




from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=None       ,    SCREAMING_SNAKE_CASE=None )			->							str:


			'''simple docstring'''







			if attention_mask is None:
						SCREAMING_SNAKE_CASE_								=						tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE       ,    config.pad_token_id )       ,    tf.inta )
			return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class       a_       :
	A    							= OPTConfig
	A    							= {}
	A    							= '''gelu'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=13      ,     SCREAMING_SNAKE_CASE=7      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=99      ,     SCREAMING_SNAKE_CASE=16      ,     SCREAMING_SNAKE_CASE=2      ,     SCREAMING_SNAKE_CASE=4      ,     SCREAMING_SNAKE_CASE=4      ,     SCREAMING_SNAKE_CASE="gelu"      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE=20      ,     SCREAMING_SNAKE_CASE=2      ,     SCREAMING_SNAKE_CASE=1      ,     SCREAMING_SNAKE_CASE=0      ,     SCREAMING_SNAKE_CASE=16      ,     SCREAMING_SNAKE_CASE=16      ,     )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						parent
				SCREAMING_SNAKE_CASE_								=						batch_size
				SCREAMING_SNAKE_CASE_								=						seq_length
				SCREAMING_SNAKE_CASE_								=						is_training
				SCREAMING_SNAKE_CASE_								=						use_labels
				SCREAMING_SNAKE_CASE_								=						vocab_size
				SCREAMING_SNAKE_CASE_								=						hidden_size
				SCREAMING_SNAKE_CASE_								=						num_hidden_layers
				SCREAMING_SNAKE_CASE_								=						num_attention_heads
				SCREAMING_SNAKE_CASE_								=						intermediate_size
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						hidden_dropout_prob
				SCREAMING_SNAKE_CASE_								=						attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE_								=						max_position_embeddings
				SCREAMING_SNAKE_CASE_								=						eos_token_id
				SCREAMING_SNAKE_CASE_								=						pad_token_id
				SCREAMING_SNAKE_CASE_								=						bos_token_id
				SCREAMING_SNAKE_CASE_								=						embed_dim
				SCREAMING_SNAKE_CASE_								=						word_embed_proj_dim
				SCREAMING_SNAKE_CASE_								=						False





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						ids_tensor([self.batch_size, self.seq_length - 1]      ,     self.vocab_size      )
				SCREAMING_SNAKE_CASE_								=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )      ,     1      )
				SCREAMING_SNAKE_CASE_								=						tf.concat([input_ids, eos_tensor]      ,     axis=1      )

				SCREAMING_SNAKE_CASE_								=						self.config_cls(
				    vocab_size=self.vocab_size      ,     hidden_size=self.hidden_size      ,     num_hidden_layers=self.num_hidden_layers      ,     num_attention_heads=self.num_attention_heads      ,     ffn_dim=self.intermediate_size      ,     dropout=self.hidden_dropout_prob      ,     attention_dropout=self.attention_probs_dropout_prob      ,     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      ,     embed_dim=self.embed_dim      ,     word_embed_proj_dim=self.word_embed_proj_dim      ,     is_encoder_decoder=SCREAMING_SNAKE_CASE      ,     **self.config_updates      ,     )
				SCREAMING_SNAKE_CASE_								=						prepare_opt_inputs_dict(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
				return config, inputs_dict





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFOPTModel(config=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						inputs_dict['input_ids']

				SCREAMING_SNAKE_CASE_								=						input_ids[:1, :]
				SCREAMING_SNAKE_CASE_								=						inputs_dict['attention_mask'][:1, :]
				SCREAMING_SNAKE_CASE_								=						1

				# first forward pass
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ,     use_cache=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				SCREAMING_SNAKE_CASE_								=						ids_tensor((self.batch_size, 3)      ,     config.vocab_size      )
				SCREAMING_SNAKE_CASE_								=						tf.cast(ids_tensor((self.batch_size, 3)      ,     2      )      ,     tf.inta      )

				# append to next input_ids and
				SCREAMING_SNAKE_CASE_								=						tf.concat([input_ids, next_tokens]      ,     axis=-1      )
				SCREAMING_SNAKE_CASE_								=						tf.concat([attention_mask, next_attn_mask]      ,     axis=-1      )

				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      )[0]
				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ,     past_key_values=SCREAMING_SNAKE_CASE      )[0]

				self.parent.assertEqual(next_tokens.shape[1]      ,     output_from_past.shape[1]      )

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

				# test that outputs are equal for slice
				tf.debugging.assert_near(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     rtol=1e-3      )




@require_tf
class       a_       (     SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
	A    							= (TFOPTForCausalLM,) if is_tf_available() else ()
	A    							= (
	    {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
	)
	A    							= False
	A    							= False
	A    							= False
	A    							= 10





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFOPTModelTester(self      )
				SCREAMING_SNAKE_CASE_								=						ConfigTester(self      ,     config_class=SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				self.config_tester.run_common_tests()





	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs_for_common()

				def _get_word_embedding_weight(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
							if hasattr(SCREAMING_SNAKE_CASE      ,     'weight'      ):
										return embedding_layer.weight
							else:
										# Here we build the word embeddings weights if not exists.
										# And then we retry to get the attribute once built.
										model.build()
										if hasattr(SCREAMING_SNAKE_CASE      ,     'weight'      ):
													return embedding_layer.weight
										else:
													return None

				for model_class in self.all_model_classes:
							for size in [config.vocab_size - 10, config.vocab_size + 10]:
										# build the embeddings
										SCREAMING_SNAKE_CASE_								=						model_class(config=SCREAMING_SNAKE_CASE      )
										SCREAMING_SNAKE_CASE_								=						_get_word_embedding_weight(SCREAMING_SNAKE_CASE      ,     model.get_input_embeddings()      )
										SCREAMING_SNAKE_CASE_								=						_get_word_embedding_weight(SCREAMING_SNAKE_CASE      ,     model.get_output_embeddings()      )

										# reshape the embeddings
										model.resize_token_embeddings(SCREAMING_SNAKE_CASE      )
										SCREAMING_SNAKE_CASE_								=						_get_word_embedding_weight(SCREAMING_SNAKE_CASE      ,     model.get_input_embeddings()      )
										SCREAMING_SNAKE_CASE_								=						_get_word_embedding_weight(SCREAMING_SNAKE_CASE      ,     model.get_output_embeddings()      )

										# check that the resized embeddings size matches the desired size.
										SCREAMING_SNAKE_CASE_								=						size if size is not None else config.vocab_size

										self.assertEqual(new_input_embeddings.shape[0]      ,     SCREAMING_SNAKE_CASE      )

										# check that weights remain the same after resizing
										SCREAMING_SNAKE_CASE_								=						True
										for pa, pa in zip(old_input_embeddings.value()      ,     new_input_embeddings.value()      ):
													if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
																SCREAMING_SNAKE_CASE_								=						False
										self.assertTrue(SCREAMING_SNAKE_CASE      )

										if old_output_embeddings is not None and new_output_embeddings is not None:
													self.assertEqual(new_output_embeddings.shape[0]      ,     SCREAMING_SNAKE_CASE      )

													SCREAMING_SNAKE_CASE_								=						True
													for pa, pa in zip(old_output_embeddings.value()      ,     new_output_embeddings.value()      ):
																if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
																			SCREAMING_SNAKE_CASE_								=						False
													self.assertTrue(SCREAMING_SNAKE_CASE      )




def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			return tf.constant(SCREAMING_SNAKE_CASE       ,    dtype=tf.intaa )



@require_tf
class       a_       (     unittest.TestCase     ):
	A    							= 99





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						tf.ones((4, 1)      ,     dtype=tf.intaa      ) * 2
				SCREAMING_SNAKE_CASE_								=						tf.concat([ids_tensor((4, 6)      ,     self.vocab_size - 3      ) + 3, eos_column_vector]      ,     axis=1      )
				SCREAMING_SNAKE_CASE_								=						input_ids.shape[0]
				SCREAMING_SNAKE_CASE_								=						OPTConfig(
				    vocab_size=self.vocab_size      ,     hidden_size=24      ,     num_hidden_layers=2      ,     num_attention_heads=2      ,     ffn_dim=32      ,     max_position_embeddings=48      ,     eos_token_id=2      ,     pad_token_id=1      ,     bos_token_id=0      ,     )
				return config, input_ids, batch_size




@require_sentencepiece
@require_tf
class       a_       (     unittest.TestCase     ):





	@slow
	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFOPTModel.from_pretrained('facebook/opt-350m'      )
				SCREAMING_SNAKE_CASE_								=						_long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      )
				SCREAMING_SNAKE_CASE_								=						tf.not_equal(SCREAMING_SNAKE_CASE      ,     model.config.pad_token_id      )
				with tf.GradientTape():
							SCREAMING_SNAKE_CASE_								=						model(input_ids=SCREAMING_SNAKE_CASE      ,     attention_mask=SCREAMING_SNAKE_CASE      ).last_hidden_state
				SCREAMING_SNAKE_CASE_								=						(1, 11, 512)
				self.assertEqual(output.shape      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						tf.constant(
				    [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]]      )
				self.assertTrue(np.allclose(output[:, :3, :3]      ,     SCREAMING_SNAKE_CASE      ,     atol=4e-3      )      )

				SCREAMING_SNAKE_CASE_								=						tf.function(SCREAMING_SNAKE_CASE      ,     jit_compile=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						xla_generate(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )[0]
				self.assertTrue(np.allclose(output[:, :3, :3]      ,     SCREAMING_SNAKE_CASE      ,     atol=4e-2      )      )




@require_tf
@slow
class       a_       (     unittest.TestCase     ):





	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				super().setUp()
				SCREAMING_SNAKE_CASE_								=						'facebook/opt-350m'





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFOPTForCausalLM.from_pretrained(self.path_model      )
				SCREAMING_SNAKE_CASE_								=						GPTaTokenizer.from_pretrained(self.path_model      )

				SCREAMING_SNAKE_CASE_								=						[
				    'Today is a beautiful day and I want to',
				    'In the city of',
				    'Paris is the capital of France and',
				    'Computers and mobile phones have taken',
				]
				# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
				SCREAMING_SNAKE_CASE_								=						tokenizer(SCREAMING_SNAKE_CASE      ,     return_tensors='tf'      ,     padding=SCREAMING_SNAKE_CASE      ,     add_special_tokens=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						tf.math.reduce_mean(model(inputs.input_ids      ,     attention_mask=inputs.attention_mask      )[0]      ,     axis=-1      )
				SCREAMING_SNAKE_CASE_								=						tf.constant(
				    [
				        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
				        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
				        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
				        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
				    ]      )
				self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     atol=1e-4      )      )

				SCREAMING_SNAKE_CASE_								=						tf.function(SCREAMING_SNAKE_CASE      ,     jit_compile=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						tf.math.reduce_mean(xla_generate(inputs.input_ids      ,     attention_mask=inputs.attention_mask      )[0]      ,     axis=-1      )
				self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     atol=1e-4      )      )




@require_tf
@slow
class       a_       (     unittest.TestCase     ):





	@property
	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				return [
				    "Today is a beautiful day and I want",
				    "In the city of",
				    "Paris is the capital of France and",
				    "Computers and mobile phones have taken",
				]





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'facebook/opt-125m'

				SCREAMING_SNAKE_CASE_								=						[
				    'Today is a beautiful day and I want to',
				    'In the city of New York, the city',
				    'Paris is the capital of France and the capital',
				    'Computers and mobile phones have taken over the',
				]

				SCREAMING_SNAKE_CASE_								=						[]
				SCREAMING_SNAKE_CASE_								=						GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						TFOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE      )

				for prompt in self.prompts:
							SCREAMING_SNAKE_CASE_								=						tokenizer(SCREAMING_SNAKE_CASE      ,     return_tensors='tf'      ).input_ids

							SCREAMING_SNAKE_CASE_								=						model.generate(SCREAMING_SNAKE_CASE      ,     max_length=10      )

							SCREAMING_SNAKE_CASE_								=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,     skip_special_tokens=SCREAMING_SNAKE_CASE      )
							predicted_outputs += generated_string

				self.assertListEqual(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'facebook/opt-350m'

				SCREAMING_SNAKE_CASE_								=						GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						TFOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						'left'

				# use different length sentences to test batching
				SCREAMING_SNAKE_CASE_								=						[
				    'Hello, my dog is a little',
				    'Today, I',
				]

				SCREAMING_SNAKE_CASE_								=						tokenizer(SCREAMING_SNAKE_CASE      ,     return_tensors='tf'      ,     padding=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						inputs['input_ids']

				SCREAMING_SNAKE_CASE_								=						model.generate(input_ids=SCREAMING_SNAKE_CASE      ,     attention_mask=inputs['attention_mask']      )

				SCREAMING_SNAKE_CASE_								=						tokenizer(sentences[0]      ,     return_tensors='tf'      ).input_ids
				SCREAMING_SNAKE_CASE_								=						model.generate(input_ids=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						inputs_non_padded.shape[-1] - tf.math.reduce_sum(
				    tf.cast(inputs['attention_mask'][-1]      ,     tf.intaa      )      )
				SCREAMING_SNAKE_CASE_								=						tokenizer(sentences[1]      ,     return_tensors='tf'      ).input_ids
				SCREAMING_SNAKE_CASE_								=						model.generate(input_ids=SCREAMING_SNAKE_CASE      ,     max_length=model.config.max_length - num_paddings      )

				SCREAMING_SNAKE_CASE_								=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,     skip_special_tokens=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						tokenizer.decode(output_non_padded[0]      ,     skip_special_tokens=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						tokenizer.decode(output_padded[0]      ,     skip_special_tokens=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						[
				    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
				    'Today, I was in the middle of a conversation with a friend about the',
				]
				self.assertListEqual(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
				self.assertListEqual(SCREAMING_SNAKE_CASE      ,     [non_padded_sentence, padded_sentence]      )





	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'facebook/opt-350m'

				SCREAMING_SNAKE_CASE_								=						[
				    'Today is a beautiful day and I want to',
				    'In the city of San Francisco, the city',
				    'Paris is the capital of France and the capital',
				    'Computers and mobile phones have taken over the',
				]

				SCREAMING_SNAKE_CASE_								=						[]
				SCREAMING_SNAKE_CASE_								=						GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						TFOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE      )

				for prompt in self.prompts:
							SCREAMING_SNAKE_CASE_								=						tokenizer(SCREAMING_SNAKE_CASE      ,     return_tensors='tf'      ).input_ids

							SCREAMING_SNAKE_CASE_								=						model.generate(SCREAMING_SNAKE_CASE      ,     max_length=10      )

							SCREAMING_SNAKE_CASE_								=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,     skip_special_tokens=SCREAMING_SNAKE_CASE      )
							predicted_outputs += generated_string

				self.assertListEqual(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
				import tensorflow as tf

				from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class       a_       (     unittest.TestCase     ):





	@slow
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('google/mt5-small'      )

				SCREAMING_SNAKE_CASE_								=						tokenizer('Hello there'      ,     return_tensors='tf'      ).input_ids
				SCREAMING_SNAKE_CASE_								=						tokenizer('Hi I am'      ,     return_tensors='tf'      ).input_ids

				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      ).loss
				SCREAMING_SNAKE_CASE_								=						-tf.math.reduce_mean(SCREAMING_SNAKE_CASE      ).numpy()

				SCREAMING_SNAKE_CASE_								=						-2_1.2_2_8_1_6_8
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )
