



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		__UpperCAmelCase    	=  '''platform'''

		import jax
		import jax.numpy as jnp

		from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer





def     __lowerCamelCase							(   __magic_name__						:	Dict  ,			__magic_name__						:	Union[str, Any]  ,			__magic_name__						:	int=None  ,			__magic_name__						:	Tuple=None    ):
							if attention_mask is None:
														a__:							Optional[Any]									=np.where(input_ids != config.pad_token_id  ,			1  ,			0    )
							return {
							    "input_ids": input_ids,
							    "attention_mask": attention_mask,
							}


@require_flax
class 						lowerCamelCase__			:


						def __init__(			self    :	Optional[Any]   ,							_a    :	Optional[Any]   ,							_a    :	Union[str, Any]=1_3   ,							_a    :	Union[str, Any]=7   ,							_a    :	Dict=True   ,							_a    :	Optional[Any]=False   ,							_a    :	Optional[int]=9_9   ,							_a    :	Optional[Any]=1_6   ,							_a    :	Dict=2   ,							_a    :	Any=4   ,							_a    :	Dict=4   ,							_a    :	str="gelu"   ,							_a    :	Any=0.1   ,							_a    :	Union[str, Any]=0.1   ,							_a    :	int=2_0   ,							_a    :	List[Any]=2   ,							_a    :	List[str]=1   ,							_a    :	Any=0   ,							_a    :	Optional[Any]=1_6   ,							_a    :	List[Any]=1_6   ,							_a    :	Union[str, Any]=0.0_2   ,							):
													a__:							List[str]									=parent
													a__:							Optional[int]									=batch_size
													a__:							str									=seq_length
													a__:							List[str]									=is_training
													a__:							Optional[int]									=use_labels
													a__:							Tuple									=vocab_size
													a__:							List[Any]									=hidden_size
													a__:							Any									=num_hidden_layers
													a__:							Any									=num_attention_heads
													a__:							List[str]									=intermediate_size
													a__:							Optional[int]									=hidden_act
													a__:							List[Any]									=hidden_dropout_prob
													a__:							Optional[int]									=attention_probs_dropout_prob
													a__:							str									=max_position_embeddings
													a__:							Optional[Any]									=eos_token_id
													a__:							Optional[int]									=pad_token_id
													a__:							str									=bos_token_id
													a__:							List[str]									=embed_dim
													a__:							Optional[int]									=word_embed_proj_dim
													a__:							Union[str, Any]									=initializer_range
													a__:							Dict									=False


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Dict									=np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,							self.vocab_size      )   ,							3   ,							self.vocab_size      )
													a__:							Optional[Any]									=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,							dtype=np.intaa      ))   ,							-1      )

													a__:							Any									=OPTConfig(
													    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   ,							is_encoder_decoder=_a   ,							word_embed_proj_dim=self.word_embed_proj_dim   ,							initializer_range=self.initializer_range   ,							use_cache=_a   ,							)
													a__:							int									=prepare_opt_inputs_dict(_a   ,							_a      )
													return config, inputs_dict


						def 	_lowerCamelCase      (			self    :	Any      ):
													a__				,      a__:							Optional[int]									=self.prepare_config_and_inputs()
													return config, inputs_dict


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	Optional[int]   ,							_a    :	List[str]   ,							_a    :	str      ):
													a__:							int									=2_0
													a__:							Dict									=model_class_name(_a      )

													a__:							Optional[Any]									=inputs_dict["input_ids"]
													a__:							Dict									=inputs_dict["attention_mask"]

													a__:							Optional[Any]									=model.init_cache(input_ids.shape[0]   ,							_a      )
													a__:							List[str]									=jnp.ones((input_ids.shape[0], max_length)   ,							dtype="i4"      )

													a__:							Optional[int]									=jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1      )[None, :]   ,							(input_ids.shape[0], input_ids.shape[-1] - 1)   ,							)
													a__:							int									=model(
													    input_ids[:, :-1]   ,							attention_mask=_a   ,							past_key_values=_a   ,							position_ids=_a   ,							)

													a__:							Union[str, Any]									=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,							dtype="i4"      )
													a__:							List[str]									=model(
													    input_ids[:, -1:]   ,							attention_mask=_a   ,							past_key_values=outputs_cache.past_key_values   ,							position_ids=_a   ,							)

													a__:							List[str]									=model(_a      )

													a__:							int									=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
													self.parent.assertTrue(diff < 1e-3   ,							msg=F"Max diff is {diff}"      )







						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	Union[str, Any]   ,							_a    :	List[Any]   ,							_a    :	str      ):
													a__:							Union[str, Any]									=2_0
													a__:							List[str]									=model_class_name(_a      )

													a__				,      a__:							Tuple									=(
													    inputs_dict["input_ids"],
													    inputs_dict["attention_mask"],
													)

													a__:							Any									=jnp.concatenate(
													    [
													        attention_mask,
													        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])      ),
													    ]   ,							axis=-1   ,							)

													a__:							int									=model.init_cache(input_ids.shape[0]   ,							_a      )
													a__:							Optional[int]									=jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1      )[None, :]   ,							(input_ids.shape[0], input_ids.shape[-1] - 1)   ,							)

													a__:							Optional[Any]									=model(
													    input_ids[:, :-1]   ,							attention_mask=_a   ,							past_key_values=_a   ,							position_ids=_a   ,							)
													a__:							Optional[Any]									=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,							dtype="i4"      )
													a__:							Optional[int]									=model(
													    input_ids[:, -1:]   ,							past_key_values=outputs_cache.past_key_values   ,							attention_mask=_a   ,							position_ids=_a   ,							)

													a__:							Any									=model(_a   ,							attention_mask=_a      )

													a__:							Union[str, Any]									=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
													self.parent.assertTrue(diff < 1e-3   ,							msg=F"Max diff is {diff}"      )


@require_flax
class 						lowerCamelCase__			(		_a					,       unittest.TestCase					,       _a ):
						_lowerCAmelCase             =			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
						_lowerCAmelCase             =			() if is_flax_available() else ()


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							Dict									=FlaxOPTModelTester(self      )


						def 	_lowerCamelCase      (			self    :	Any      ):
													a__				,      a__:							int									=self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																				self.model_tester.check_use_cache_forward(_a   ,							_a   ,							_a      )


						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__				,      a__:							Dict									=self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																				self.model_tester.check_use_cache_forward_with_attn_mask(_a   ,							_a   ,							_a      )







						@slow
						def 	_lowerCamelCase      (			self    :	Dict      ):
													for model_class_name in self.all_model_classes:
																				a__:							Tuple									=model_class_name.from_pretrained("facebook/opt-125m"      )
																				a__:							Union[str, Any]									=np.ones((1, 1)      ) * model.config.eos_token_id
																				a__:							Optional[int]									=model(_a      )
																				self.assertIsNotNone(_a      )


@require_sentencepiece
@require_flax
class 						lowerCamelCase__			(		unittest.TestCase ):


						@slow
						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							Dict									=FlaxOPTModel.from_pretrained("facebook/opt-350m"      )
													a__:							List[Any]									=jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]      )
													a__:							Any									=model(input_ids=_a      ).last_hidden_state
													a__:							Tuple									=(1, 1_1, 5_1_2)
													self.assertEqual(output.shape   ,							_a      )
													a__:							int									=jnp.array(
													    [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]      )
													self.assertTrue(jnp.allclose(output[:, :3, :3]   ,							_a   ,							atol=4e-2      )      )


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


						def 	_lowerCamelCase      (			self    :	Any      ):
													super().setUp()
													a__:							Dict									="facebook/opt-350m"







						def 	_lowerCamelCase      (			self    :	str      ):
													a__:							List[Any]									=FlaxOPTForCausalLM.from_pretrained(self.path_model      )
													a__:							Any									=GPTaTokenizer.from_pretrained(self.path_model      )

													a__:							Any									=[
													    "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
													a__:							Optional[Any]									=tokenizer(_a   ,							return_tensors="jax"   ,							padding=_a   ,							add_special_tokens=_a      )
													a__:							int									=model(inputs.input_ids   ,							attention_mask=inputs.attention_mask      )[0].mean(axis=-1      )
													a__:							List[str]									=jnp.array(
													    [
													        [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(jnp.allclose(_a   ,							_a   ,							atol=4e-2      )      )

													a__:							Optional[int]									=jax.jit(_a      )
													a__:							int									=model(inputs.input_ids   ,							attention_mask=inputs.attention_mask      )[0].mean(axis=-1      )
													self.assertTrue(jnp.allclose(_a   ,							_a   ,							atol=4e-2      )      )


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


						@property
						def 	_lowerCamelCase      (			self    :	Any      ):
													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 	_lowerCamelCase      (			self    :	Any      ):
													a__:							Optional[int]									="facebook/opt-125m"

													a__:							str									=[
													    "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",
													]

													a__:							Union[str, Any]									=[]

													a__:							Optional[Any]									=FlaxOPTForCausalLM.from_pretrained(_a      )
													a__:							Tuple									=GPTaTokenizer.from_pretrained(_a      )

													for prompt in self.prompts:
																				a__:							int									=tokenizer(_a   ,							return_tensors="jax"      ).input_ids

																				a__:							Any									=model.generate(_a   ,							max_length=1_0      )
																				a__:							List[Any]									=generated_ids[0]

																				a__:							Any									=tokenizer.batch_decode(_a   ,							skip_special_tokens=_a      )
																				predicted_outputs += generated_string

													self.assertListEqual(_a   ,							_a      )


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__:							Optional[int]									="facebook/opt-350m"

													a__:							List[Any]									=[
													    "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",
													]

													a__:							Optional[Any]									=[]
													a__:							Optional[int]									=FlaxOPTForCausalLM.from_pretrained(_a      )
													a__:							str									=GPTaTokenizer.from_pretrained(_a      )

													for prompt in self.prompts:
																				a__:							Any									=tokenizer(_a   ,							return_tensors="jax"      ).input_ids

																				a__:							Optional[int]									=model.generate(_a   ,							max_length=1_0      )
																				a__:							Tuple									=generated_ids[0]

																				a__:							Optional[Any]									=tokenizer.batch_decode(_a   ,							skip_special_tokens=_a      )
																				predicted_outputs += generated_string

													self.assertListEqual(_a   ,							_a      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							Any									="facebook/opt-125m"
													a__:							int									=[
													    "Today is a beautiful day and I want to thank",
													    "In the city of Rome Canaver Canaver Canaver Canaver",
													]
													a__:							Union[str, Any]									=FlaxOPTForCausalLM.from_pretrained(_a      )
													a__:							List[Any]									=GPTaTokenizer.from_pretrained(_a      )
													a__:							Optional[Any]									=tokenizer(
													    [
													        "Today is a beautiful day and I want to",
													        "In the city of",
													    ]   ,							return_tensors="jax"   ,							padding=_a   ,							)

													a__:							Optional[int]									=jax.jit(model.generate      )

													a__:							Optional[int]									=jit_generate(inputs["input_ids"]   ,							attention_mask=inputs["attention_mask"]      ).sequences

													a__:							str									=tokenizer.batch_decode(_a   ,							skip_special_tokens=_a      )

													self.assertIsNotNone(_a   ,							_a      )







						def 	_lowerCamelCase      (			self    :	str      ):
													a__:							int									="facebook/opt-350m"

													a__:							List[str]									=GPTaTokenizer.from_pretrained(_a      )
													a__:							Optional[int]									=FlaxOPTForCausalLM.from_pretrained(_a      )

													a__:							List[Any]									="left"

													# use different length sentences to test batching
													a__:							Any									=[
													    "Hello, my dog is a little",
													    "Today, I",
													]

													a__:							str									=tokenizer(_a   ,							return_tensors="jax"   ,							padding=_a      )
													a__:							Any									=inputs["input_ids"]

													a__:							List[str]									=model.generate(input_ids=_a   ,							attention_mask=inputs["attention_mask"]   ,							trace=_a      )

													a__:							Any									=tokenizer(sentences[0]   ,							return_tensors="jax"      ).input_ids
													a__:							Optional[Any]									=model.generate(input_ids=_a      )

													a__:							str									=inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
													a__:							Union[str, Any]									=tokenizer(sentences[1]   ,							return_tensors="jax"      ).input_ids
													a__:							Dict									=model.generate(input_ids=_a   ,							max_length=model.config.max_length - num_paddings      )

													a__:							List[str]									=tokenizer.batch_decode(outputs[0]   ,							skip_special_tokens=_a      )
													a__:							str									=tokenizer.decode(output_non_padded[0][0]   ,							skip_special_tokens=_a      )
													a__:							Optional[Any]									=tokenizer.decode(output_padded[0][0]   ,							skip_special_tokens=_a      )

													a__:							Tuple									=[
													    "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(_a   ,							_a      )
													self.assertListEqual(_a   ,							[non_padded_sentence, padded_sentence]      )










from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__UpperCAmelCase    	=  {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    	=  ['''ErnieMTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    	=  [
		    '''ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ErnieMForMultipleChoice''',
		    '''ErnieMForQuestionAnswering''',
		    '''ErnieMForSequenceClassification''',
		    '''ErnieMForTokenClassification''',
		    '''ErnieMModel''',
		    '''ErnieMPreTrainedModel''',
		    '''ErnieMForInformationExtraction''',
		]


if TYPE_CHECKING:
		from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ernie_m import ErnieMTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ernie_m import (
				    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ErnieMForInformationExtraction,
				    ErnieMForMultipleChoice,
				    ErnieMForQuestionAnswering,
				    ErnieMForSequenceClassification,
				    ErnieMForTokenClassification,
				    ErnieMModel,
				    ErnieMPreTrainedModel,
				)


else:
		import sys

		__UpperCAmelCase    	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





