
'''simple docstring'''






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
 UpperCAmelCase_							: List[str] =							'platform'
 import jax
 import jax.numpy as jnp
 import numpy as np

 from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class 			UpperCAmelCase__    :
    lowerCAmelCase_					     =		PegasusConfig
    lowerCAmelCase_					     =		{}
    lowerCAmelCase_					     =		'gelu'



    def __init__(						self   :							Optional[int],__A   :							Tuple,__A   :							Dict=1_3,__A   :							List[Any]=7,__A   :							int=True,__A   :							Optional[int]=False,__A   :							Any=9_9,__A   :							Any=3_2,__A   :							Any=5,__A   :							List[Any]=4,__A   :							Tuple=3_7,__A   :							int=0.1,__A   :							Optional[Any]=0.1,__A   :							int=2_0,__A   :							Optional[Any]=2,__A   :							List[Any]=1,__A   :							Optional[int]=0,):
          _lowerCamelCase					: List[str]		    = parent
          _lowerCamelCase					: str		    = batch_size
          _lowerCamelCase					: Dict		    = seq_length
          _lowerCamelCase					: Dict		    = is_training
          _lowerCamelCase					: Dict		    = use_labels
          _lowerCamelCase					: int		    = vocab_size
          _lowerCamelCase					: int		    = hidden_size
          _lowerCamelCase					: Tuple		    = num_hidden_layers
          _lowerCamelCase					: Any		    = num_attention_heads
          _lowerCamelCase					: List[str]		    = intermediate_size

          _lowerCamelCase					: str		    = hidden_dropout_prob
          _lowerCamelCase					: int		    = attention_probs_dropout_prob
          _lowerCamelCase					: Union[str, Any]		    = max_position_embeddings
          _lowerCamelCase					: Tuple		    = eos_token_id
          _lowerCamelCase					: Optional[int]		    = pad_token_id
          _lowerCamelCase					: Union[str, Any]		    = bos_token_id



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: int		    = ids_tensor([self.batch_size, self.seq_length - 1],self.vocab_size  ).clip(3,self.vocab_size  )
          _lowerCamelCase					: Optional[int]		    = np.expand_dims(np.array([self.eos_token_id] * self.batch_size  ),1  )
          _lowerCamelCase					: List[str]		    = np.concatenate([input_ids, eos_tensor],axis=1  )

          _lowerCamelCase					: List[str]		    = ids_tensor([self.batch_size, self.seq_length],self.vocab_size  )

          _lowerCamelCase					: Any		    = 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,)
          _lowerCamelCase					: List[Any]		    = prepare_pegasus_inputs_dict(__A,__A,__A  )
          return config, inputs_dict



    def 	lowerCamelCase_	(						self   :							str,__A   :							List[Any],__A   :							List[str],__A   :							Dict  ):
          _lowerCamelCase					: Union[str, Any]		    = 2_0
          _lowerCamelCase					: List[str]		    = model_class_name(__A  )

          _lowerCamelCase					: Optional[Any]		    = model.encode(inputs_dict["input_ids"]  )

          _lowerCamelCase    ,		_lowerCamelCase					: str		    = (
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          _lowerCamelCase					: List[Any]		    = model.init_cache(decoder_input_ids.shape[0],__A,__A  )
          _lowerCamelCase					: List[str]		    = jnp.ones((decoder_input_ids.shape[0], max_decoder_length),dtype="i4"  )

          _lowerCamelCase					: List[Any]		    = jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :],(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),)
          _lowerCamelCase					: Union[str, Any]		    = model.decode(
              decoder_input_ids[:, :-1],__A,decoder_attention_mask=__A,past_key_values=__A,decoder_position_ids=__A,)

          _lowerCamelCase					: Union[str, Any]		    = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],dtype="i4"  )
          _lowerCamelCase					: Union[str, Any]		    = model.decode(
              decoder_input_ids[:, -1:],__A,decoder_attention_mask=__A,past_key_values=outputs_cache.past_key_values,decoder_position_ids=__A,)

          _lowerCamelCase					: List[Any]		    = model.decode(__A,__A  )

          _lowerCamelCase					: List[Any]		    = 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 	lowerCamelCase_	(						self   :							Any,__A   :							List[str],__A   :							Tuple,__A   :							Dict  ):
          _lowerCamelCase					: str		    = 2_0
          _lowerCamelCase					: Union[str, Any]		    = model_class_name(__A  )

          _lowerCamelCase					: Dict		    = model.encode(inputs_dict["input_ids"]  )

          _lowerCamelCase    ,		_lowerCamelCase					: Union[str, Any]		    = (
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

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

          _lowerCamelCase					: Optional[int]		    = model.init_cache(decoder_input_ids.shape[0],__A,__A  )
          _lowerCamelCase					: Tuple		    = jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :],(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),)

          _lowerCamelCase					: Tuple		    = model.decode(
              decoder_input_ids[:, :-1],__A,decoder_attention_mask=__A,past_key_values=__A,decoder_position_ids=__A,)
          _lowerCamelCase					: Any		    = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],dtype="i4"  )
          _lowerCamelCase					: int		    = model.decode(
              decoder_input_ids[:, -1:],__A,past_key_values=outputs_cache.past_key_values,decoder_attention_mask=__A,decoder_position_ids=__A,)

          _lowerCamelCase					: Optional[Any]		    = model.decode(__A,__A,decoder_attention_mask=__A  )

          _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}'  )







def 		A_							(						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Dict					,						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     int=None					,						_lowerCAmelCase :     Tuple=None					,						):



      """simple docstring"""




      if attention_mask is None:
            _lowerCamelCase					: List[str]		    = np.not_equal(_lowerCAmelCase					,						config.pad_token_id				).astype(np.inta				)
      if decoder_attention_mask is None:
            _lowerCamelCase					: List[Any]		    = 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 			UpperCAmelCase__    (							A    ,						unittest.TestCase			):
    lowerCAmelCase_					     =		(
        (
            FlaxPegasusForConditionalGeneration,
            FlaxPegasusModel,
        )
        if is_flax_available()
        else ()
    )
    lowerCAmelCase_					     =		(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
    lowerCAmelCase_					     =		True
    lowerCAmelCase_					     =		False
    lowerCAmelCase_					     =		False
    lowerCAmelCase_					     =		False



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          _lowerCamelCase					: List[str]		    = FlaxPegasusModelTester(self  )
          _lowerCamelCase					: Dict		    = ConfigTester(self,config_class=__A  )



    def 	lowerCamelCase_	(						self   :							Optional[int]  ):
          self.config_tester.run_common_tests()



    def 	lowerCamelCase_	(						self   :							str  ):
          _lowerCamelCase    ,		_lowerCamelCase					: Union[str, Any]		    = self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward(__A,__A,__A  )



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase    ,		_lowerCamelCase					: str		    = 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(__A,__A,__A  )



    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase    ,		_lowerCamelCase					: Optional[Any]		    = self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__  ):
                      _lowerCamelCase					: Optional[int]		    = self._prepare_for_class(__A,__A  )
                      _lowerCamelCase					: Union[str, Any]		    = model_class(__A  )

                      @jax.jit
                      def encode_jitted(__A   :							Union[str, Any],__A   :							Dict=None,**__A   :							Union[str, Any]  ):
                            return model.encode(input_ids=__A,attention_mask=__A  )

                      with self.subTest("JIT Enabled"  ):
                            _lowerCamelCase					: Dict		    = encode_jitted(**__A  ).to_tuple()

                      with self.subTest("JIT Disabled"  ):
                            with jax.disable_jit():
                                  _lowerCamelCase					: List[str]		    = encode_jitted(**__A  ).to_tuple()

                      self.assertEqual(len(__A  ),len(__A  )  )
                      for jitted_output, output in zip(__A,__A  ):
                            self.assertEqual(jitted_output.shape,output.shape  )



    def 	lowerCamelCase_	(						self   :							Optional[int]  ):
          _lowerCamelCase    ,		_lowerCamelCase					: Optional[Any]		    = self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__  ):
                      _lowerCamelCase					: Union[str, Any]		    = model_class(__A  )
                      _lowerCamelCase					: Optional[Any]		    = model.encode(inputs_dict["input_ids"],inputs_dict["attention_mask"]  )

                      _lowerCamelCase					: Optional[int]		    = {
                          "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(__A   :							Optional[int],__A   :							str,__A   :							Optional[Any]  ):
                            return model.decode(
                                decoder_input_ids=__A,decoder_attention_mask=__A,encoder_outputs=__A,)

                      with self.subTest("JIT Enabled"  ):
                            _lowerCamelCase					: Any		    = decode_jitted(**__A  ).to_tuple()

                      with self.subTest("JIT Disabled"  ):
                            with jax.disable_jit():
                                  _lowerCamelCase					: List[str]		    = decode_jitted(**__A  ).to_tuple()

                      self.assertEqual(len(__A  ),len(__A  )  )
                      for jitted_output, output in zip(__A,__A  ):
                            self.assertEqual(jitted_output.shape,output.shape  )



    @slow
    def 	lowerCamelCase_	(						self   :							Dict  ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase					: str		    = model_class_name.from_pretrained("google/pegasus-large",from_pt=__A  )
                _lowerCamelCase					: Union[str, Any]		    = np.ones((1, 1)  )
                _lowerCamelCase					: Optional[Any]		    = model(__A  )
                self.assertIsNotNone(__A  )



    @slow
    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: str		    = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"  )
          _lowerCamelCase					: Dict		    = PegasusTokenizer.from_pretrained("google/pegasus-xsum"  )

          _lowerCamelCase					: Any		    = [
              " 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!\" ",
          ]

          _lowerCamelCase					: Dict		    = [
              "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.",
          ]

          _lowerCamelCase					: Optional[Any]		    = tokenizer(__A,return_tensors="np",truncation=__A,max_length=5_1_2,padding=__A  )
          _lowerCamelCase					: int		    = model.generate(**__A,num_beams=2  ).sequences
          _lowerCamelCase					: Union[str, Any]		    = tokenizer.batch_decode(__A,skip_special_tokens=__A  )
          assert tgt_text == decoded



import argparse
import os
import re

import packaging.version


SCREAMING_SNAKE_CASE__     					=			"examples/"
SCREAMING_SNAKE_CASE__     					=			{
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
SCREAMING_SNAKE_CASE__     					=			{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
SCREAMING_SNAKE_CASE__     					=			"README.md"


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		with open(a     , "r"     , encoding="utf-8"     , newline="\n" ) as f:
				SCREAMING_SNAKE_CASE_				:Dict				     =						f.read()
		SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						REPLACE_PATTERNS[pattern]
		SCREAMING_SNAKE_CASE_				:List[str]				     =						replace.replace("VERSION"     , a )
		SCREAMING_SNAKE_CASE_				:str				     =						re_pattern.sub(a     , a )
		with open(a     , "w"     , encoding="utf-8"     , newline="\n" ) as f:
				f.write(a )


def     lowercase	(    a ):



		'''simple docstring'''




		for folder, directories, fnames in os.walk(a ):
				# Removing some of the folders with non-actively maintained examples from the walk
				if "research_projects" in directories:
						directories.remove("research_projects" )
				if "legacy" in directories:
						directories.remove("legacy" )
				for fname in fnames:
						if fname.endswith(".py" ):
								update_version_in_file(os.path.join(a     , a )     , a     , pattern="examples" )


def     lowercase	(    a     , a=False ):



		'''simple docstring'''




		for pattern, fname in REPLACE_FILES.items():
				update_version_in_file(a     , a     , a )
		if not patch:
				update_version_in_examples(a )


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						"🤗 Transformers currently provides the following architectures"
		SCREAMING_SNAKE_CASE_				:Any				     =						"1. Want to contribute a new model?"
		with open(a     , "r"     , encoding="utf-8"     , newline="\n" ) as f:
				SCREAMING_SNAKE_CASE_				:List[str]				     =						f.readlines()

		# Find the start of the list.
		SCREAMING_SNAKE_CASE_				:int				     =						0
		while not lines[start_index].startswith(_start_prompt ):
				start_index += 1
		start_index += 1

		SCREAMING_SNAKE_CASE_				:int				     =						start_index
		# Update the lines in the model list.
		while not lines[index].startswith(_end_prompt ):
				if lines[index].startswith("1." ):
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						lines[index].replace(
						    "https://huggingface.co/docs/transformers/main/model_doc"     , "https://huggingface.co/docs/transformers/model_doc"     , )
				index += 1

		with open(a     , "w"     , encoding="utf-8"     , newline="\n" ) as f:
				f.writelines(a )


def     lowercase	(    ):



		'''simple docstring'''




		with open(REPLACE_FILES["init"]     , "r" ) as f:
				SCREAMING_SNAKE_CASE_				:str				     =						f.read()
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						REPLACE_PATTERNS["init"][0].search(a ).groups()[0]
		return packaging.version.parse(a )


def     lowercase	(    a=False ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:str				     =						get_version()
		if patch and default_version.is_devrelease:
				raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" )
		if default_version.is_devrelease:
				SCREAMING_SNAKE_CASE_				:int				     =						default_version.base_version
		elif patch:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
		else:
				SCREAMING_SNAKE_CASE_				:List[str]				     =						F"{default_version.major}.{default_version.minor + 1}.0"

		# Now let's ask nicely if that's the right one.
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						input(F"Which version are you releasing? [{default_version}]" )
		if len(a ) == 0:
				SCREAMING_SNAKE_CASE_				:Any				     =						default_version

		print(F"Updating version to {version}." )
		global_version_update(a     , patch=a )
		if not patch:
				print("Cleaning main README, don't forget to run `make fix-copies`." )
				clean_main_ref_in_model_list()


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						get_version()
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						F"{current_version.major}.{current_version.minor + 1}.0.dev0"
		SCREAMING_SNAKE_CASE_				:str				     =						current_version.base_version

		# Check with the user we got that right.
		SCREAMING_SNAKE_CASE_				:Any				     =						input(F"Which version are we developing now? [{dev_version}]" )
		if len(a ) == 0:
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						dev_version

		print(F"Updating version to {version}." )
		global_version_update(a )
		print("Cleaning main README, don't forget to run `make fix-copies`." )
		clean_main_ref_in_model_list()


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE__     					=			argparse.ArgumentParser()
		parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
		parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
		SCREAMING_SNAKE_CASE__     					=			parser.parse_args()
		if not args.post_release:
				pre_release_work(patch=args.patch)
		elif args.patch:
				print("Nothing to do after a patch :-)")
		else:
				post_release_work()






