





"""simple docstring"""


import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel



class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def __init__(		self   :       str					,   __a   :       Dict					,   __a   :       Union[str, Any]=99					,   __a   :       Union[str, Any]=13					,   __a   :       Tuple=7					,   __a   :       List[Any]=9					,   __a   :       Any=True					,   __a   :       List[Any]=True					,   __a   :       Optional[int]=False					,   __a   :       List[Any]=32					,   __a   :       int=5					,   __a   :       Dict=4					,   __a   :       Any=37					,   __a   :       int=8					,   __a   :       List[str]=0.1					,   __a   :       Optional[Any]=0.0_02					,   __a   :       Optional[int]=1					,   __a   :       List[Any]=0					,   __a   :       int=0					,   __a   :       Optional[int]=None					,   __a   :       int=None					,   )		->				Optional[Any]:
        _UpperCamelCase      :	int						=       parent
        _UpperCamelCase      :	str						=       batch_size
        _UpperCamelCase      :	Any						=       encoder_seq_length
        _UpperCamelCase      :	Union[str, Any]						=       decoder_seq_length
        # For common tests
        _UpperCamelCase      :	List[Any]						=       self.decoder_seq_length
        _UpperCamelCase      :	Dict						=       is_training
        _UpperCamelCase      :	Optional[Any]						=       use_attention_mask
        _UpperCamelCase      :	Tuple						=       use_labels
        _UpperCamelCase      :	Union[str, Any]						=       vocab_size
        _UpperCamelCase      :	List[str]						=       hidden_size
        _UpperCamelCase      :	Union[str, Any]						=       num_hidden_layers
        _UpperCamelCase      :	str						=       num_attention_heads
        _UpperCamelCase      :	Optional[int]						=       d_ff
        _UpperCamelCase      :	Any						=       relative_attention_num_buckets
        _UpperCamelCase      :	Union[str, Any]						=       dropout_rate
        _UpperCamelCase      :	Dict						=       initializer_factor
        _UpperCamelCase      :	Optional[Any]						=       eos_token_id
        _UpperCamelCase      :	Optional[int]						=       pad_token_id
        _UpperCamelCase      :	Union[str, Any]						=       decoder_start_token_id
        _UpperCamelCase      :	Optional[int]						=       None
        _UpperCamelCase      :	int						=       decoder_layers

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Optional[Any]:
        return TaConfig.from_pretrained("google/umt5-base"	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       Optional[int]					,   __a   :       str					,   __a   :       Any					,   __a   :       Dict=None					,   __a   :       Optional[int]=None					,   __a   :       Tuple=None					,   __a   :       Optional[int]=None					,   __a   :       Tuple=None					,   )		->				str:
        if attention_mask is None:
              _UpperCamelCase      :	List[str]						=       input_ids.ne(config.pad_token_id	)
        if decoder_attention_mask is None:
              _UpperCamelCase      :	List[str]						=       decoder_input_ids.ne(config.pad_token_id	)
        if head_mask is None:
              _UpperCamelCase      :	Optional[Any]						=       torch.ones(config.num_hidden_layers					,   config.num_attention_heads					,   device=__a	)
        if decoder_head_mask is None:
              _UpperCamelCase      :	str						=       torch.ones(config.num_decoder_layers					,   config.num_attention_heads					,   device=__a	)
        if cross_attn_head_mask is None:
              _UpperCamelCase      :	Union[str, Any]						=       torch.ones(
                  config.num_decoder_layers					,   config.num_attention_heads					,   device=__a	)
        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,
        }

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Tuple:
        _UpperCamelCase      :	str						=       ids_tensor([self.batch_size, self.encoder_seq_length]					,   self.vocab_size	)
        _UpperCamelCase      :	Optional[Any]						=       ids_tensor([self.batch_size, self.decoder_seq_length]					,   self.vocab_size	)

        # we need to clamp the input ids here to avoid having pad token in between
        # this is because for NllbMoe the position_ids are prepared such that
        # all pad tokens have pos id = 2 and rest are between 2..seq_length
        # and the seq_length here is seq_length - num_pad_tokens
        # but when using past, there is no way of knowing if the past input ids had
        # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
        # position_ids being off by num_pad_tokens in past input
        _UpperCamelCase      :	int						=       input_ids.clamp(self.pad_token_id + 1	)
        _UpperCamelCase      :	str						=       decoder_input_ids.clamp(self.pad_token_id + 1	)

        _UpperCamelCase      :	Optional[Any]						=       self.get_config()
        _UpperCamelCase      :	Dict						=       config.num_attention_heads
        _UpperCamelCase      :	Optional[Any]						=       self.prepare_inputs_dict(__a					,   __a					,   __a	)
        return config, input_dict

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Tuple:
        _UpperCamelCase,  _UpperCamelCase      :	List[str]						=       self.prepare_config_and_inputs()
        return config, inputs_dict

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Dict:
        return TaConfig(
            vocab_size=166					,   d_model=self.hidden_size					,   d_ff=self.d_ff					,   d_kv=self.hidden_size // self.num_attention_heads					,   num_layers=self.num_hidden_layers					,   num_decoder_layers=self.decoder_layers					,   num_heads=self.num_attention_heads					,   relative_attention_num_buckets=self.relative_attention_num_buckets					,   dropout_rate=self.dropout_rate					,   initializer_factor=self.initializer_factor					,   eos_token_id=self.eos_token_id					,   bos_token_id=self.pad_token_id					,   pad_token_id=self.pad_token_id					,   decoder_start_token_id=self.decoder_start_token_id					,   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Optional[int]:
        return TaConfig(
            vocab_size=self.vocab_size					,   d_model=self.hidden_size					,   d_ff=self.d_ff					,   d_kv=self.hidden_size // self.num_attention_heads					,   num_layers=self.num_hidden_layers					,   num_decoder_layers=self.decoder_layers					,   num_heads=self.num_attention_heads					,   relative_attention_num_buckets=self.relative_attention_num_buckets					,   dropout_rate=self.dropout_rate					,   initializer_factor=self.initializer_factor					,   eos_token_id=self.eos_token_id					,   bos_token_id=self.pad_token_id					,   pad_token_id=self.pad_token_id					,   decoder_start_token_id=self.decoder_start_token_id					,   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       str					,   __a   :       Optional[Any]					,   __a   :       List[str]					,   __a   :       List[str]					,   __a   :       List[str]					,   __a   :       List[Any]					,   )		->				Union[str, Any]:
        _UpperCamelCase      :	Optional[int]						=       UMTaModel(config=__a	)
        model.to(__a	)
        model.eval()
        _UpperCamelCase      :	Dict						=       model(
            input_ids=__a					,   decoder_input_ids=__a					,   attention_mask=__a					,   decoder_attention_mask=__a					,   )
        _UpperCamelCase      :	Dict						=       model(input_ids=__a					,   decoder_input_ids=__a	)
        _UpperCamelCase      :	List[str]						=       result.last_hidden_state
        _UpperCamelCase      :	str						=       result.past_key_values
        _UpperCamelCase      :	List[Any]						=       result.encoder_last_hidden_state

        self.parent.assertEqual(encoder_output.size()					,   (self.batch_size, self.encoder_seq_length, self.hidden_size)	)
        self.parent.assertEqual(decoder_output.size()					,   (self.batch_size, self.decoder_seq_length, self.hidden_size)	)
        # There should be `num_layers` key value embeddings stored in decoder_past
        self.parent.assertEqual(len(__a	)					,   config.num_layers	)
        # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
        self.parent.assertEqual(len(decoder_past[0]	)					,   4	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       Union[str, Any]					,   __a   :       List[Any]					,   __a   :       Any					,   __a   :       Tuple					,   __a   :       Union[str, Any]					,   __a   :       Optional[int]					,   )		->				Union[str, Any]:
        _UpperCamelCase      :	Optional[Any]						=       UMTaModel(config=__a	).get_decoder().to(__a	).eval()
        # first forward pass
        _UpperCamelCase      :	Dict						=       model(__a					,   use_cache=__a	)
        _UpperCamelCase      :	Dict						=       model(__a	)
        _UpperCamelCase      :	str						=       model(__a					,   use_cache=__a	)

        self.parent.assertTrue(len(__a	) == len(__a	)	)
        self.parent.assertTrue(len(__a	) == len(__a	) + 1	)

        _UpperCamelCase,  _UpperCamelCase      :	List[str]						=       outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        _UpperCamelCase      :	List[str]						=       ids_tensor((self.batch_size, 1)					,   config.vocab_size	)

        # append to next input_ids and
        _UpperCamelCase      :	str						=       torch.cat([input_ids, next_tokens]					,   dim=-1	)

        _UpperCamelCase      :	Optional[Any]						=       model(__a	)["last_hidden_state"]
        _UpperCamelCase      :	Union[str, Any]						=       model(__a					,   past_key_values=__a	)["last_hidden_state"]

        # select random slice
        _UpperCamelCase      :	Any						=       ids_tensor((1,)					,   output_from_past.shape[-1]	).item()
        _UpperCamelCase      :	Union[str, Any]						=       output_from_no_past[:, -1, random_slice_idx].detach()
        _UpperCamelCase      :	int						=       output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__a					,   __a					,   atol=1e-3	)	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       int					,   __a   :       Union[str, Any]					,   )		->				Dict:
        _UpperCamelCase      :	Tuple						=       UMTaModel(config=__a	).to(__a	).half().eval()
        _UpperCamelCase      :	List[Any]						=       model(**__a	)["last_hidden_state"]
        self.parent.assertFalse(torch.isnan(__a	).any().item()	)





@require_torch
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  _UpperCamelCase		,  _UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					(
      (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
  )
  SCREAMING_SNAKE_CASE__							:Dict	     =					(UMTaForConditionalGeneration,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__							:int	     =					(
      {
          "conversational": UMTaForConditionalGeneration,
          "feature-extraction": UMTaModel,
          "summarization": UMTaForConditionalGeneration,
          "text2text-generation": UMTaForConditionalGeneration,
          "translation": UMTaForConditionalGeneration,
          "question-answering": UMTaForQuestionAnswering,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__							:Dict	     =					True
  SCREAMING_SNAKE_CASE__							:Dict	     =					False
  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					False
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					True
  SCREAMING_SNAKE_CASE__							:List[str]	     =					True
  # The small UMT5 model needs higher percentages for CPU/MP tests
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					[0.8, 0.9]

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Optional[int]:
        _UpperCamelCase      :	Optional[int]						=       UMTaModelTester(self	)

  @unittest.skip("Test has a segmentation fault on torch 1.8.0"	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Union[str, Any]:
        _UpperCamelCase      :	Dict						=       self.model_tester.prepare_config_and_inputs()
        _UpperCamelCase      :	int						=       UMTaModel(config_and_inputs[0]	).to(__a	)
        with tempfile.TemporaryDirectory() as tmpdirname:
              torch.onnx.export(
                  __a					,   (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])					,   F'''{tmpdirname}/t5_test.onnx'''					,   export_params=__a					,   opset_version=9					,   input_names=["input_ids", "decoder_input_ids"]					,   )

  @unittest.skipIf(torch_device == "cpu"					,   "Cant do half precision"	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				List[str]:
        _UpperCamelCase      :	Tuple						=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fpaa_forward(*__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Tuple:
        _UpperCamelCase      :	int						=       ["encoder_attentions", "decoder_attentions", "cross_attentions"]
        _UpperCamelCase      :	List[str]						=       self.model_tester.prepare_config_and_inputs()
        _UpperCamelCase      :	List[Any]						=       config_and_inputs[0]
        _UpperCamelCase      :	Optional[Any]						=       UMTaForConditionalGeneration(__a	).eval()
        model.to(__a	)

        _UpperCamelCase      :	Dict						=       {
            "head_mask": torch.zeros(config.num_layers					,   config.num_heads					,   device=__a	),
            "decoder_head_mask": torch.zeros(config.num_decoder_layers					,   config.num_heads					,   device=__a	),
            "cross_attn_head_mask": torch.zeros(config.num_decoder_layers					,   config.num_heads					,   device=__a	),
        }

        for attn_name, (name, mask) in zip(__a					,   head_masking.items()	):
              _UpperCamelCase      :	List[Any]						=       {name: mask}
              # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
              if name == "head_mask":
                    _UpperCamelCase      :	int						=       torch.ones(
                        config.num_decoder_layers					,   config.num_heads					,   device=__a	)

              _UpperCamelCase      :	Any						=       model.generate(
                  config_and_inputs[1]["input_ids"]					,   num_beams=1					,   max_length=3					,   output_attentions=__a					,   return_dict_in_generate=__a					,   **__a					,   )
              # We check the state of decoder_attentions and cross_attentions just from the last step
              _UpperCamelCase      :	Dict						=       out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
              self.assertEqual(sum([w.sum().item() for w in attn_weights]	)					,   0.0	)


  @unittest.skip("Does not work on the tiny model as we keep hitting edge cases."	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Tuple:
        pass





@require_torch
@require_sentencepiece
@require_tokenizers
class      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







  '''simple docstring'''


  @slow
  @unittest.skip(
      "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Optional[Any]:
        _UpperCamelCase      :	Dict						=       UMTaForConditionalGeneration.from_pretrained("google/umt5-small"					,   return_dict=__a	).to(__a	)
        _UpperCamelCase      :	List[str]						=       AutoTokenizer.from_pretrained("google/umt5-small"					,   use_fast=__a					,   legacy=__a	)
        _UpperCamelCase      :	List[Any]						=       [
            "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
            "No se como puedo <extra_id_0>.",
            "This is the reason why we <extra_id_0> them.",
            "The <extra_id_0> walks in <extra_id_1>, seats",
            "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
        ]
        _UpperCamelCase      :	Tuple						=       tokenizer(__a					,   return_tensors="pt"					,   padding=__a	).input_ids
        # fmt: off
        _UpperCamelCase      :	Tuple						=       torch.tensor(
            [
                [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
            ]	)
        # fmt: on
        torch.testing.assert_allclose(__a					,   __a	)

        _UpperCamelCase      :	Tuple						=       model.generate(input_ids.to(__a	)	)
        _UpperCamelCase      :	Union[str, Any]						=       [
            "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
            "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
            "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
            "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
            "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
        ]
        _UpperCamelCase      :	List[str]						=       tokenizer.batch_decode(__a	)
        self.assertEqual(__a					,   __a	)







"""simple docstring"""


from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
  lowerCamelCase__				  =   input("Enter image url: ").strip()
  print(f"""Downloading image from {url} ...""")
  lowerCamelCase__				  =   BeautifulSoup(requests.get(url).content, "html.parser")
  # The image URL is in the content field of the first meta tag with property og:image
  lowerCamelCase__				  =   soup.find("meta", {"property": "og:image"})["content"]
  lowerCamelCase__				  =   requests.get(image_url).content
  lowerCamelCase__				  =   f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
  with open(file_name, "wb") as fp:
    fp.write(image_data)
  print(f"""Done. Image saved to disk as {file_name}.""")
