





"""simple docstring"""
import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							CLIPTokenizer
					UpperCamelCase      =							CLIPTokenizerFast
					UpperCamelCase      =							True
					UpperCamelCase      =							{}
					UpperCamelCase      =							False


					def 	lowerCamelCase       (					self			:List[str]		):
												super().setUp()

												# fmt: off
												A		=  ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
												# fmt: on
												A		=  dict(zip(__UpperCamelCase     ,       range(len(__UpperCamelCase		)		)		)		)
												A		=  ["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
												A		=  {"unk_token": "<unk>"}

												A		=  os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES["vocab_file"]		)
												A		=  os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES["merges_file"]		)
												with open(self.vocab_file     ,       "w"     ,       encoding="utf-8"		) as fp:
																			fp.write(json.dumps(__UpperCamelCase		) + "\n"		)
												with open(self.merges_file     ,       "w"     ,       encoding="utf-8"		) as fp:
																			fp.write("\n".join(__UpperCamelCase		)		)


					def 	lowerCamelCase       (					self			:Tuple     ,       **__UpperCamelCase			:int		):
												kwargs.update(self.special_tokens_map		)
												return CLIPTokenizer.from_pretrained(self.tmpdirname     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[Any]     ,       **__UpperCamelCase			:Tuple		):
												kwargs.update(self.special_tokens_map		)
												return CLIPTokenizerFast.from_pretrained(self.tmpdirname     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:Any		):
												A		=  "lower newer"
												A		=  "lower newer"
												return input_text, output_text


					def 	lowerCamelCase       (					self			:List[Any]		):
												A		=  CLIPTokenizer(self.vocab_file     ,       self.merges_file     ,       **self.special_tokens_map		)
												A		=  "lower newer"
												A		=  ["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
												A		=  tokenizer.tokenize(__UpperCamelCase		)
												self.assertListEqual(__UpperCamelCase     ,       __UpperCamelCase		)

												A		=  tokens + [tokenizer.unk_token]
												A		=  [10, 2, 16, 9, 3, 2, 16, 20]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					@require_ftfy
					def 	lowerCamelCase       (					self			:Optional[int]		):
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
																										A		=  self.tokenizer_class.from_pretrained(__UpperCamelCase     ,       **__UpperCamelCase		)
																										A		=  self.rust_tokenizer_class.from_pretrained(__UpperCamelCase     ,       **__UpperCamelCase		)

																										A		=  "A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
																										A		=  tokenizer_s.tokenize(__UpperCamelCase		)
																										A		=  tokenizer_r.tokenize(__UpperCamelCase		)

																										self.assertListEqual(__UpperCamelCase     ,       __UpperCamelCase		)

																										# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
																										# with Tilde) encoded in 2 different ways
																										A		=  "xa\u0303y" + " " + "x\xe3y"
																										A		=  tokenizer_s.tokenize(__UpperCamelCase		)
																										A		=  tokenizer_r.tokenize(__UpperCamelCase		)

																										self.assertListEqual(__UpperCamelCase     ,       __UpperCamelCase		)

																										# Test that the tokenization is identical on unicode of space type
																										A		=  [
																										    "\u0009",  # (horizontal tab, '\t')
																										    "\u000B",  # (vertical tab)
																										    "\u000C",  # (form feed)
																										    "\u0020",  # (space, ' ')
																										    "\u200E",  # (left-to-right mark):w
																										    "\u200F",  # (right-to-left mark)
																										]
																										for unicode_seq in spaces_unicodes:
																																	A		=  tokenizer_s.tokenize(__UpperCamelCase		)
																																	A		=  tokenizer_r.tokenize(__UpperCamelCase		)

																																	self.assertListEqual(__UpperCamelCase     ,       __UpperCamelCase		)

																										# Test that the tokenization is identical on unicode of line break type
																										A		=  [
																										    "\u000A",  # (line feed, '\n')
																										    "\r\n",  # (carriage return and line feed, '\r\n')
																										    "\u000D",  # (carriage return, '\r')
																										    "\r",  # (carriage return, '\r')
																										    "\u000D",  # (carriage return, '\r')
																										    "\u2028",  # (line separator)
																										    "\u2029",  # (paragraph separator)
																										    # "\u0085", # (next line)
																										]

																										# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
																										# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
																										# space (and thus into an empty list).

																										for unicode_seq in line_break_unicodes:
																																	A		=  tokenizer_s.tokenize(__UpperCamelCase		)
																																	A		=  tokenizer_r.tokenize(__UpperCamelCase		)

																																	self.assertListEqual(__UpperCamelCase     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
																										A		=  "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
																										A		=  f"{text_of_1_token} {text_of_1_token}"

																										A		=  self.rust_tokenizer_class.from_pretrained(
																										    __UpperCamelCase     ,       use_fast=__UpperCamelCase     ,       )
																										A		=  tokenizer_r(__UpperCamelCase     ,       return_offsets_mapping=__UpperCamelCase     ,       add_special_tokens=__UpperCamelCase		)
																										self.assertEqual(encoding.offset_mapping[0]     ,       (0, len(__UpperCamelCase		))		)
																										self.assertEqual(
																										    encoding.offset_mapping[1]     ,       (len(__UpperCamelCase		) + 1, len(__UpperCamelCase		) + 1 + len(__UpperCamelCase		))     ,       )

																										A		=  f" {text}"

																										A		=  self.rust_tokenizer_class.from_pretrained(
																										    __UpperCamelCase     ,       use_fast=__UpperCamelCase     ,       )
																										A		=  tokenizer_r(__UpperCamelCase     ,       return_offsets_mapping=__UpperCamelCase     ,       add_special_tokens=__UpperCamelCase		)
																										self.assertEqual(encoding.offset_mapping[0]     ,       (1, 1 + len(__UpperCamelCase		))		)
																										self.assertEqual(
																										    encoding.offset_mapping[1]     ,       (1 + len(__UpperCamelCase		) + 1, 1 + len(__UpperCamelCase		) + 1 + len(__UpperCamelCase		))     ,       )


					def 	lowerCamelCase       (					self			:Optional[int]		):
												# Test related to the breaking change introduced in transformers v4.17.0
												# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
												with self.assertRaises(__UpperCamelCase		) as context:
																			self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer"		)

												self.assertTrue(
												    context.exception.args[0].startswith(
												        "The `backend_tokenizer` provided does not match the expected format."		)		)


					@require_ftfy
					def 	lowerCamelCase       (					self			:List[str]		):
												super().test_tokenization_python_rust_equals()


					def 	lowerCamelCase       (					self			:Optional[int]		):
												# CLIP always lower cases letters
												pass









"""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   :
					UpperCamelCase      =							PegasusConfig
					UpperCamelCase      =							{}
					UpperCamelCase      =							'''gelu'''


					def __init__(					self			:Union[str, Any]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:str=13     ,       __UpperCamelCase			:List[Any]=7     ,       __UpperCamelCase			:Union[str, Any]=True     ,       __UpperCamelCase			:List[Any]=False     ,       __UpperCamelCase			:Any=99     ,       __UpperCamelCase			:Tuple=32     ,       __UpperCamelCase			:Optional[int]=2     ,       __UpperCamelCase			:Optional[Any]=4     ,       __UpperCamelCase			:Tuple=37     ,       __UpperCamelCase			:Optional[Any]=0.1     ,       __UpperCamelCase			:Tuple=0.1     ,       __UpperCamelCase			:Optional[int]=40     ,       __UpperCamelCase			:Tuple=2     ,       __UpperCamelCase			:Dict=1     ,       __UpperCamelCase			:Any=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			:Tuple		):
												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			:str     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Union[str, Any]		):
												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 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase=None					,  UpperCamelCase=None					,  UpperCamelCase=None					,  UpperCamelCase=None					,  UpperCamelCase=None					,  ):
							if attention_mask is None:
														A		=  tf.cast(tf.math.not_equal(UpperCamelCase					,  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 					_UpperCAmelCase   (	lowercase_     ,							lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
					UpperCamelCase      =							(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
					UpperCamelCase      =							(
					    {
					        '''conversational''': TFPegasusForConditionalGeneration,
					        '''feature-extraction''': TFPegasusModel,
					        '''summarization''': TFPegasusForConditionalGeneration,
					        '''text2text-generation''': TFPegasusForConditionalGeneration,
					        '''translation''': TFPegasusForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					UpperCamelCase      =							True
					UpperCamelCase      =							False
					UpperCamelCase      =							False


					def 	lowerCamelCase       (					self			:int		):
												A		=  TFPegasusModelTester(self		)
												A		=  ConfigTester(self     ,       config_class=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Dict		):
												self.config_tester.run_common_tests()


					def 	lowerCamelCase       (					self			:Any		):
												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 					_UpperCAmelCase   (	unittest.TestCase ):
					UpperCamelCase      =							[
					    ''' 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      =							[
					    '''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      =							'''google/pegasus-xsum'''


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


					@cached_property
					def 	lowerCamelCase       (					self			:Dict		):
												A		=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
												return model


					def 	lowerCamelCase       (					self			:str     ,       **__UpperCamelCase			:str		):
												A		=  self.translate_src_text(**__UpperCamelCase		)
												assert self.expected_text == generated_words


					def 	lowerCamelCase       (					self			:Any     ,       **__UpperCamelCase			:List[str]		):
												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			:Union[str, Any]		):
												self._assert_generated_batch_equal_expected()


