
"""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 __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase=99			,  __UpperCAmelCase=13			,  __UpperCAmelCase=7			,  __UpperCAmelCase=9			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase=False			,  __UpperCAmelCase=32			,  __UpperCAmelCase=5			,  __UpperCAmelCase=4			,  __UpperCAmelCase=37			,  __UpperCAmelCase=8			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.0_0_2			,  __UpperCAmelCase=1			,  __UpperCAmelCase=0			,  __UpperCAmelCase=0			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= parent
      __UpperCamelCase       						= batch_size
      __UpperCamelCase       						= encoder_seq_length
      __UpperCamelCase       						= decoder_seq_length
      # For common tests
      __UpperCamelCase       						= self.decoder_seq_length
      __UpperCamelCase       						= is_training
      __UpperCamelCase       						= use_attention_mask
      __UpperCamelCase       						= use_labels
      __UpperCamelCase       						= vocab_size
      __UpperCamelCase       						= hidden_size
      __UpperCamelCase       						= num_hidden_layers
      __UpperCamelCase       						= num_attention_heads
      __UpperCamelCase       						= d_ff
      __UpperCamelCase       						= relative_attention_num_buckets
      __UpperCamelCase       						= dropout_rate
      __UpperCamelCase       						= initializer_factor
      __UpperCamelCase       						= eos_token_id
      __UpperCamelCase       						= pad_token_id
      __UpperCamelCase       						= decoder_start_token_id
      __UpperCamelCase       						= None
      __UpperCamelCase       						= decoder_layers





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return TaConfig.from_pretrained('google/umt5-base'   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  ):

      '''simple docstring'''




      if attention_mask is None:
       __UpperCamelCase       						= input_ids.ne(config.pad_token_id   )
      if decoder_attention_mask is None:
       __UpperCamelCase       						= decoder_input_ids.ne(config.pad_token_id   )
      if head_mask is None:
       __UpperCamelCase       						= torch.ones(config.num_hidden_layers			,  config.num_attention_heads			,  device=__UpperCAmelCase   )
      if decoder_head_mask is None:
       __UpperCamelCase       						= torch.ones(config.num_decoder_layers			,  config.num_attention_heads			,  device=__UpperCAmelCase   )
      if cross_attn_head_mask is None:
       __UpperCamelCase       						= torch.ones(
           config.num_decoder_layers			,  config.num_attention_heads			,  device=__UpperCAmelCase   )
      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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= ids_tensor([self.batch_size, self.encoder_seq_length]			,  self.vocab_size   )
      __UpperCamelCase       						= 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       						= input_ids.clamp(self.pad_token_id + 1   )
      __UpperCamelCase       						= decoder_input_ids.clamp(self.pad_token_id + 1   )

      __UpperCamelCase       						= self.get_config()
      __UpperCamelCase       						= config.num_attention_heads
      __UpperCamelCase       						= self.prepare_inputs_dict(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   )
      return config, input_dict





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.prepare_config_and_inputs()
      return config, inputs_dict





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      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 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= UMTaModel(config=__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(
          input_ids=__UpperCAmelCase			,  decoder_input_ids=__UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  decoder_attention_mask=__UpperCAmelCase			,  )
      __UpperCamelCase       						= model(input_ids=__UpperCAmelCase			,  decoder_input_ids=__UpperCAmelCase   )
      __UpperCamelCase       						= result.last_hidden_state
      __UpperCamelCase       						= result.past_key_values
      __UpperCamelCase       						= 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(__UpperCAmelCase   )			,  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 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= UMTaModel(config=__UpperCAmelCase   ).get_decoder().to(__UpperCAmelCase   ).eval()
      # first forward pass
      __UpperCamelCase       						= model(__UpperCAmelCase			,  use_cache=__UpperCAmelCase   )
      __UpperCamelCase       						= model(__UpperCAmelCase   )
      __UpperCamelCase       						= model(__UpperCAmelCase			,  use_cache=__UpperCAmelCase   )

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

      __UpperCamelCase						,			__UpperCamelCase       						= outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      __UpperCamelCase       						= ids_tensor((self.batch_size, 1)			,  config.vocab_size   )

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

      __UpperCamelCase       						= model(__UpperCAmelCase   )['last_hidden_state']
      __UpperCamelCase       						= model(__UpperCAmelCase			,  past_key_values=__UpperCAmelCase   )['last_hidden_state']

      # select random slice
      __UpperCamelCase       						= ids_tensor((1,)			,  output_from_past.shape[-1]   ).item()
      __UpperCamelCase       						= output_from_no_past[:, -1, random_slice_idx].detach()
      __UpperCamelCase       						= output_from_past[:, 0, random_slice_idx].detach()

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







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= UMTaModel(config=__UpperCAmelCase   ).to(__UpperCAmelCase   ).half().eval()
      __UpperCamelCase       						= model(**__UpperCAmelCase   )['last_hidden_state']
      self.parent.assertFalse(torch.isnan(__UpperCAmelCase   ).any().item()   )


@require_torch
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		(
         (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
     )
     lowercase		     =		(UMTaForConditionalGeneration,) if is_torch_available() else ()
     lowercase		     =		(
         {
             "conversational": UMTaForConditionalGeneration,
             "feature-extraction": UMTaModel,
             "summarization": UMTaForConditionalGeneration,
             "text2text-generation": UMTaForConditionalGeneration,
             "translation": UMTaForConditionalGeneration,
             "question-answering": UMTaForQuestionAnswering,
         }
         if is_torch_available()
         else {}
     )
     lowercase		     =		True
     lowercase		     =		False
     lowercase		     =		False
     lowercase		     =		True
     lowercase		     =		True
     # The small UMT5 model needs higher percentages for CPU/MP tests
     lowercase		     =		[0.8, 0.9]





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= UMTaModelTester(self   )





     @unittest.skip('Test has a segmentation fault on torch 1.8.0'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      __UpperCamelCase       						= UMTaModel(config_and_inputs[0]   ).to(__UpperCAmelCase   )
      with tempfile.TemporaryDirectory() as tmpdirname:
       torch.onnx.export(
           __UpperCAmelCase			,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])			,  F'{tmpdirname}/t5_test.onnx'			,  export_params=__UpperCAmelCase			,  opset_version=9			,  input_names=['input_ids', 'decoder_input_ids']			,  )





     @unittest.skipIf(torch_device == 'cpu'			,  'Cant do half precision'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model_fpaa_forward(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      __UpperCamelCase       						= config_and_inputs[0]
      __UpperCamelCase       						= UMTaForConditionalGeneration(__UpperCAmelCase   ).eval()
      model.to(__UpperCAmelCase   )

      __UpperCamelCase       						= {
          'head_mask': torch.zeros(config.num_layers			,  config.num_heads			,  device=__UpperCAmelCase   ),
          'decoder_head_mask': torch.zeros(config.num_decoder_layers			,  config.num_heads			,  device=__UpperCAmelCase   ),
          'cross_attn_head_mask': torch.zeros(config.num_decoder_layers			,  config.num_heads			,  device=__UpperCAmelCase   ),
      }

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

       __UpperCamelCase       						= model.generate(
           config_and_inputs[1]['input_ids']			,  num_beams=1			,  max_length=3			,  output_attentions=__UpperCAmelCase			,  return_dict_in_generate=__UpperCAmelCase			,  **__UpperCAmelCase			,  )
       # We check the state of decoder_attentions and cross_attentions just from the last step
       __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass


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





     @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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= UMTaForConditionalGeneration.from_pretrained('google/umt5-small'			,  return_dict=__UpperCAmelCase   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= AutoTokenizer.from_pretrained('google/umt5-small'			,  use_fast=__UpperCAmelCase			,  legacy=__UpperCAmelCase   )
      __UpperCamelCase       						= [
          '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       						= tokenizer(__UpperCAmelCase			,  return_tensors='pt'			,  padding=__UpperCAmelCase   ).input_ids
      # fmt: off
      __UpperCamelCase       						= 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(__UpperCAmelCase			,  __UpperCAmelCase   )

      __UpperCamelCase       						= model.generate(input_ids.to(__UpperCAmelCase   )   )
      __UpperCamelCase       						= [
          '<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       						= tokenizer.batch_decode(__UpperCAmelCase   )
      self.assertEqual(__UpperCAmelCase			,  __UpperCAmelCase   )





"""simple docstring"""

from __future__ import annotations

import os
from typing import Any

import requests

UpperCamelCase			:       Union[str, Any]									=				"https://api.github.com"

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCamelCase			:       Union[str, Any]									=				BASE_URL + "/user"

# https://github.com/settings/tokens
UpperCamelCase			:       Optional[int]									=				os.environ.get("USER_TOKEN", "")






def 							A		(       snake_case   :str	)   ->			dict[Any, Any]:
 __UpperCamelCase       						= {
     'Authorization': f'token {auth_token}',
     'Accept': 'application/vnd.github.v3+json',
 }
 return requests.get(snake_case       ,      headers=snake_case	).json()


if __name__ == "__main__":  # pragma: no cover
      if USER_TOKEN:
            for key, value in fetch_github_info(USER_TOKEN).items():
                  print(f'''{key}: {value}''')
      else:
            raise ValueError("'USER_TOKEN' field cannot be empty.")



