




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	,    _A	,    _A=9_9	,    _A=1_3	,    _A=7	,    _A=9	,    _A=True	,    _A=True	,    _A=False	,    _A=3_2	,    _A=5	,    _A=4	,    _A=3_7	,    _A=8	,    _A=0.1	,    _A=0.0_02	,    _A=1	,    _A=0	,    _A=0	,    _A=None	,    _A=None	,    ):




											"""simple docstring"""



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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



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






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A=None	,    _A=None	,    _A=None	,    _A=None	,    _A=None	,    ):




											"""simple docstring"""



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




											"""simple docstring"""



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

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return TaConfig(
											    vocab_size=1_6_6	,    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    ):




											"""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	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    ):




											"""simple docstring"""



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




											"""simple docstring"""



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

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

											__lowerCAmelCase     ,							__lowerCAmelCase            = outputs.to_tuple()

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

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

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

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




											"""simple docstring"""



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






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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = UMTaModelTester(self    )






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




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											__lowerCAmelCase            = 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    ):




											"""simple docstring"""



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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



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

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

																__lowerCAmelCase            = 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
																__lowerCAmelCase            = 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    ):




											"""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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = UMTaForConditionalGeneration.from_pretrained("google/umt5-small"	,    return_dict=_A    ).to(_A    )
											__lowerCAmelCase            = AutoTokenizer.from_pretrained("google/umt5-small"	,    use_fast=_A	,    legacy=_A    )
											__lowerCAmelCase            = [
											    "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>.",
											]
											__lowerCAmelCase            = tokenizer(_A	,    return_tensors="pt"	,    padding=_A    ).input_ids
											# fmt: off
											__lowerCAmelCase            = torch.tensor(
											    [
											        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
											        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
											    ]    )
											# fmt: on
											torch.testing.assert_allclose(_A	,    _A    )

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase__					    =    {
    """configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
    """tokenization_ctrl""": ["""CTRLTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__					    =    [
					    """CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CTRLForSequenceClassification""",
					    """CTRLLMHeadModel""",
					    """CTRLModel""",
					    """CTRLPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__					    =    [
					    """TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFCTRLForSequenceClassification""",
					    """TFCTRLLMHeadModel""",
					    """TFCTRLModel""",
					    """TFCTRLPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
					from .tokenization_ctrl import CTRLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ctrl import (
										    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CTRLForSequenceClassification,
										    CTRLLMHeadModel,
										    CTRLModel,
										    CTRLPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ctrl import (
										    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCTRLForSequenceClassification,
										    TFCTRLLMHeadModel,
										    TFCTRLModel,
										    TFCTRLPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase__					    =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




