


"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class   _UpperCamelCase  :
							'''simple docstring'''







							__UpperCAmelCase						:    List[str]				      =PegasusConfig
							__UpperCAmelCase						:    List[Any]				      ={}
							__UpperCAmelCase						:    int				      ="""gelu"""

							def __init__(     self    ,   __a    ,   __a=13    ,   __a=7    ,   __a=True    ,   __a=False    ,   __a=99    ,   __a=32    ,   __a=2    ,   __a=4    ,   __a=37    ,   __a=0.1    ,   __a=0.1    ,   __a=40    ,   __a=2    ,   __a=1    ,   __a=0    ,   ):
										__lowerCAmelCase				    =			parent
										__lowerCAmelCase				    =			batch_size
										__lowerCAmelCase				    =			seq_length
										__lowerCAmelCase				    =			is_training
										__lowerCAmelCase				    =			use_labels
										__lowerCAmelCase				    =			vocab_size
										__lowerCAmelCase				    =			hidden_size
										__lowerCAmelCase				    =			num_hidden_layers
										__lowerCAmelCase				    =			num_attention_heads
										__lowerCAmelCase				    =			intermediate_size

										__lowerCAmelCase				    =			hidden_dropout_prob
										__lowerCAmelCase				    =			attention_probs_dropout_prob
										__lowerCAmelCase				    =			max_position_embeddings
										__lowerCAmelCase				    =			eos_token_id
										__lowerCAmelCase				    =			pad_token_id
										__lowerCAmelCase				    =			bos_token_id

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			ids_tensor([self.batch_size, self.seq_length - 1]    ,   self.vocab_size	)
										__lowerCAmelCase				    =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)    ,   1	)
										__lowerCAmelCase				    =			tf.concat([input_ids, eos_tensor]    ,   axis=1	)

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

										__lowerCAmelCase				    =			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				    =			prepare_pegasus_inputs_dict(__a    ,   __a    ,   __a	)
										return config, inputs_dict



							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			TFPegasusModel(config=__a	).get_decoder()
										__lowerCAmelCase				    =			inputs_dict["input_ids"]

										__lowerCAmelCase				    =			input_ids[:1, :]
										__lowerCAmelCase				    =			inputs_dict["attention_mask"][:1, :]
										__lowerCAmelCase				    =			inputs_dict["head_mask"]
										__lowerCAmelCase				    =			1

										# first forward pass
										__lowerCAmelCase				    =			model(__a    ,   attention_mask=__a    ,   head_mask=__a    ,   use_cache=__a	)

										__lowerCAmelCase    ,	__lowerCAmelCase				    =			outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										__lowerCAmelCase				    =			ids_tensor((self.batch_size, 3)    ,   config.vocab_size	)
										__lowerCAmelCase				    =			tf.cast(ids_tensor((self.batch_size, 3)    ,   2	)    ,   tf.inta	)

										# append to next input_ids and
										__lowerCAmelCase				    =			tf.concat([input_ids, next_tokens]    ,   axis=-1	)
										__lowerCAmelCase				    =			tf.concat([attention_mask, next_attn_mask]    ,   axis=-1	)

										__lowerCAmelCase				    =			model(__a    ,   attention_mask=__a	)[0]
										__lowerCAmelCase				    =			model(__a    ,   attention_mask=__a    ,   past_key_values=__a	)[0]

										self.parent.assertEqual(next_tokens.shape[1]    ,   output_from_past.shape[1]	)

										# select random slice
										__lowerCAmelCase				    =			int(ids_tensor((1,)    ,   output_from_past.shape[-1]	)	)
										__lowerCAmelCase				    =			output_from_no_past[:, -3:, random_slice_idx]
										__lowerCAmelCase				    =			output_from_past[:, :, random_slice_idx]

										# test that outputs are equal for slice
										tf.debugging.assert_near(__a    ,   __a    ,   rtol=1e-3	)


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=None    , _UpperCamelCase=None    , _UpperCamelCase=None    , _UpperCamelCase=None    , _UpperCamelCase=None    , ):

			'''simple docstring'''




			if attention_mask is None:
						__lowerCAmelCase				    =			tf.cast(tf.math.not_equal(_UpperCamelCase    , config.pad_token_id      )    , tf.inta      )
			if decoder_attention_mask is None:
						__lowerCAmelCase				    =			tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape    , dtype=tf.inta      ),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]    , config.pad_token_id      )    , tf.inta      ),
						    ]    , axis=-1    , )
			if head_mask is None:
						__lowerCAmelCase				    =			tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
			if decoder_head_mask is None:
						__lowerCAmelCase				    =			tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
			if cross_attn_head_mask is None:
						__lowerCAmelCase				    =			tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}
@require_tf
class   _UpperCamelCase  (   lowerCAmelCase__       ,lowerCAmelCase__       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[int]				      =(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
							__UpperCAmelCase						:    List[str]				      =(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
							__UpperCAmelCase						:    str				      =(
							    {
							        """conversational""": TFPegasusForConditionalGeneration,
							        """feature-extraction""": TFPegasusModel,
							        """summarization""": TFPegasusForConditionalGeneration,
							        """text2text-generation""": TFPegasusForConditionalGeneration,
							        """translation""": TFPegasusForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							__UpperCAmelCase						:    List[str]				      =True
							__UpperCAmelCase						:    Tuple				      =False
							__UpperCAmelCase						:    List[Any]				      =False

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			TFPegasusModelTester(self	)
										__lowerCAmelCase				    =			ConfigTester(self    ,   config_class=__a	)

							def 			snake_case				(     self	):
										self.config_tester.run_common_tests()



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*__a	)
@require_sentencepiece
@require_tokenizers
@require_tf
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[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!\" """,
							]
							__UpperCAmelCase						:    str				      =[
							    """California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
							    """ reduce the risk of wildfires.""",
							    """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
							]  # differs slightly from pytorch, likely due to numerical differences in linear layers
							__UpperCAmelCase						:    int				      ="""google/pegasus-xsum"""

							@cached_property
							def 			snake_case				(     self	):
										return AutoTokenizer.from_pretrained(self.model_name	)

							@cached_property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
										return model

							def 			snake_case				(     self    ,   **__a	):
										__lowerCAmelCase				    =			self.translate_src_text(**__a	)
										assert self.expected_text == generated_words

							def 			snake_case				(     self    ,   **__a	):
										__lowerCAmelCase				    =			self.tokenizer(self.src_text    ,   **__a    ,   padding=__a    ,   return_tensors="tf"	)
										__lowerCAmelCase				    =			self.model.generate(
										    model_inputs.input_ids    ,   attention_mask=model_inputs.attention_mask    ,   num_beams=2    ,   use_cache=__a    ,   )
										__lowerCAmelCase				    =			self.tokenizer.batch_decode(generated_ids.numpy()    ,   skip_special_tokens=__a	)
										return generated_words



							@slow
							def 			snake_case				(     self	):
										self._assert_generated_batch_equal_expected()








"""simple docstring"""


import argparse
import os
import re

import packaging.version


A     :   Any			    =			"examples/"
A     :   Optional[Any]			    =			{
    "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"),
}
A     :   Optional[int]			    =			{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
A     :   List[Any]			    =			"README.md"


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			with open(_UpperCamelCase    , "r"    , encoding="utf-8"    , newline="\n"      ) as f:
						__lowerCAmelCase				    =			f.read()
			__lowerCAmelCase    ,	__lowerCAmelCase				    =			REPLACE_PATTERNS[pattern]
			__lowerCAmelCase				    =			replace.replace("VERSION"    , _UpperCamelCase      )
			__lowerCAmelCase				    =			re_pattern.sub(_UpperCamelCase    , _UpperCamelCase      )
			with open(_UpperCamelCase    , "w"    , encoding="utf-8"    , newline="\n"      ) as f:
						f.write(_UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			for folder, directories, fnames in os.walk(_UpperCamelCase      ):
						# 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(_UpperCamelCase    , _UpperCamelCase      )    , _UpperCamelCase    , pattern="examples"      )


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=False      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"🤗 Transformers currently provides the following architectures"
			__lowerCAmelCase				    =			"1. Want to contribute a new model?"
			with open(_UpperCamelCase    , "r"    , encoding="utf-8"    , newline="\n"      ) as f:
						__lowerCAmelCase				    =			f.readlines()

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

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

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


def _lowerCamelCase	(		):

			'''simple docstring'''




			with open(REPLACE_FILES["init"]    , "r"      ) as f:
						__lowerCAmelCase				    =			f.read()
			__lowerCAmelCase				    =			REPLACE_PATTERNS["init"][0].search(_UpperCamelCase      ).groups()[0]
			return packaging.version.parse(_UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase=False      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			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:
						__lowerCAmelCase				    =			default_version.base_version
			elif patch:
						__lowerCAmelCase				    =			f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
			else:
						__lowerCAmelCase				    =			f"{default_version.major}.{default_version.minor + 1}.0"

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

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


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			get_version()
			__lowerCAmelCase				    =			f"{current_version.major}.{current_version.minor + 1}.0.dev0"
			__lowerCAmelCase				    =			current_version.base_version

			# Check with the user we got that right.
			__lowerCAmelCase				    =			input(f"Which version are we developing now? [{dev_version}]"      )
			if len(_UpperCamelCase      ) == 0:
						__lowerCAmelCase				    =			dev_version

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


if __name__ == "__main__":
	A     :   Union[str, Any]			    =			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.")
	A     :   Dict			    =			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()




