

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:
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				input_ids.ne(config.pad_token_id)
	if decoder_attention_mask is None:
		SCREAMING_SNAKE_CASE      :					str    =				decoder_input_ids.ne(config.pad_token_id)
	if head_mask is None:
		SCREAMING_SNAKE_CASE      :					Dict    =				torch.ones(config.encoder_layers							,	config.encoder_attention_heads							,	device=UpperCAmelCase_)
	if decoder_head_mask is None:
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.ones(config.decoder_layers							,	config.decoder_attention_heads							,	device=UpperCAmelCase_)
	if cross_attn_head_mask is None:
		SCREAMING_SNAKE_CASE      :					int    =				torch.ones(config.decoder_layers							,	config.decoder_attention_heads							,	device=UpperCAmelCase_)
	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 _UpperCamelCase							:
			'''simple docstring'''







			def __init__(	self							:       List[str]					,					a							:       Optional[Any]					,					a							:       List[str]=13					,					a							:       Optional[int]=7					,					a							:       str=True					,					a							:       Union[str, Any]=False					,					a							:       int=99					,					a							:       str=16					,					a							:       Union[str, Any]=2					,					a							:       Tuple=4					,					a							:       Optional[Any]=4					,					a							:       Any="relu"					,					a							:       List[Any]=0.1					,					a							:       Optional[int]=0.1					,					a							:       int=0.0					,					a							:       Dict=0.0					,					a							:       Optional[Any]=20					,					a							:       Union[str, Any]=2					,					a							:       str=1					,					a							:       int=0					,					)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				parent
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				batch_size
				SCREAMING_SNAKE_CASE      :					Dict    =				seq_length
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				is_training
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				use_labels
				SCREAMING_SNAKE_CASE      :					Tuple    =				vocab_size
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				hidden_size
				SCREAMING_SNAKE_CASE      :					Any    =				num_hidden_layers
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				num_attention_heads
				SCREAMING_SNAKE_CASE      :					List[Any]    =				intermediate_size
				SCREAMING_SNAKE_CASE      :					str    =				hidden_act
				SCREAMING_SNAKE_CASE      :					Tuple    =				hidden_dropout_prob
				SCREAMING_SNAKE_CASE      :					str    =				attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE      :					List[Any]    =				encoder_layerdrop
				SCREAMING_SNAKE_CASE      :					List[str]    =				decoder_layerdrop
				SCREAMING_SNAKE_CASE      :					List[str]    =				max_position_embeddings
				SCREAMING_SNAKE_CASE      :					Any    =				eos_token_id
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				pad_token_id
				SCREAMING_SNAKE_CASE      :					List[Any]    =				bos_token_id
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size		)
				SCREAMING_SNAKE_CASE      :					Dict    =				self.eos_token_id  # Eos Token
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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
				SCREAMING_SNAKE_CASE      :					List[Any]    =				input_ids.clamp(self.pad_token_id + 1		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				decoder_input_ids.clamp(self.pad_token_id + 1		)

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.get_config()
				SCREAMING_SNAKE_CASE      :					List[str]    =				prepare_mam_aaa_inputs_dict(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_		)
				return config, inputs_dict
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				str:

				"""simple docstring"""

				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 							__UpperCamelCase       (	self							:       Dict		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				self.prepare_config_and_inputs()
				return config, inputs_dict
			def 							__UpperCamelCase       (	self							:       Any					,					a							:       int					,					a							:       Tuple		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				MaMaaaModel(config=SCREAMING_SNAKE_CASE_		).get_decoder().to(SCREAMING_SNAKE_CASE_		).eval()
				SCREAMING_SNAKE_CASE      :					List[str]    =				inputs_dict['input_ids']
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				inputs_dict['attention_mask']
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				inputs_dict['head_mask']

				# first forward pass
				SCREAMING_SNAKE_CASE      :					List[str]    =				model(SCREAMING_SNAKE_CASE_					,					attention_mask=SCREAMING_SNAKE_CASE_					,					head_mask=SCREAMING_SNAKE_CASE_					,					use_cache=SCREAMING_SNAKE_CASE_		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				outputs.to_tuple()

				# create hypothetical multiple next token and extent to next_input_ids
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				ids_tensor((self.batch_size, 3)					,					config.vocab_size		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				ids_tensor((self.batch_size, 3)					,					2		)

				# append to next input_ids and
				SCREAMING_SNAKE_CASE      :					str    =				torch.cat([input_ids, next_tokens]					,					dim=-1		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.cat([attention_mask, next_attn_mask]					,					dim=-1		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				model(SCREAMING_SNAKE_CASE_					,					attention_mask=SCREAMING_SNAKE_CASE_		)['last_hidden_state']
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				model(SCREAMING_SNAKE_CASE_					,					attention_mask=SCREAMING_SNAKE_CASE_					,					past_key_values=SCREAMING_SNAKE_CASE_		)[
				    'last_hidden_state'
				]

				# select random slice
				SCREAMING_SNAKE_CASE      :					List[Any]    =				ids_tensor((1,)					,					output_from_past.shape[-1]		).item()
				SCREAMING_SNAKE_CASE      :					int    =				output_from_no_past[:, -3:, random_slice_idx].detach()
				SCREAMING_SNAKE_CASE      :					str    =				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(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_					,					atol=1e-2		)		)






			def 							__UpperCamelCase       (	self							:       Tuple					,					a							:       Optional[int]					,					a							:       Optional[int]		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				MaMaaaModel(config=SCREAMING_SNAKE_CASE_		).to(SCREAMING_SNAKE_CASE_		).eval()
				SCREAMING_SNAKE_CASE      :					List[Any]    =				model(**SCREAMING_SNAKE_CASE_		)

				SCREAMING_SNAKE_CASE      :					List[Any]    =				outputs.encoder_last_hidden_state
				SCREAMING_SNAKE_CASE      :					Tuple    =				outputs.last_hidden_state

				with tempfile.TemporaryDirectory() as tmpdirname:
					SCREAMING_SNAKE_CASE      :					List[str]    =				model.get_encoder()
					encoder.save_pretrained(SCREAMING_SNAKE_CASE_		)
					SCREAMING_SNAKE_CASE      :					int    =				MaMaaaEncoder.from_pretrained(SCREAMING_SNAKE_CASE_		).to(SCREAMING_SNAKE_CASE_		)

				SCREAMING_SNAKE_CASE      :					List[Any]    =				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:
					SCREAMING_SNAKE_CASE      :					Tuple    =				model.get_decoder()
					decoder.save_pretrained(SCREAMING_SNAKE_CASE_		)
					SCREAMING_SNAKE_CASE      :					Any    =				MaMaaaDecoder.from_pretrained(SCREAMING_SNAKE_CASE_		).to(SCREAMING_SNAKE_CASE_		)

				SCREAMING_SNAKE_CASE      :					str    =				decoder(
				    input_ids=inputs_dict["decoder_input_ids"]					,					attention_mask=inputs_dict["decoder_attention_mask"]					,					encoder_hidden_states=SCREAMING_SNAKE_CASE_					,					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 _UpperCamelCase							(							_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=(
			    (
			        MaMaaaModel,
			        MaMaaaForConditionalGeneration,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__							=(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
			lowerCamelCase__							=(
			    {
			        'conversational': MaMaaaForConditionalGeneration,
			        'feature-extraction': MaMaaaModel,
			        'summarization': MaMaaaForConditionalGeneration,
			        'text2text-generation': MaMaaaForConditionalGeneration,
			        'translation': MaMaaaForConditionalGeneration,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__							=True
			lowerCamelCase__							=True
			lowerCamelCase__							=False
			lowerCamelCase__							=False
			def 							__UpperCamelCase       (	self							:       Union[str, Any]					,					a							:       Union[str, Any]					,					a							:       Dict					,					a							:       Tuple					,					a							:       Dict					,					a							:       Optional[Any]		)							->				str:

				"""simple docstring"""

				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 							__UpperCamelCase       (	self							:       str		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				MaMaaaModelTester(self		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				ConfigTester(self					,					config_class=SCREAMING_SNAKE_CASE_		)
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Tuple:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 							__UpperCamelCase       (	self							:       Any		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE      :					List[Any]    =				model_class(SCREAMING_SNAKE_CASE_		)

					with tempfile.TemporaryDirectory() as tmpdirname:
						model.save_pretrained(SCREAMING_SNAKE_CASE_		)
						SCREAMING_SNAKE_CASE      :					Dict    =				model_class.from_pretrained(SCREAMING_SNAKE_CASE_					,					output_loading_info=SCREAMING_SNAKE_CASE_		)
					self.assertEqual(info["missing_keys"]					,					[]		)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_		)
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE_		)
			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				model_class(SCREAMING_SNAKE_CASE_		)
					model.to(SCREAMING_SNAKE_CASE_		)
					model.eval()

					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_		)		)

					if not self.is_encoder_decoder:
						SCREAMING_SNAKE_CASE      :					Any    =				inputs['input_ids']
						del inputs["input_ids"]
					else:
						SCREAMING_SNAKE_CASE      :					Dict    =				inputs['input_ids']
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				inputs.get("decoder_input_ids"					,					SCREAMING_SNAKE_CASE_		)
						del inputs["input_ids"]
						inputs.pop("decoder_input_ids"					,					SCREAMING_SNAKE_CASE_		)

					SCREAMING_SNAKE_CASE      :					Any    =				model.get_input_embeddings()
					if not self.is_encoder_decoder:
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				wte(SCREAMING_SNAKE_CASE_		)
					else:
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				wte(SCREAMING_SNAKE_CASE_		)
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				wte(SCREAMING_SNAKE_CASE_		)

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






			def 							__UpperCamelCase       (	self							:       int		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				self.model_tester.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE      :					Any    =				input_dict['input_ids']
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				input_ids.ne(1		).to(SCREAMING_SNAKE_CASE_		)
				SCREAMING_SNAKE_CASE      :					int    =				MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_		).eval().to(SCREAMING_SNAKE_CASE_		)
				if torch_device == "cuda":
					model.half()
				model.generate(SCREAMING_SNAKE_CASE_					,					attention_mask=SCREAMING_SNAKE_CASE_		)
				model.generate(num_beams=4					,					do_sample=SCREAMING_SNAKE_CASE_					,					early_stopping=SCREAMING_SNAKE_CASE_					,					num_return_sequences=3		)

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


a_     =			1E-4






@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				List[Any]:

				"""simple docstring"""

				return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"		)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				MaMaaaModel.from_pretrained("facebook/m2m100_418M"		).to(SCREAMING_SNAKE_CASE_		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				_long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				_long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				prepare_mam_aaa_inputs_dict(model.config					,					SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_		)
				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					Any    =				model(**SCREAMING_SNAKE_CASE_		)[0]
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.Size((1, 11, 1024)		)
				self.assertEqual(output.shape					,					SCREAMING_SNAKE_CASE_		)
				# change to expected output here
				SCREAMING_SNAKE_CASE      :					int    =				torch.tensor(
				    [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]					,					device=SCREAMING_SNAKE_CASE_		)
				self.assertTrue(torch.allclose(output[:, :3, :3]					,					SCREAMING_SNAKE_CASE_					,					atol=SCREAMING_SNAKE_CASE_		)		)
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"		).to(SCREAMING_SNAKE_CASE_		)

				# change to intended input
				SCREAMING_SNAKE_CASE      :					Tuple    =				_long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				_long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				prepare_mam_aaa_inputs_dict(model.config					,					SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_		)
				with torch.no_grad():
					SCREAMING_SNAKE_CASE      :					List[str]    =				model(**SCREAMING_SNAKE_CASE_		)[0]
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.Size((1, 11, model.config.vocab_size)		)
				self.assertEqual(output.shape					,					SCREAMING_SNAKE_CASE_		)
				# change to expected output here
				SCREAMING_SNAKE_CASE      :					Dict    =				torch.tensor(
				    [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]					,					device=SCREAMING_SNAKE_CASE_		)
				self.assertTrue(torch.allclose(output[:, :3, :3]					,					SCREAMING_SNAKE_CASE_					,					atol=SCREAMING_SNAKE_CASE_		)		)






			def 							__UpperCamelCase       (	self							:       str		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"		).to(SCREAMING_SNAKE_CASE_		)
				SCREAMING_SNAKE_CASE      :					str    =				MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"					,					src_lang="fr"					,					tgt_lang="en"		)

				SCREAMING_SNAKE_CASE      :					str    =				[
				    '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
				SCREAMING_SNAKE_CASE      :					List[Any]    =				tokenizer(SCREAMING_SNAKE_CASE_					,					padding=SCREAMING_SNAKE_CASE_					,					return_tensors="pt"		)

				SCREAMING_SNAKE_CASE      :					List[str]    =				model.generate(
				    input_ids=dct["input_ids"].to(SCREAMING_SNAKE_CASE_		)					,					attention_mask=dct["attention_mask"].to(SCREAMING_SNAKE_CASE_		)					,					num_beams=5					,					forced_bos_token_id=tokenizer.get_lang_id("en"		)					,					)

				SCREAMING_SNAKE_CASE      :					Dict    =				[
				    '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.',
				]

				SCREAMING_SNAKE_CASE      :					str    =				tokenizer.batch_decode(
				    hypotheses_batch.tolist()					,					clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_					,					skip_special_tokens=SCREAMING_SNAKE_CASE_		)
				assert generated == expected_en



'''simple docstring'''



import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
      import jax
      import jax.numpy as jnp





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





    """simple docstring"""






    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         return f'gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE_	) for s in shape]	)}.npy'







    def       lowercase_				(							self	)				->     Optional[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=(4, 4, 64, 64)			,     SCREAMING_SNAKE_CASE_=False	)				->     str:
         __lowerCamelCase   :	List[str]								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Tuple								=		jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)			,     dtype=SCREAMING_SNAKE_CASE_	)
         return image







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_="CompVis/stable-diffusion-v1-4"	)				->     Dict:
         __lowerCamelCase   :	Union[str, Any]								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Optional[Any]								=		'bf16' if fpaa else None

         __lowerCamelCase				,       __lowerCamelCase   :	str								=		FlaxUNetaDConditionModel.from_pretrained(
             SCREAMING_SNAKE_CASE_			,     subfolder='unet'			,     dtype=SCREAMING_SNAKE_CASE_			,     revision=SCREAMING_SNAKE_CASE_	)
         return model, params







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=(4, 77, 7_68)			,     SCREAMING_SNAKE_CASE_=False	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Optional[Any]								=		jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)			,     dtype=SCREAMING_SNAKE_CASE_	)
         return hidden_states







    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
            [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
            [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
            [3, 10_00, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
            # fmt: on
        ]	)
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Any:
         __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		self.get_latents(SCREAMING_SNAKE_CASE_			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE_			,     fpaa=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[Any]								=		model.apply(
             {'params': params}			,     SCREAMING_SNAKE_CASE_			,     jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	)			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     ).sample

         assert sample.shape == latents.shape

         __lowerCamelCase   :	Any								=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Optional[Any]								=		jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.floataa	)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
         assert jnp.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)







    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
            [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
            [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
            [3, 10_00, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
            # fmt: on
        ]	)
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		self.get_unet_model(model_id='stabilityai/stable-diffusion-2'			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		self.get_latents(SCREAMING_SNAKE_CASE_			,     shape=(4, 4, 96, 96)			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Optional[int]								=		self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE_			,     shape=(4, 77, 10_24)			,     fpaa=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		model.apply(
             {'params': params}			,     SCREAMING_SNAKE_CASE_			,     jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	)			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     ).sample

         assert sample.shape == latents.shape

         __lowerCamelCase   :	Optional[int]								=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Tuple								=		jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.floataa	)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
         assert jnp.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)






