





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        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Union[str, Any]      ,   snake_case							:List[Any]      ,   snake_case							:Dict=99      ,   snake_case							:List[str]=13      ,   snake_case							:List[Any]=7      ,   snake_case							:Dict=9      ,   snake_case							:int=True      ,   snake_case							:Optional[int]=True      ,   snake_case							:int=False      ,   snake_case							:Optional[Any]=32      ,   snake_case							:Dict=5      ,   snake_case							:str=4      ,   snake_case							:Tuple=37      ,   snake_case							:str=8      ,   snake_case							:str=0.1      ,   snake_case							:Optional[Any]=0.002      ,   snake_case							:Any=1      ,   snake_case							:List[str]=0      ,   snake_case							:int=0      ,   snake_case							:str=None      ,   snake_case							:Union[str, Any]=None      ,   ):





        '''simple docstring'''




        A_       :					int        =      parent
        A_       :					int        =      batch_size
        A_       :					Union[str, Any]        =      encoder_seq_length
        A_       :					Dict        =      decoder_seq_length
        # For common tests
        A_       :					Tuple        =      self.decoder_seq_length
        A_       :					Tuple        =      is_training
        A_       :					Union[str, Any]        =      use_attention_mask
        A_       :					Union[str, Any]        =      use_labels
        A_       :					Dict        =      vocab_size
        A_       :					Optional[int]        =      hidden_size
        A_       :					Union[str, Any]        =      num_hidden_layers
        A_       :					Dict        =      num_attention_heads
        A_       :					int        =      d_ff
        A_       :					List[Any]        =      relative_attention_num_buckets
        A_       :					Dict        =      dropout_rate
        A_       :					Optional[Any]        =      initializer_factor
        A_       :					Dict        =      eos_token_id
        A_       :					Tuple        =      pad_token_id
        A_       :					Optional[int]        =      decoder_start_token_id
        A_       :					Dict        =      None
        A_       :					Union[str, Any]        =      decoder_layers



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:List[str]      ,   snake_case							:Dict      ,   snake_case							:int      ,   snake_case							:List[str]=None      ,   snake_case							:Dict=None      ,   snake_case							:List[Any]=None      ,   snake_case							:str=None      ,   snake_case							:Any=None      ,   ):





        '''simple docstring'''




        if attention_mask is None:
           A_       :					int        =      input_ids.ne(config.pad_token_id	)
        if decoder_attention_mask is None:
           A_       :					List[str]        =      decoder_input_ids.ne(config.pad_token_id	)
        if head_mask is None:
           A_       :					Optional[Any]        =      torch.ones(config.num_hidden_layers      ,   config.num_attention_heads      ,   device=snake_case	)
        if decoder_head_mask is None:
           A_       :					int        =      torch.ones(config.num_decoder_layers      ,   config.num_attention_heads      ,   device=snake_case	)
        if cross_attn_head_mask is None:
           A_       :					List[Any]        =      torch.ones(
               config.num_decoder_layers      ,   config.num_attention_heads      ,   device=snake_case	)
        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							:Dict	):





        '''simple docstring'''




        A_       :					List[Any]        =      ids_tensor([self.batch_size, self.encoder_seq_length]      ,   self.vocab_size	)
        A_       :					str        =      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
        A_       :					Optional[int]        =      input_ids.clamp(self.pad_token_id + 1	)
        A_       :					str        =      decoder_input_ids.clamp(self.pad_token_id + 1	)

        A_       :					Tuple        =      self.get_config()
        A_       :					Optional[int]        =      config.num_attention_heads
        A_       :					Any        =      self.prepare_inputs_dict(snake_case      ,   snake_case      ,   snake_case	)
        return config, input_dict



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_   ,    A_       :					Tuple        =      self.prepare_config_and_inputs()
        return config, inputs_dict



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''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     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''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     SCREAMING_SNAKE_CASE		(				self							:Any      ,   snake_case							:Union[str, Any]      ,   snake_case							:Optional[int]      ,   snake_case							:str      ,   snake_case							:Optional[int]      ,   snake_case							:Optional[int]      ,   snake_case							:int      ,   ):





        '''simple docstring'''




        A_       :					Any        =      UMTaModel(config=snake_case	)
        model.to(snake_case	)
        model.eval()
        A_       :					str        =      model(
            input_ids=snake_case      ,   decoder_input_ids=snake_case      ,   attention_mask=snake_case      ,   decoder_attention_mask=snake_case      ,   )
        A_       :					Dict        =      model(input_ids=snake_case      ,   decoder_input_ids=snake_case	)
        A_       :					int        =      result.last_hidden_state
        A_       :					Optional[int]        =      result.past_key_values
        A_       :					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(snake_case	)      ,   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							:str      ,   snake_case							:Tuple      ,   snake_case							:Optional[Any]      ,   snake_case							:Any      ,   snake_case							:Optional[Any]      ,   snake_case							:int      ,   snake_case							:List[str]      ,   ):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      UMTaModel(config=snake_case	).get_decoder().to(snake_case	).eval()
        # first forward pass
        A_       :					str        =      model(snake_case      ,   use_cache=snake_case	)
        A_       :					List[Any]        =      model(snake_case	)
        A_       :					int        =      model(snake_case      ,   use_cache=snake_case	)

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

        A_   ,    A_       :					Dict        =      outputs.to_tuple()

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

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

        A_       :					int        =      model(snake_case	)["last_hidden_state"]
        A_       :					Tuple        =      model(snake_case      ,   past_key_values=snake_case	)["last_hidden_state"]

        # select random slice
        A_       :					Dict        =      ids_tensor((1,)      ,   output_from_past.shape[-1]	).item()
        A_       :					List[str]        =      output_from_no_past[:, -1, random_slice_idx].detach()
        A_       :					Optional[Any]        =      output_from_past[:, 0, random_slice_idx].detach()

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





     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]      ,   snake_case							:int      ,   snake_case							:Optional[Any]      ,   ):





        '''simple docstring'''




        A_       :					Optional[int]        =      UMTaModel(config=snake_case	).to(snake_case	).half().eval()
        A_       :					List[Any]        =      model(**snake_case	)["last_hidden_state"]
        self.parent.assertFalse(torch.isnan(snake_case	).any().item()	)




@require_torch
class        __magic_name__	(				lowerCamelCase__     ,					lowerCamelCase__     ,					lowerCamelCase__     ,					unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  (
         (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
     )
     __UpperCamelCase            =  (UMTaForConditionalGeneration,) if is_torch_available() else ()
     __UpperCamelCase            =  (
         {
             '''conversational''': UMTaForConditionalGeneration,
             '''feature-extraction''': UMTaModel,
             '''summarization''': UMTaForConditionalGeneration,
             '''text2text-generation''': UMTaForConditionalGeneration,
             '''translation''': UMTaForConditionalGeneration,
             '''question-answering''': UMTaForQuestionAnswering,
         }
         if is_torch_available()
         else {}
     )
     __UpperCamelCase            =  True
     __UpperCamelCase            =  False
     __UpperCamelCase            =  False
     __UpperCamelCase            =  True
     __UpperCamelCase            =  True
     # The small UMT5 model needs higher percentages for CPU/MP tests
     __UpperCamelCase            =  [0.8, 0.9]



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      UMTaModelTester(self	)



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





        '''simple docstring'''




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



     @unittest.skipIf(torch_device == "cpu"      ,   "Cant do half precision"	)
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fpaa_forward(*snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        A_       :					List[str]        =      ["encoder_attentions", "decoder_attentions", "cross_attentions"]
        A_       :					Optional[int]        =      self.model_tester.prepare_config_and_inputs()
        A_       :					List[str]        =      config_and_inputs[0]
        A_       :					List[Any]        =      UMTaForConditionalGeneration(snake_case	).eval()
        model.to(snake_case	)

        A_       :					List[str]        =      {
            "head_mask": torch.zeros(config.num_layers      ,   config.num_heads      ,   device=snake_case	),
            "decoder_head_mask": torch.zeros(config.num_decoder_layers      ,   config.num_heads      ,   device=snake_case	),
            "cross_attn_head_mask": torch.zeros(config.num_decoder_layers      ,   config.num_heads      ,   device=snake_case	),
        }

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

           A_       :					List[Any]        =      model.generate(
               config_and_inputs[1]["input_ids"]      ,   num_beams=1      ,   max_length=3      ,   output_attentions=snake_case      ,   return_dict_in_generate=snake_case      ,   **snake_case      ,   )
           # We check the state of decoder_attentions and cross_attentions just from the last step
           A_       :					Optional[int]        =      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							:Tuple	):





        '''simple docstring'''




        pass




@require_torch
@require_sentencepiece
@require_tokenizers
class        __magic_name__	(				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							:int	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      UMTaForConditionalGeneration.from_pretrained("google/umt5-small"      ,   return_dict=snake_case	).to(snake_case	)
        A_       :					int        =      AutoTokenizer.from_pretrained("google/umt5-small"      ,   use_fast=snake_case      ,   legacy=snake_case	)
        A_       :					Optional[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>.",
        ]
        A_       :					Optional[int]        =      tokenizer(snake_case      ,   return_tensors="pt"      ,   padding=snake_case	).input_ids
        # fmt: off
        A_       :					List[str]        =      torch.tensor(
            [
                [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
            ]	)
        # fmt: on
        torch.testing.assert_allclose(snake_case      ,   snake_case	)

        A_       :					Union[str, Any]        =      model.generate(input_ids.to(snake_case	)	)
        A_       :					str        =      [
            "<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>",
        ]
        A_       :					Any        =      tokenizer.batch_decode(snake_case	)
        self.assertEqual(snake_case      ,   snake_case	)







import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard


@slow
@require_flax
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        super().tearDown()
        gc.collect()



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_   ,    A_       :					Optional[Any]        =      FlaxStableDiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2"      ,   revision="bf16"      ,   dtype=jnp.bfloataa      ,   )

        A_       :					List[Any]        =      "A painting of a squirrel eating a burger"
        A_       :					int        =      jax.device_count()
        A_       :					Union[str, Any]        =      num_samples * [prompt]
        A_       :					Dict        =      sd_pipe.prepare_inputs(snake_case	)

        A_       :					List[Any]        =      replicate(snake_case	)
        A_       :					str        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Dict        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					int        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        A_       :					List[str]        =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
        A_       :					Union[str, Any]        =      images[0, 253:256, 253:256, -1]

        A_       :					Dict        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					List[str]        =      jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					str        =      "stabilityai/stable-diffusion-2"
        A_   ,    A_       :					Dict        =      FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case      ,   subfolder="scheduler"	)
        A_   ,    A_       :					Any        =      FlaxStableDiffusionPipeline.from_pretrained(
            snake_case      ,   scheduler=snake_case      ,   revision="bf16"      ,   dtype=jnp.bfloataa      ,   )
        A_       :					Any        =      scheduler_params

        A_       :					Optional[Any]        =      "A painting of a squirrel eating a burger"
        A_       :					Dict        =      jax.device_count()
        A_       :					List[str]        =      num_samples * [prompt]
        A_       :					int        =      sd_pipe.prepare_inputs(snake_case	)

        A_       :					Optional[Any]        =      replicate(snake_case	)
        A_       :					Dict        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Union[str, Any]        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					Union[str, Any]        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        A_       :					List[Any]        =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
        A_       :					Union[str, Any]        =      images[0, 253:256, 253:256, -1]

        A_       :					Tuple        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					str        =      jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2
