_case__						=				pad_token_id
          snake_case__						=				bos_token_id
      def A_			(			self ):
          snake_case__						=				ids_tensor([self.batch_size, self.seq_length - 1]		,	self.vocab_size ).clip(3		,	self.vocab_size )
          snake_case__						=				np.expand_dims(np.array([self.eos_token_id] * self.batch_size )		,	1 )
          snake_case__						=				np.concatenate([input_ids, eos_tensor]		,	axis=1 )

          snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size )

          snake_case__						=				self.config_cls(
              vocab_size=self.vocab_size		,	d_model=self.hidden_size		,	encoder_layers=self.num_hidden_layers		,	decoder_layers=self.num_hidden_layers		,	encoder_attention_heads=self.num_attention_heads		,	decoder_attention_heads=self.num_attention_heads		,	encoder_ffn_dim=self.intermediate_size		,	decoder_ffn_dim=self.intermediate_size		,	dropout=self.hidden_dropout_prob		,	attention_dropout=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	eos_token_ids=[2]		,	bos_token_id=self.bos_token_id		,	pad_token_id=self.pad_token_id		,	decoder_start_token_id=self.pad_token_id		,	**self.config_updates		,	)
          snake_case__						=				prepare_pegasus_inputs_dict(lowerCamelCase_		,	lowerCamelCase_		,	lowerCamelCase_ )
          return config, inputs_dict
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				20
          snake_case__						=				model_class_name(lowerCamelCase_ )

          snake_case__						=				model.encode(inputs_dict["input_ids"] )

          snake_case__						,					snake_case__						=				(
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          snake_case__						=				model.init_cache(decoder_input_ids.shape[0]		,	lowerCamelCase_		,	lowerCamelCase_ )
          snake_case__						=				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,	dtype="i4" )

          snake_case__						=				jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]		,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,	)
          snake_case__						=				model.decode(
              decoder_input_ids[:, :-1]		,	lowerCamelCase_		,	decoder_attention_mask=lowerCamelCase_		,	past_key_values=lowerCamelCase_		,	decoder_position_ids=lowerCamelCase_		,	)

          snake_case__						=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,	dtype="i4" )
          snake_case__						=				model.decode(
              decoder_input_ids[:, -1:]		,	lowerCamelCase_		,	decoder_attention_mask=lowerCamelCase_		,	past_key_values=outputs_cache.past_key_values		,	decoder_position_ids=lowerCamelCase_		,	)

          snake_case__						=				model.decode(lowerCamelCase_		,	lowerCamelCase_ )

          snake_case__						=				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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				20
          snake_case__						=				model_class_name(lowerCamelCase_ )

          snake_case__						=				model.encode(inputs_dict["input_ids"] )

          snake_case__						,					snake_case__						=				(
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          snake_case__						=				jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
              ]		,	axis=-1		,	)

          snake_case__						=				model.init_cache(decoder_input_ids.shape[0]		,	lowerCamelCase_		,	lowerCamelCase_ )
          snake_case__						=				jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]		,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,	)

          snake_case__						=				model.decode(
              decoder_input_ids[:, :-1]		,	lowerCamelCase_		,	decoder_attention_mask=lowerCamelCase_		,	past_key_values=lowerCamelCase_		,	decoder_position_ids=lowerCamelCase_		,	)
          snake_case__						=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,	dtype="i4" )
          snake_case__						=				model.decode(
              decoder_input_ids[:, -1:]		,	lowerCamelCase_		,	past_key_values=outputs_cache.past_key_values		,	decoder_attention_mask=lowerCamelCase_		,	decoder_position_ids=lowerCamelCase_		,	)

          snake_case__						=				model.decode(lowerCamelCase_		,	lowerCamelCase_		,	decoder_attention_mask=lowerCamelCase_ )

          snake_case__						=				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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase=None    ,		):
    if attention_mask is None:
        snake_case__						=				np.not_equal(__lowerCAmelCase    ,		config.pad_token_id							).astype(np.inta							)
    if decoder_attention_mask is None:
        snake_case__						=				np.concatenate(
            [
                np.ones(decoder_input_ids[:, :1].shape    ,		dtype=np.inta							),
                np.not_equal(decoder_input_ids[:, 1:]    ,		config.pad_token_id							).astype(np.inta							),
            ]    ,		axis=-1    ,		)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
    }





@require_flax
class 					_SCREAMING_SNAKE_CASE  (      a__       ,					unittest.TestCase ):
      _A						:		Union[str, Any]    	=     (
          (
              FlaxPegasusForConditionalGeneration,
              FlaxPegasusModel,
          )
          if is_flax_available()
          else ()
      )
      _A						:		str    	=     (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
      _A						:		Optional[int]    	=     True
      _A						:		List[Any]    	=     False
      _A						:		Union[str, Any]    	=     False
      _A						:		Any    	=     False
      def A_			(			self ):
          snake_case__						=				FlaxPegasusModelTester(self )
          snake_case__						=				ConfigTester(self		,	config_class=lowerCamelCase_ )
      def A_			(			self ):
          self.config_tester.run_common_tests()
      def A_			(			self ):
          snake_case__						,					snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(lowerCamelCase_		,	lowerCamelCase_		,	lowerCamelCase_ )
      def A_			(			self ):
          snake_case__						,					snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase_		,	lowerCamelCase_		,	lowerCamelCase_ )
      def A_			(			self ):
          snake_case__						,					snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                  snake_case__						=				self._prepare_for_class(lowerCamelCase_		,	lowerCamelCase_ )
                  snake_case__						=				model_class(lowerCamelCase_ )

                  @jax.jit
                  def encode_jitted(lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
                      return model.encode(input_ids=lowerCamelCase_		,	attention_mask=lowerCamelCase_ )

                  with self.subTest("JIT Enabled" ):
                      snake_case__						=				encode_jitted(**lowerCamelCase_ ).to_tuple()

                  with self.subTest("JIT Disabled" ):
                      with jax.disable_jit():
                          snake_case__						=				encode_jitted(**lowerCamelCase_ ).to_tuple()

                  self.assertEqual(len(lowerCamelCase_ )		,	len(lowerCamelCase_ ) )
                  for jitted_output, output in zip(lowerCamelCase_		,	lowerCamelCase_ ):
                      self.assertEqual(jitted_output.shape		,	output.shape )
      def A_			(			self ):
          snake_case__						,					snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                  snake_case__						=				model_class(lowerCamelCase_ )
                  snake_case__						=				model.encode(inputs_dict["input_ids"]		,	inputs_dict["attention_mask"] )

                  snake_case__						=				{
                      "decoder_input_ids": inputs_dict["decoder_input_ids"],
                      "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                      "encoder_outputs": encoder_outputs,
                  }

                  @jax.jit
                  def decode_jitted(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
                      return model.decode(
                          decoder_input_ids=lowerCamelCase_		,	decoder_attention_mask=lowerCamelCase_		,	encoder_outputs=lowerCamelCase_		,	)

                  with self.subTest("JIT Enabled" ):
                      snake_case__						=				decode_jitted(**lowerCamelCase_ ).to_tuple()

                  with self.subTest("JIT Disabled" ):
                      with jax.disable_jit():
                          snake_case__						=				decode_jitted(**lowerCamelCase_ ).to_tuple()

                  self.assertEqual(len(lowerCamelCase_ )		,	len(lowerCamelCase_ ) )
                  for jitted_output, output in zip(lowerCamelCase_		,	lowerCamelCase_ ):
                      self.assertEqual(jitted_output.shape		,	output.shape )
      @slow
      def A_			(			self ):
          for model_class_name in self.all_model_classes:
              snake_case__						=				model_class_name.from_pretrained("google/pegasus-large"		,	from_pt=lowerCamelCase_ )
              snake_case__						=				np.ones((1, 1) )
              snake_case__						=				model(lowerCamelCase_ )
              self.assertIsNotNone(lowerCamelCase_ )



      @slow
      def A_			(			self ):
          snake_case__						=				FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum" )
          snake_case__						=				PegasusTokenizer.from_pretrained("google/pegasus-xsum" )

          snake_case__						=				[
              " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
              " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
          ]

          snake_case__						=				[
              "California\'s largest electricity provider has turned off power to hundreds of thousands of customers.",
              "Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.",
          ]

          snake_case__						=				tokenizer(lowerCamelCase_		,	return_tensors="np"		,	truncation=lowerCamelCase_		,	max_length=5_12		,	padding=lowerCamelCase_ )
          snake_case__						=				model.generate(**lowerCamelCase_		,	num_beams=2 ).sequences
          snake_case__						=				tokenizer.batch_decode(lowerCamelCase_		,	skip_special_tokens=lowerCamelCase_ )
          assert tgt_text == decoded








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"



