




'''simple docstring'''


import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
	from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a=None    ,	a=None    ,	a=None    ,	a=None    ,	a=None    ,	):
						if attention_mask is None:
												__snake_case						 =   input_ids.ne(config.pad_token_id							)
						if decoder_attention_mask is None:
												__snake_case						 =   decoder_input_ids.ne(config.pad_token_id							)
						if head_mask is None:
												__snake_case						 =   torch.ones(config.encoder_layers    ,	config.encoder_attention_heads    ,	device=a							)
						if decoder_head_mask is None:
												__snake_case						 =   torch.ones(config.decoder_layers    ,	config.decoder_attention_heads    ,	device=a							)
						if cross_attn_head_mask is None:
												__snake_case						 =   torch.ones(config.decoder_layers    ,	config.decoder_attention_heads    ,	device=a							)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}
class 						a_				:




	def __init__(       self     :  List[Any]       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Dict=1_3       ,		__lowerCAmelCase     :  List[Any]=7       ,		__lowerCAmelCase     :  List[Any]=True       ,		__lowerCAmelCase     :  Tuple=False       ,		__lowerCAmelCase     :  int=9_9       ,		__lowerCAmelCase     :  Any=1_6       ,		__lowerCAmelCase     :  Dict=2       ,		__lowerCAmelCase     :  str=4       ,		__lowerCAmelCase     :  Union[str, Any]=4       ,		__lowerCAmelCase     :  Optional[Any]="relu"       ,		__lowerCAmelCase     :  Dict=0.1       ,		__lowerCAmelCase     :  Any=0.1       ,		__lowerCAmelCase     :  List[str]=0.0       ,		__lowerCAmelCase     :  Any=0.0       ,		__lowerCAmelCase     :  int=2_0       ,		__lowerCAmelCase     :  Tuple=2       ,		__lowerCAmelCase     :  Tuple=1       ,		__lowerCAmelCase     :  Dict=0       ,		):
							__snake_case						 =   parent
							__snake_case						 =   batch_size
							__snake_case						 =   seq_length
							__snake_case						 =   is_training
							__snake_case						 =   use_labels
							__snake_case						 =   vocab_size
							__snake_case						 =   hidden_size
							__snake_case						 =   num_hidden_layers
							__snake_case						 =   num_attention_heads
							__snake_case						 =   intermediate_size
							__snake_case						 =   hidden_act
							__snake_case						 =   hidden_dropout_prob
							__snake_case						 =   attention_probs_dropout_prob
							__snake_case						 =   encoder_layerdrop
							__snake_case						 =   decoder_layerdrop
							__snake_case						 =   max_position_embeddings
							__snake_case						 =   eos_token_id
							__snake_case						 =   pad_token_id
							__snake_case						 =   bos_token_id




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size      )
							__snake_case						 =   self.eos_token_id  # Eos Token
							__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size      )

							# we need to clamp the input ids here to avoid having pad token in between
							# this is because for M2M100 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
							__snake_case						 =   input_ids.clamp(self.pad_token_id + 1      )
							__snake_case						 =   decoder_input_ids.clamp(self.pad_token_id + 1      )

							__snake_case						 =   self.get_config()
							__snake_case						 =   prepare_mam_aaa_inputs_dict(__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase      )
							return config, inputs_dict




	def   lowercase__   (       self     :  Union[str, Any]      ):
							return MaMaaaConfig(
							    vocab_size=self.vocab_size       ,		d_model=self.hidden_size       ,		encoder_layers=self.num_hidden_layers       ,		decoder_layers=self.num_hidden_layers       ,		encoder_attention_heads=self.num_attention_heads       ,		decoder_attention_heads=self.num_attention_heads       ,		encoder_ffn_dim=self.intermediate_size       ,		decoder_ffn_dim=self.intermediate_size       ,		dropout=self.hidden_dropout_prob       ,		attention_dropout=self.attention_probs_dropout_prob       ,		encoder_layerdrop=self.encoder_layerdrop       ,		decoder_layerdrop=self.decoder_layerdrop       ,		max_position_embeddings=self.max_position_embeddings       ,		eos_token_id=self.eos_token_id       ,		bos_token_id=self.bos_token_id       ,		pad_token_id=self.pad_token_id       ,		)




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case ,       __snake_case						 =   self.prepare_config_and_inputs()
							return config, inputs_dict




	def   lowercase__   (       self     :  int       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  str      ):
							__snake_case						 =   MaMaaaModel(config=__lowerCAmelCase      ).get_decoder().to(__lowerCAmelCase      ).eval()
							__snake_case						 =   inputs_dict['input_ids']
							__snake_case						 =   inputs_dict['attention_mask']
							__snake_case						 =   inputs_dict['head_mask']

							# first forward pass
							__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase       ,		head_mask=__lowerCAmelCase       ,		use_cache=__lowerCAmelCase      )

							__snake_case ,       __snake_case						 =   outputs.to_tuple()

							# create hypothetical multiple next token and extent to next_input_ids
							__snake_case						 =   ids_tensor((self.batch_size, 3)       ,		config.vocab_size      )
							__snake_case						 =   ids_tensor((self.batch_size, 3)       ,		2      )

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

							__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase      )['last_hidden_state']
							__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase       ,		past_key_values=__lowerCAmelCase      )[
							    'last_hidden_state'
							]

							# select random slice
							__snake_case						 =   ids_tensor((1,)       ,		output_from_past.shape[-1]      ).item()
							__snake_case						 =   output_from_no_past[:, -3:, random_slice_idx].detach()
							__snake_case						 =   output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(__lowerCAmelCase       ,		__lowerCAmelCase       ,		atol=1E-2      )      )




	def   lowercase__   (       self     :  Any       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[Any]      ):
							__snake_case						 =   MaMaaaModel(config=__lowerCAmelCase      ).to(__lowerCAmelCase      ).eval()
							__snake_case						 =   model(**__lowerCAmelCase      )

							__snake_case						 =   outputs.encoder_last_hidden_state
							__snake_case						 =   outputs.last_hidden_state

							with tempfile.TemporaryDirectory() as tmpdirname:
													__snake_case						 =   model.get_encoder()
													encoder.save_pretrained(__lowerCAmelCase      )
													__snake_case						 =   MaMaaaEncoder.from_pretrained(__lowerCAmelCase      ).to(__lowerCAmelCase      )

							__snake_case						 =   encoder(inputs_dict['input_ids']       ,		attention_mask=inputs_dict['attention_mask']      )[
							    0
							]

							self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3      )

							with tempfile.TemporaryDirectory() as tmpdirname:
													__snake_case						 =   model.get_decoder()
													decoder.save_pretrained(__lowerCAmelCase      )
													__snake_case						 =   MaMaaaDecoder.from_pretrained(__lowerCAmelCase      ).to(__lowerCAmelCase      )

							__snake_case						 =   decoder(
							    input_ids=inputs_dict['decoder_input_ids']       ,		attention_mask=inputs_dict['decoder_attention_mask']       ,		encoder_hidden_states=__lowerCAmelCase       ,		encoder_attention_mask=inputs_dict['attention_mask']       ,		)[0]

							self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3      )





@require_torch
class 						a_				(			UpperCAmelCase__     ,						UpperCAmelCase__     ,						UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     Any			  =      (
	    (
	        MaMaaaModel,
	        MaMaaaForConditionalGeneration,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase_       :     Optional[Any]			  =      (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
	lowercase_       :     List[str]			  =      (
	    {
	        '''conversational''': MaMaaaForConditionalGeneration,
	        '''feature-extraction''': MaMaaaModel,
	        '''summarization''': MaMaaaForConditionalGeneration,
	        '''text2text-generation''': MaMaaaForConditionalGeneration,
	        '''translation''': MaMaaaForConditionalGeneration,
	    }
	    if is_torch_available()
	    else {}
	)
	lowercase_       :     Tuple			  =      True
	lowercase_       :     int			  =      True
	lowercase_       :     str			  =      False
	lowercase_       :     Dict			  =      False




	def   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  str      ):
							if pipeline_test_casse_name == "TranslationPipelineTests":
													# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
													# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
													return True

							return False




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   MaMaaaModelTester(self      )
							__snake_case						 =   ConfigTester(self       ,		config_class=__lowerCAmelCase      )




	def   lowercase__   (       self     :  Any      ):
							self.config_tester.run_common_tests()




	def   lowercase__   (       self     :  str      ):
							__snake_case ,       __snake_case						 =   self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
													__snake_case						 =   model_class(__lowerCAmelCase      )

													with tempfile.TemporaryDirectory() as tmpdirname:
																			model.save_pretrained(__lowerCAmelCase      )
																			__snake_case ,       __snake_case						 =   model_class.from_pretrained(__lowerCAmelCase       ,		output_loading_info=__lowerCAmelCase      )
													self.assertEqual(info['missing_keys']       ,		[]      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_encoder_decoder_model_standalone(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case ,       __snake_case						 =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
													__snake_case						 =   model_class(__lowerCAmelCase      )
													model.to(__lowerCAmelCase      )
													model.eval()

													__snake_case						 =   copy.deepcopy(self._prepare_for_class(__lowerCAmelCase       ,		__lowerCAmelCase      )      )

													if not self.is_encoder_decoder:
																			__snake_case						 =   inputs['input_ids']
																			del inputs["input_ids"]
													else:
																			__snake_case						 =   inputs['input_ids']
																			__snake_case						 =   inputs.get('decoder_input_ids'       ,		__lowerCAmelCase      )
																			del inputs["input_ids"]
																			inputs.pop('decoder_input_ids'       ,		__lowerCAmelCase      )

													__snake_case						 =   model.get_input_embeddings()
													if not self.is_encoder_decoder:
																			__snake_case						 =   wte(__lowerCAmelCase      )
													else:
																			__snake_case						 =   wte(__lowerCAmelCase      )
																			__snake_case						 =   wte(__lowerCAmelCase      )

													with torch.no_grad():
																			model(**__lowerCAmelCase      )[0]




	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case ,       __snake_case						 =   self.model_tester.prepare_config_and_inputs()
							__snake_case						 =   input_dict['input_ids']
							__snake_case						 =   input_ids.ne(1      ).to(__lowerCAmelCase      )
							__snake_case						 =   MaMaaaForConditionalGeneration(__lowerCAmelCase      ).eval().to(__lowerCAmelCase      )
							if torch_device == "cuda":
													model.half()
							model.generate(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase      )
							model.generate(num_beams=4       ,		do_sample=__lowerCAmelCase       ,		early_stopping=__lowerCAmelCase       ,		num_return_sequences=3      )





def 						lowerCamelCase__   (				a							):
						return torch.tensor(a    ,	dtype=torch.long    ,	device=a							)


_lowercase  =			1e-4






@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 						a_				(			unittest.TestCase						):




	@cached_property
	def   lowercase__   (       self     :  Union[str, Any]      ):
							return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'      )




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   MaMaaaModel.from_pretrained('facebook/m2m100_418M'      ).to(__lowerCAmelCase      )
							__snake_case						 =   _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]      )
							__snake_case						 =   _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]      )
							__snake_case						 =   prepare_mam_aaa_inputs_dict(model.config       ,		__lowerCAmelCase       ,		__lowerCAmelCase      )
							with torch.no_grad():
													__snake_case						 =   model(**__lowerCAmelCase      )[0]
							__snake_case						 =   torch.Size((1, 1_1, 1_0_2_4)      )
							self.assertEqual(output.shape       ,		__lowerCAmelCase      )
							# change to expected output here
							__snake_case						 =   torch.tensor(
							    [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]       ,		device=__lowerCAmelCase      )
							self.assertTrue(torch.allclose(output[:, :3, :3]       ,		__lowerCAmelCase       ,		atol=__lowerCAmelCase      )      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'      ).to(__lowerCAmelCase      )

							# change to intended input
							__snake_case						 =   _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]      )
							__snake_case						 =   _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]      )
							__snake_case						 =   prepare_mam_aaa_inputs_dict(model.config       ,		__lowerCAmelCase       ,		__lowerCAmelCase      )
							with torch.no_grad():
													__snake_case						 =   model(**__lowerCAmelCase      )[0]
							__snake_case						 =   torch.Size((1, 1_1, model.config.vocab_size)      )
							self.assertEqual(output.shape       ,		__lowerCAmelCase      )
							# change to expected output here
							__snake_case						 =   torch.tensor(
							    [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]       ,		device=__lowerCAmelCase      )
							self.assertTrue(torch.allclose(output[:, :3, :3]       ,		__lowerCAmelCase       ,		atol=__lowerCAmelCase      )      )




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'      ).to(__lowerCAmelCase      )
							__snake_case						 =   MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'       ,		src_lang='fr'       ,		tgt_lang='en'      )

							__snake_case						 =   [
							    'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
							    'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
							    'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
							    ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
							    ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
							]

							# The below article tests that we don't add any hypotheses outside of the top n_beams
							__snake_case						 =   tokenizer(__lowerCAmelCase       ,		padding=__lowerCAmelCase       ,		return_tensors='pt'      )

							__snake_case						 =   model.generate(
							    input_ids=dct['input_ids'].to(__lowerCAmelCase      )       ,		attention_mask=dct['attention_mask'].to(__lowerCAmelCase      )       ,		num_beams=5       ,		forced_bos_token_id=tokenizer.get_lang_id('en'      )       ,		)

							__snake_case						 =   [
							    'The NSA case highlights the total absence of intelligence debate',
							    'I think there are two levels of response from the French government.',
							    'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
							    ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
							    ' communications in France.',
							]

							__snake_case						 =   tokenizer.batch_decode(
							    hypotheses_batch.tolist()       ,		clean_up_tokenization_spaces=__lowerCAmelCase       ,		skip_special_tokens=__lowerCAmelCase      )
							assert generated == expected_en







'''simple docstring'''


from typing import TYPE_CHECKING

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


_lowercase  =			{
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GroupViTModel""",
	    """GroupViTPreTrainedModel""",
	    """GroupViTTextModel""",
	    """GroupViTVisionModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFGroupViTModel""",
	    """TFGroupViTPreTrainedModel""",
	    """TFGroupViTTextModel""",
	    """TFGroupViTVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_groupvit import (
	    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    GroupViTConfig,
	    GroupViTOnnxConfig,
	    GroupViTTextConfig,
	    GroupViTVisionConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_groupvit import (
		    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GroupViTModel,
		    GroupViTPreTrainedModel,
		    GroupViTTextModel,
		    GroupViTVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_groupvit import (
		    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFGroupViTModel,
		    TFGroupViTPreTrainedModel,
		    TFGroupViTTextModel,
		    TFGroupViTVisionModel,
		)

else:
	import sys

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

