
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 				lowercase					:



		def __init__( self				,      A_				,      A_=99				,      A_=13				,      A_=7				,      A_=9				,      A_=True				,      A_=True				,      A_=False				,      A_=32				,      A_=5				,      A_=4				,      A_=37				,      A_=8				,      A_=0.1				,      A_=0.002				,      A_=1				,      A_=0				,      A_=0				,      A_=None				,      A_=None				,      )  ->					Any:
				"""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       )  ->					Union[str, Any]:
				"""simple docstring"""
				return TaConfig.from_pretrained('google/umt5-base'       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_=None				,      A_=None				,      A_=None				,      A_=None				,      A_=None				,      )  ->					Optional[int]:
				"""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=A_       )
				if decoder_head_mask is None:
						UpperCamelCase		=     torch.ones(config.num_decoder_layers				,      config.num_attention_heads				,      device=A_       )
				if cross_attn_head_mask is None:
						UpperCamelCase		=     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 __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""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(A_				,      A_				,      A_       )
				return config, input_dict



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase	,  UpperCamelCase		=     self.prepare_config_and_inputs()
				return config, inputs_dict



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""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       )  ->					int:
				"""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				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_				,      )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     UMTaModel(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(
				    input_ids=A_				,      decoder_input_ids=A_				,      attention_mask=A_				,      decoder_attention_mask=A_				,      )
				UpperCamelCase		=     model(input_ids=A_				,      decoder_input_ids=A_       )
				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(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 __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_				,      A_				,      A_				,      )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     UMTaModel(config=A_       ).get_decoder().to(A_       ).eval()
				# first forward pass
				UpperCamelCase		=     model(A_				,      use_cache=A_       )
				UpperCamelCase		=     model(A_       )
				UpperCamelCase		=     model(A_				,      use_cache=A_       )

				self.parent.assertTrue(len(A_       ) == len(A_       )       )
				self.parent.assertTrue(len(A_       ) == len(A_       ) + 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(A_       )['last_hidden_state']
				UpperCamelCase		=     model(A_				,      past_key_values=A_       )['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(A_				,      A_				,      atol=1e-3       )       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     UMTaModel(config=A_       ).to(A_       ).half().eval()
				UpperCamelCase		=     model(**A_       )['last_hidden_state']
				self.parent.assertFalse(torch.isnan(A_       ).any().item()       )






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



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     UMTaModelTester(self       )



		@unittest.skip('Test has a segmentation fault on torch 1.8.0'       )
		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				UpperCamelCase		=     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 __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model_fpaa_forward(*A_       )



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				UpperCamelCase		=     config_and_inputs[0]
				UpperCamelCase		=     UMTaForConditionalGeneration(A_       ).eval()
				model.to(A_       )

				UpperCamelCase		=     {
				    '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()       ):
						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=A_       )

						UpperCamelCase		=     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
						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       )  ->					List[str]:
				"""simple docstring"""
				pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 				lowercase					(		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       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     UMTaForConditionalGeneration.from_pretrained('google/umt5-small'				,      return_dict=A_       ).to(A_       )
				UpperCamelCase		=     AutoTokenizer.from_pretrained('google/umt5-small'				,      use_fast=A_				,      legacy=A_       )
				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(A_				,      return_tensors='pt'				,      padding=A_       ).input_ids
				# fmt: off
				UpperCamelCase		=     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(A_				,      A_       )

				UpperCamelCase		=     model.generate(input_ids.to(A_       )       )
				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(A_       )
				self.assertEqual(A_				,      A_       )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase					: Union[str, Any]      =       {
    "configuration_instructblip": [
        "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InstructBlipConfig",
        "InstructBlipQFormerConfig",
        "InstructBlipVisionConfig",
    ],
    "processing_instructblip": ["InstructBlipProcessor"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase					: List[Any]      =       [
					    "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "InstructBlipQFormerModel",
					    "InstructBlipPreTrainedModel",
					    "InstructBlipForConditionalGeneration",
					    "InstructBlipVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_instructblip import (
					    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    InstructBlipConfig,
					    InstructBlipQFormerConfig,
					    InstructBlipVisionConfig,
					)
					from .processing_instructblip import InstructBlipProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_instructblip import (
										    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    InstructBlipForConditionalGeneration,
										    InstructBlipPreTrainedModel,
										    InstructBlipQFormerModel,
										    InstructBlipVisionModel,
										)

else:
					import sys

					_UpperCAmelCase					: Union[str, Any]      =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
