

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 				_snake_case			:




				def __init__(						self					:						Any,					__lowercase					:						List[Any],					__lowercase					:						List[str]=99,					__lowercase					:						Optional[int]=13,					__lowercase					:						str=7,					__lowercase					:						str=9,					__lowercase					:						str=True,					__lowercase					:						Optional[int]=True,					__lowercase					:						str=False,					__lowercase					:						Dict=32,					__lowercase					:						Dict=5,					__lowercase					:						int=4,					__lowercase					:						Optional[int]=37,					__lowercase					:						Tuple=8,					__lowercase					:						int=0.1,					__lowercase					:						List[str]=0.002,					__lowercase					:						List[str]=1,					__lowercase					:						Tuple=0,					__lowercase					:						Optional[int]=0,					__lowercase					:						Optional[int]=None,					__lowercase					:						List[str]=None,					):
							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					:						Dict						):
							return TaConfig.from_pretrained("google/umt5-base"						)




				def 							A__      (						self					:						str,					__lowercase					:						Dict,					__lowercase					:						Any,					__lowercase					:						List[str],					__lowercase					:						Optional[int]=None,					__lowercase					:						Union[str, Any]=None,					__lowercase					:						Tuple=None,					__lowercase					:						int=None,					__lowercase					:						Optional[int]=None,					):





							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=UpperCamelCase_						)
							if decoder_head_mask is None:
										lowercase__					  =				torch.ones(config.num_decoder_layers,					config.num_attention_heads,					device=UpperCamelCase_						)
							if cross_attn_head_mask is None:
										lowercase__					  =				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 							A__      (						self					:						Union[str, Any]						):
							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(UpperCamelCase_,					UpperCamelCase_,					UpperCamelCase_						)
							return config, input_dict




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				self.prepare_config_and_inputs()
							return config, inputs_dict




				def 							A__      (						self					:						List[str]						):
							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					:						List[str]						):
							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					:						Any,					__lowercase					:						Optional[int],					__lowercase					:						str,					__lowercase					:						str,					__lowercase					:						Optional[Any],					__lowercase					:						Optional[Any],					__lowercase					:						Tuple,					):
							lowercase__					  =				UMTaModel(config=UpperCamelCase_						)
							model.to(UpperCamelCase_						)
							model.eval()
							lowercase__					  =				model(
							    input_ids=UpperCamelCase_,					decoder_input_ids=UpperCamelCase_,					attention_mask=UpperCamelCase_,					decoder_attention_mask=UpperCamelCase_,					)
							lowercase__					  =				model(input_ids=UpperCamelCase_,					decoder_input_ids=UpperCamelCase_						)
							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(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 							A__      (						self					:						str,					__lowercase					:						Any,					__lowercase					:						Any,					__lowercase					:						List[str],					__lowercase					:						List[str],					__lowercase					:						str,					__lowercase					:						Any,					):
							lowercase__					  =				UMTaModel(config=UpperCamelCase_						).get_decoder().to(UpperCamelCase_						).eval()
							# first forward pass
							lowercase__					  =				model(UpperCamelCase_,					use_cache=UpperCamelCase_						)
							lowercase__					  =				model(UpperCamelCase_						)
							lowercase__					  =				model(UpperCamelCase_,					use_cache=UpperCamelCase_						)

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

							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(UpperCamelCase_						)['last_hidden_state']
							lowercase__					  =				model(UpperCamelCase_,					past_key_values=UpperCamelCase_						)['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(UpperCamelCase_,					UpperCamelCase_,					atol=1e-3						)						)




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						Any,					__lowercase					:						Optional[int],					):
							lowercase__					  =				UMTaModel(config=UpperCamelCase_						).to(UpperCamelCase_						).half().eval()
							lowercase__					  =				model(**UpperCamelCase_						)['last_hidden_state']
							self.parent.assertFalse(torch.isnan(UpperCamelCase_						).any().item()						)
@require_torch
class 				_snake_case			(					lowercase__    ,	lowercase__    ,	lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Dict     		=(
				    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
				)
				UpperCamelCase__				:					List[Any]     		=(UMTaForConditionalGeneration,) if is_torch_available() else ()
				UpperCamelCase__				:					Union[str, Any]     		=(
				    {
				        """conversational""": UMTaForConditionalGeneration,
				        """feature-extraction""": UMTaModel,
				        """summarization""": UMTaForConditionalGeneration,
				        """text2text-generation""": UMTaForConditionalGeneration,
				        """translation""": UMTaForConditionalGeneration,
				        """question-answering""": UMTaForQuestionAnswering,
				    }
				    if is_torch_available()
				    else {}
				)
				UpperCamelCase__				:					Optional[Any]     		=True
				UpperCamelCase__				:					List[str]     		=False
				UpperCamelCase__				:					Any     		=False
				UpperCamelCase__				:					str     		=True
				UpperCamelCase__				:					List[str]     		=True
				# The small UMT5 model needs higher percentages for CPU/MP tests
				UpperCamelCase__				:					List[str]     		=[0.8, 0.9]




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				UMTaModelTester(self						)




				@unittest.skip("Test has a segmentation fault on torch 1.8.0"						)
				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							lowercase__					  =				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 							A__      (						self					:						str						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model_fpaa_forward(*UpperCamelCase_						)




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				['encoder_attentions', 'decoder_attentions', 'cross_attentions']
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							lowercase__					  =				config_and_inputs[0]
							lowercase__					  =				UMTaForConditionalGeneration(UpperCamelCase_						).eval()
							model.to(UpperCamelCase_						)

							lowercase__					  =				{
							    '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()						):
										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=UpperCamelCase_						)

										lowercase__					  =				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
										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					:						str						):
							pass
@require_torch
@require_sentencepiece
@require_tokenizers
class 				_snake_case			(					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					:						List[str]						):
							lowercase__					  =				UMTaForConditionalGeneration.from_pretrained("google/umt5-small",					return_dict=UpperCamelCase_						).to(UpperCamelCase_						)
							lowercase__					  =				AutoTokenizer.from_pretrained("google/umt5-small",					use_fast=UpperCamelCase_,					legacy=UpperCamelCase_						)
							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(UpperCamelCase_,					return_tensors="pt",					padding=UpperCamelCase_						).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(UpperCamelCase_,					UpperCamelCase_						)

							lowercase__					  =				model.generate(input_ids.to(UpperCamelCase_						)						)
							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(UpperCamelCase_						)
							self.assertEqual(UpperCamelCase_,					UpperCamelCase_						)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_								=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TimesformerModel""",
							    """TimesformerForVideoClassification""",
							    """TimesformerPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_timesformer import (
														    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TimesformerForVideoClassification,
														    TimesformerModel,
														    TimesformerPreTrainedModel,
														)

else:
							import sys

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




