"""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 					_A		:

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







            """simple docstring"""



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

      def 							A__			(		self		):







            """simple docstring"""



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

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			):







            """simple docstring"""



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







            """simple docstring"""



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

            lowercase								=      self.get_config()
            lowercase								=      config.num_attention_heads
            lowercase								=      self.prepare_inputs_dict(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
            return config, input_dict

      def 							A__			(		self		):







            """simple docstring"""



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

      def 							A__			(		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 							A__			(		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 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      UMTaModel(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(
                input_ids=__lowerCAmelCase						,			decoder_input_ids=__lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			decoder_attention_mask=__lowerCAmelCase						,			)
            lowercase								=      model(input_ids=__lowerCAmelCase						,			decoder_input_ids=__lowerCAmelCase		)
            lowercase								=      result.last_hidden_state
            lowercase								=      result.past_key_values
            lowercase								=      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(__lowerCAmelCase		)						,			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 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			):







            """simple docstring"""



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

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

            lowercase				,				lowercase								=      outputs.to_tuple()

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

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

            lowercase								=      model(__lowerCAmelCase		)["""last_hidden_state"""]
            lowercase								=      model(__lowerCAmelCase						,			past_key_values=__lowerCAmelCase		)["""last_hidden_state"""]

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

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







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      UMTaModel(config=__lowerCAmelCase		).to(__lowerCAmelCase		).half().eval()
            lowercase								=      model(**__lowerCAmelCase		)["""last_hidden_state"""]
            self.parent.assertFalse(torch.isnan(__lowerCAmelCase		).any().item()		)






@require_torch
class 					_A		(			lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  Optional[int]				    =      (
          (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
      )
      snake_case__     :  Optional[int]				    =      (UMTaForConditionalGeneration,) if is_torch_available() else ()
      snake_case__     :  Union[str, Any]				    =      (
          {
              'conversational': UMTaForConditionalGeneration,
              'feature-extraction': UMTaModel,
              'summarization': UMTaForConditionalGeneration,
              'text2text-generation': UMTaForConditionalGeneration,
              'translation': UMTaForConditionalGeneration,
              'question-answering': UMTaForQuestionAnswering,
          }
          if is_torch_available()
          else {}
      )
      snake_case__     :  Any				    =      True
      snake_case__     :  Any				    =      False
      snake_case__     :  List[str]				    =      False
      snake_case__     :  str				    =      True
      snake_case__     :  Union[str, Any]				    =      True
      # The small UMT5 model needs higher percentages for CPU/MP tests
      snake_case__     :  int				    =      [0.8, 0.9]

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      UMTaModelTester(self		)

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







            """simple docstring"""



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

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







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model_fpaa_forward(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
            lowercase								=      self.model_tester.prepare_config_and_inputs()
            lowercase								=      config_and_inputs[0]
            lowercase								=      UMTaForConditionalGeneration(__lowerCAmelCase		).eval()
            model.to(__lowerCAmelCase		)

            lowercase								=      {
                """head_mask""": torch.zeros(config.num_layers						,			config.num_heads						,			device=__lowerCAmelCase		),
                """decoder_head_mask""": torch.zeros(config.num_decoder_layers						,			config.num_heads						,			device=__lowerCAmelCase		),
                """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers						,			config.num_heads						,			device=__lowerCAmelCase		),
            }

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

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







            """simple docstring"""



            pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 					_A		(			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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""						,			return_dict=__lowerCAmelCase		).to(__lowerCAmelCase		)
            lowercase								=      AutoTokenizer.from_pretrained("""google/umt5-small"""						,			use_fast=__lowerCAmelCase						,			legacy=__lowerCAmelCase		)
            lowercase								=      [
                """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>.""",
            ]
            lowercase								=      tokenizer(__lowerCAmelCase						,			return_tensors="""pt"""						,			padding=__lowerCAmelCase		).input_ids
            # fmt: off
            lowercase								=      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(__lowerCAmelCase						,			__lowerCAmelCase		)

            lowercase								=      model.generate(input_ids.to(__lowerCAmelCase		)		)
            lowercase								=      [
                """<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>""",
            ]
            lowercase								=      tokenizer.batch_decode(__lowerCAmelCase		)
            self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)




"""simple docstring"""






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase       :			str   	="""path-to-your-trained-model"""
__lowerCAmelCase       :			int   	=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

__lowerCAmelCase       :			Optional[int]   	="""A photo of sks dog in a bucket"""
__lowerCAmelCase       :			int   	=pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")



