





'''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
  __lowerCAmelCase							:	Any			="platform"
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel






@require_flax
class    UpperCAmelCase	:
 __lowercase   		=				PegasusConfig
 __lowercase   		=				{}
 __lowercase   		=				"""gelu"""
 def __init__(				self      :Tuple						,       lowercase_      :List[str]						,       lowercase_      :int=13						,       lowercase_      :List[Any]=7						,       lowercase_      :Tuple=True						,       lowercase_      :Tuple=False						,       lowercase_      :List[Any]=99						,       lowercase_      :Optional[Any]=32						,       lowercase_      :Union[str, Any]=5						,       lowercase_      :Union[str, Any]=4						,       lowercase_      :List[Any]=37						,       lowercase_      :List[str]=0.1						,       lowercase_      :List[Any]=0.1						,       lowercase_      :Any=20						,       lowercase_      :List[Any]=2						,       lowercase_      :Optional[int]=1						,       lowercase_      :Optional[Any]=0						,       )->							str:
     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      UpperCAmelCase_							(				self      :Any							)->							int:
     A__				=			ids_tensor([self.batch_size, self.seq_length - 1]						,       self.vocab_size							).clip(3						,       self.vocab_size							)
     A__				=			np.expand_dims(np.array([self.eos_token_id] * self.batch_size							)						,       1							)
     A__				=			np.concatenate([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(lowercase_						,       lowercase_						,       lowercase_							)
     return config, inputs_dict
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :int						,       lowercase_      :Union[str, Any]						,       lowercase_      :int							)->							int:
     A__				=			20
     A__				=			model_class_name(lowercase_							)

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

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

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

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

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

     A__				=			model.decode(lowercase_						,       lowercase_							)

     A__				=			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      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :str						,       lowercase_      :List[Any]						,       lowercase_      :Dict							)->							Optional[Any]:
     A__				=			20
     A__				=			model_class_name(lowercase_							)

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

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

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

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

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

     A__				=			model.decode(lowercase_						,       lowercase_						,       decoder_attention_mask=lowercase_							)

     A__				=			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 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					str=None						,       _lowerCamelCase					:					Tuple=None						,       ):
    if attention_mask is None:
        A__				=			np.not_equal(_lowerCamelCase						,       config.pad_token_id       ).astype(np.inta       )
    if decoder_attention_mask is None:
        A__				=			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	(       UpperCamelCase__			,       unittest.TestCase				):
 __lowercase   		=				(
     (
         FlaxPegasusForConditionalGeneration,
         FlaxPegasusModel,
     )
     if is_flax_available()
     else ()
 )
 __lowercase   		=				(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
 __lowercase   		=				True
 __lowercase   		=				False
 __lowercase   		=				False
 __lowercase   		=				False
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							List[Any]:
     A__				=			FlaxPegasusModelTester(self							)
     A__				=			ConfigTester(self						,       config_class=lowercase_							)
 def      UpperCAmelCase_							(				self      :Any							)->							int:
     self.config_tester.run_common_tests()
 def      UpperCAmelCase_							(				self      :Tuple							)->							Optional[int]:
     A__,	A__				=			self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(lowercase_						,       lowercase_						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							List[Any]:
     A__,	A__				=			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(lowercase_						,       lowercase_						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							int:
     A__,	A__				=			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__							):
             A__				=			self._prepare_for_class(lowercase_						,       lowercase_							)
             A__				=			model_class(lowercase_							)

             @jax.jit
             def encode_jitted(lowercase_      :Dict						,       lowercase_      :Any=None						,       **lowercase_      :Any							):
                 return model.encode(input_ids=lowercase_						,       attention_mask=lowercase_							)

             with self.subTest("JIT Enabled"							):
                 A__				=			encode_jitted(**lowercase_							).to_tuple()

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

             self.assertEqual(len(lowercase_							)						,       len(lowercase_							)							)
             for jitted_output, output in zip(lowercase_						,       lowercase_							):
                 self.assertEqual(jitted_output.shape						,       output.shape							)
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							int:
     A__,	A__				=			self.model_tester.prepare_config_and_inputs_for_common()

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

             A__				=			{
                 "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(lowercase_      :str						,       lowercase_      :str						,       lowercase_      :Optional[Any]							):
                 return model.decode(
                     decoder_input_ids=lowercase_						,       decoder_attention_mask=lowercase_						,       encoder_outputs=lowercase_						,       )

             with self.subTest("JIT Enabled"							):
                 A__				=			decode_jitted(**lowercase_							).to_tuple()

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

             self.assertEqual(len(lowercase_							)						,       len(lowercase_							)							)
             for jitted_output, output in zip(lowercase_						,       lowercase_							):
                 self.assertEqual(jitted_output.shape						,       output.shape							)
 @slow
 def      UpperCAmelCase_							(				self      :str							)->							Any:
     for model_class_name in self.all_model_classes:
         A__				=			model_class_name.from_pretrained("google/pegasus-large"						,       from_pt=lowercase_							)
         A__				=			np.ones((1, 1)							)
         A__				=			model(lowercase_							)
         self.assertIsNotNone(lowercase_							)



 @slow
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Any:
     A__				=			FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"							)
     A__				=			PegasusTokenizer.from_pretrained("google/pegasus-xsum"							)

     A__				=			[
         " 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!\" ",
     ]

     A__				=			[
         "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.",
     ]

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










'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import CLIPImageProcessor, CLIPProcessor






@require_vision
class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Tuple:
     A__				=			tempfile.mkdtemp()

     # 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(lowercase_						,       range(len(lowercase_							)							)							)							)
     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(lowercase_							) + "\n"							)
     with open(self.merges_file						,       "w"						,       encoding="utf-8"							) as fp:
         fp.write("\n".join(lowercase_							)							)

     A__				=			{
         "do_resize": True,
         "size": 20,
         "do_center_crop": True,
         "crop_size": 18,
         "do_normalize": True,
         "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
         "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
     }
     A__				=			os.path.join(self.tmpdirname						,       lowercase_							)
     with open(self.image_processor_file						,       "w"						,       encoding="utf-8"							) as fp:
         json.dump(lowercase_						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :Any						,       **lowercase_      :Union[str, Any]							)->							Tuple:
     return CLIPTokenizer.from_pretrained(self.tmpdirname						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :Any						,       **lowercase_      :Tuple							)->							Dict:
     return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :Dict						,       **lowercase_      :Union[str, Any]							)->							Any:
     return CLIPImageProcessor.from_pretrained(self.tmpdirname						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							int:
     shutil.rmtree(self.tmpdirname							)
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Optional[int]:
     A__				=			[np.random.randint(2_55						,       size=(3, 30, 4_00)						,       dtype=np.uinta							)]

     A__				=			[Image.fromarray(np.moveaxis(lowercase_						,       0						,       -1							)							) for x in image_inputs]

     return image_inputs
 def      UpperCAmelCase_							(				self      :int							)->							List[Any]:
     A__				=			self.get_tokenizer()
     A__				=			self.get_rust_tokenizer()
     A__				=			self.get_image_processor()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)
     processor_slow.save_pretrained(self.tmpdirname							)
     A__				=			CLIPProcessor.from_pretrained(self.tmpdirname						,       use_fast=lowercase_							)

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)
     processor_fast.save_pretrained(self.tmpdirname							)
     A__				=			CLIPProcessor.from_pretrained(self.tmpdirname							)

     self.assertEqual(processor_slow.tokenizer.get_vocab()						,       tokenizer_slow.get_vocab()							)
     self.assertEqual(processor_fast.tokenizer.get_vocab()						,       tokenizer_fast.get_vocab()							)
     self.assertEqual(tokenizer_slow.get_vocab()						,       tokenizer_fast.get_vocab()							)
     self.assertIsInstance(processor_slow.tokenizer						,       lowercase_							)
     self.assertIsInstance(processor_fast.tokenizer						,       lowercase_							)

     self.assertEqual(processor_slow.image_processor.to_json_string()						,       image_processor.to_json_string()							)
     self.assertEqual(processor_fast.image_processor.to_json_string()						,       image_processor.to_json_string()							)
     self.assertIsInstance(processor_slow.image_processor						,       lowercase_							)
     self.assertIsInstance(processor_fast.image_processor						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Optional[int]:
     A__				=			CLIPProcessor(tokenizer=self.get_tokenizer()						,       image_processor=self.get_image_processor()							)
     processor.save_pretrained(self.tmpdirname							)

     A__				=			self.get_tokenizer(bos_token="(BOS)"						,       eos_token="(EOS)"							)
     A__				=			self.get_image_processor(do_normalize=lowercase_						,       padding_value=1.0							)

     A__				=			CLIPProcessor.from_pretrained(
         self.tmpdirname						,       bos_token="(BOS)"						,       eos_token="(EOS)"						,       do_normalize=lowercase_						,       padding_value=1.0							)

     self.assertEqual(processor.tokenizer.get_vocab()						,       tokenizer_add_kwargs.get_vocab()							)
     self.assertIsInstance(processor.tokenizer						,       lowercase_							)

     self.assertEqual(processor.image_processor.to_json_string()						,       image_processor_add_kwargs.to_json_string()							)
     self.assertIsInstance(processor.image_processor						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Tuple:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			self.prepare_image_inputs()

     A__				=			image_processor(lowercase_						,       return_tensors="np"							)
     A__				=			processor(images=lowercase_						,       return_tensors="np"							)

     for key in input_image_proc.keys():
         self.assertAlmostEqual(input_image_proc[key].sum()						,       input_processor[key].sum()						,       delta=1E-2							)
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Dict:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			"lower newer"

     A__				=			processor(text=lowercase_							)

     A__				=			tokenizer(lowercase_							)

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]						,       encoded_processor[key]							)
 def      UpperCAmelCase_							(				self      :str							)->							Any:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			"lower newer"
     A__				=			self.prepare_image_inputs()

     A__				=			processor(text=lowercase_						,       images=lowercase_							)

     self.assertListEqual(list(inputs.keys()							)						,       ["input_ids", "attention_mask", "pixel_values"]							)

     # test if it raises when no input is passed
     with pytest.raises(lowercase_							):
         processor()
 def      UpperCAmelCase_							(				self      :Tuple							)->							Tuple:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     A__				=			processor.batch_decode(lowercase_							)
     A__				=			tokenizer.batch_decode(lowercase_							)

     self.assertListEqual(lowercase_						,       lowercase_							)



 def      UpperCAmelCase_							(				self      :List[Any]							)->							Dict:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			"lower newer"
     A__				=			self.prepare_image_inputs()

     A__				=			processor(text=lowercase_						,       images=lowercase_							)

     self.assertListEqual(list(inputs.keys()							)						,       processor.model_input_names							)



