
'''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   a_ :
			lowerCamelCase__	:			List[str]       =    PegasusConfig
			lowerCamelCase__	:			Optional[Any]       =    {}
			lowerCamelCase__	:			Tuple       =    'gelu'




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

				a_      				=						hidden_dropout_prob
				a_      				=						attention_probs_dropout_prob
				a_      				=						max_position_embeddings
				a_      				=						eos_token_id
				a_      				=						pad_token_id
				a_      				=						bos_token_id




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

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

				a_      				=						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					,   )
				a_      				=						prepare_pegasus_inputs_dict(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)
				return config, inputs_dict






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						TFPegasusModel(config=UpperCAmelCase	).get_decoder()
				a_      				=						inputs_dict["""input_ids"""]

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

				# first forward pass
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   head_mask=UpperCAmelCase					,   use_cache=UpperCAmelCase	)

				a_				,  a_      				=						outputs.to_tuple()

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

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

				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase	)[0]
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   past_key_values=UpperCAmelCase	)[0]

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

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

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







def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__=None  ,      A__=None  ,      A__=None  ,      A__=None  ,      A__=None  ,      ):
	if attention_mask is None:
		a_      				=						tf.cast(tf.math.not_equal(A__  ,      config.pad_token_id						)  ,      tf.inta						)
	if decoder_attention_mask is None:
		a_      				=						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:
		a_      				=						tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
	if decoder_head_mask is None:
		a_      				=						tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
	if cross_attn_head_mask is None:
		a_      				=						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   a_ (      UpperCamelCase__		,  UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			Optional[Any]       =    (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
			lowerCamelCase__	:			Any       =    (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
			lowerCamelCase__	:			str       =    (
			    {
			        'conversational': TFPegasusForConditionalGeneration,
			        'feature-extraction': TFPegasusModel,
			        'summarization': TFPegasusForConditionalGeneration,
			        'text2text-generation': TFPegasusForConditionalGeneration,
			        'translation': TFPegasusForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			lowerCamelCase__	:			Union[str, Any]       =    True
			lowerCamelCase__	:			List[str]       =    False
			lowerCamelCase__	:			List[Any]       =    False




			def 					lowerCAmelCase__					(       self	):
				a_      				=						TFPegasusModelTester(self	)
				a_      				=						ConfigTester(self					,   config_class=UpperCAmelCase	)




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






			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase	)







@require_sentencepiece
@require_tokenizers
@require_tf
class   a_ (      unittest.TestCase					):
			lowerCamelCase__	:			int       =    [
			    ' 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__	:			Union[str, Any]       =    [
			    '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
			lowerCamelCase__	:			Union[str, Any]       =    'google/pegasus-xsum'




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




			@cached_property
			def 					lowerCAmelCase__					(       self	):
				a_      				=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
				return model




			def 					lowerCAmelCase__					(       self					,   **UpperCAmelCase	):
				a_      				=						self.translate_src_text(**UpperCAmelCase	)
				assert self.expected_text == generated_words




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






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


'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__											=logging.get_logger(__name__)


lowercase__											={
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

lowercase__											={
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

lowercase__											='</w>'
lowercase__											='@@ '






def      UpperCamelCase_				(      A__						):
	a_      				=						set()
	a_      				=						word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)						)
		a_      				=						char
	return pairs


# Speech2Text2 has no max input length
lowercase__											={'facebook/s2t-wav2vec2-large-en-de': 10_24}







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			List[str]       =    VOCAB_FILES_NAMES
			lowerCamelCase__	:			Optional[Any]       =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__	:			List[Any]       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__	:			int       =    ['input_ids', 'attention_mask']




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase="<s>"					,   UpperCAmelCase="<pad>"					,   UpperCAmelCase="</s>"					,   UpperCAmelCase="<unk>"					,   UpperCAmelCase=False					,   UpperCAmelCase=None					,   **UpperCAmelCase					,   ):
				super().__init__(
				    unk_token=UpperCAmelCase					,   bos_token=UpperCAmelCase					,   eos_token=UpperCAmelCase					,   pad_token=UpperCAmelCase					,   do_lower_case=UpperCAmelCase					,   **UpperCAmelCase					,   )

				a_      				=						do_lower_case

				with open(UpperCAmelCase					,   encoding="""utf-8"""	) as vocab_handle:
					a_      				=						json.load(UpperCAmelCase	)
				a_      				=						{v: k for k, v in self.encoder.items()}

				if merges_file is None:
					logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''	)

					a_      				=						None
					a_      				=						None
				else:
					with open(UpperCAmelCase					,   encoding="""utf-8"""	) as merges_handle:
						a_      				=						merges_handle.read().split("""\n"""	)[:-1]

					a_      				=						[tuple(merge.split()[:2]	) for merge in merges]
					a_      				=						dict(zip(UpperCAmelCase					,   range(len(UpperCAmelCase	)	)	)	)
					a_      				=						{}




			@property
			def 					lowerCAmelCase__					(       self	):
				return len(self.decoder	)




			def 					lowerCAmelCase__					(       self	):
				return dict(self.encoder					,   **self.added_tokens_encoder	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						tuple(token[:-1]	) + (token[-1] + BPE_TOKEN_MERGES,)
				if token in self.cache:
					return self.cache[token]
				a_      				=						get_pairs(UpperCAmelCase	)

				if not pairs:
					return token

				while True:
					a_      				=						min(UpperCAmelCase					,   key=lambda UpperCAmelCase	: self.bpe_ranks.get(UpperCAmelCase					,   float("""inf"""	)	)	)
					if bigram not in self.bpe_ranks:
						break
					a_				,  a_      				=						bigram
					a_      				=						[]
					a_      				=						0
					while i < len(UpperCAmelCase	):
						try:
							a_      				=						word.index(UpperCAmelCase					,   UpperCAmelCase	)
						except ValueError:
							new_word.extend(word[i:]	)
							break
						else:
							new_word.extend(word[i:j]	)
							a_      				=						j

						if word[i] == first and i < len(UpperCAmelCase	) - 1 and word[i + 1] == second:
							new_word.append(first + second	)
							i += 2
						else:
							new_word.append(word[i]	)
							i += 1
					a_      				=						tuple(UpperCAmelCase	)
					a_      				=						new_word
					if len(UpperCAmelCase	) == 1:
						break
					else:
						a_      				=						get_pairs(UpperCAmelCase	)
				a_      				=						""" """.join(UpperCAmelCase	)
				if word == "\n  " + BPE_TOKEN_MERGES:
					a_      				=						"""\n""" + BPE_TOKEN_MERGES

				if word.endswith(UpperCAmelCase	):
					a_      				=						word.replace(UpperCAmelCase					,   """"""	)

				a_      				=						word.replace(""" """					,   UpperCAmelCase	)
				a_      				=						word
				return word




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):

				if self.bpe_ranks is None:
					raise ValueError(
					    """This tokenizer was instantiated without a `merges.txt` file, so"""
					    """ that it can only be used for decoding, not for encoding."""
					    """Make sure to provide `merges.txt` file at instantiation to enable """
					    """encoding."""	)

				if self.do_lower_case:
					a_      				=						text.lower()

				a_      				=						text.split()

				a_      				=						[]
				for token in text:
					if token:
						split_tokens.extend(list(self.bpe(UpperCAmelCase	).split(""" """	)	)	)

				return split_tokens




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				return self.encoder.get(UpperCAmelCase					,   self.encoder.get(self.unk_token	)	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						self.decoder.get(UpperCAmelCase					,   self.unk_token	)
				return result




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						""" """.join(UpperCAmelCase	)

				# make sure @@ tokens are concatenated
				a_      				=						"""""".join(string.split(UpperCAmelCase	)	)

				return string






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None	):
				if not os.path.isdir(UpperCAmelCase	):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
					return
				a_      				=						os.path.join(
				    UpperCAmelCase					,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)
				a_      				=						os.path.join(
				    UpperCAmelCase					,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]	)

				with open(UpperCAmelCase					,   """w"""					,   encoding="""utf-8"""	) as f:
					f.write(json.dumps(self.encoder					,   indent=2					,   sort_keys=UpperCAmelCase					,   ensure_ascii=UpperCAmelCase	) + """\n"""	)

				a_      				=						0
				if self.bpe_ranks is None:
					return (vocab_file,)

				with open(UpperCAmelCase					,   """w"""					,   encoding="""utf-8"""	) as writer:
					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,   key=lambda UpperCAmelCase	: kv[1]	):
						if index != token_index:
							logger.warning(
							    f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
							    """ Please check that the tokenizer is not corrupted!"""	)
							a_      				=						token_index
						writer.write(""" """.join(UpperCAmelCase	) + """\n"""	)
						index += 1

				return (vocab_file, merges_file)
