



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   SCREAMING_SNAKE_CASE_       (  _snake_case  :int  ,      _snake_case  :int  ,      _snake_case  :Union[str, Any]=None  ,      _snake_case  :Optional[int]=None							)     ->    Optional[int]:
			if attention_mask is None:
						_A						     =					np.where(input_ids != config.pad_token_id  ,      1  ,      0							)
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			}


@require_flax
class   lowerCamelCase__  :



	"""simple docstring"""

	def __init__(						self				:				List[str] ,	__lowerCAmelCase				:				Tuple ,	__lowerCAmelCase				:				List[str]=13 ,	__lowerCAmelCase				:				List[str]=7 ,	__lowerCAmelCase				:				List[Any]=True ,	__lowerCAmelCase				:				Optional[int]=False ,	__lowerCAmelCase				:				Union[str, Any]=99 ,	__lowerCAmelCase				:				List[str]=16 ,	__lowerCAmelCase				:				Optional[Any]=2 ,	__lowerCAmelCase				:				Tuple=4 ,	__lowerCAmelCase				:				Optional[Any]=4 ,	__lowerCAmelCase				:				List[Any]="gelu" ,	__lowerCAmelCase				:				List[str]=0.1 ,	__lowerCAmelCase				:				Union[str, Any]=0.1 ,	__lowerCAmelCase				:				List[str]=20 ,	__lowerCAmelCase				:				Any=2 ,	__lowerCAmelCase				:				Union[str, Any]=1 ,	__lowerCAmelCase				:				Dict=0 ,	__lowerCAmelCase				:				Optional[Any]=16 ,	__lowerCAmelCase				:				Union[str, Any]=16 ,	__lowerCAmelCase				:				List[Any]=0.02 ,	)      ->  str:
				_A						     =					parent
				_A						     =					batch_size
				_A						     =					seq_length
				_A						     =					is_training
				_A						     =					use_labels
				_A						     =					vocab_size
				_A						     =					hidden_size
				_A						     =					num_hidden_layers
				_A						     =					num_attention_heads
				_A						     =					intermediate_size
				_A						     =					hidden_act
				_A						     =					hidden_dropout_prob
				_A						     =					attention_probs_dropout_prob
				_A						     =					max_position_embeddings
				_A						     =					eos_token_id
				_A						     =					pad_token_id
				_A						     =					bos_token_id
				_A						     =					embed_dim
				_A						     =					word_embed_proj_dim
				_A						     =					initializer_range
				_A						     =					False

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

				_A						     =					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=lowercase_ ,	word_embed_proj_dim=self.word_embed_proj_dim ,	initializer_range=self.initializer_range ,	use_cache=lowercase_ ,	)
				_A						     =					prepare_opt_inputs_dict(lowercase_ ,	lowercase_			)
				return config, inputs_dict

	def 						snake_case_			(						self				:				List[str]			)      ->  Tuple:
				_A						     =					self.prepare_config_and_inputs()
				return config, inputs_dict

	def 						snake_case_			(						self				:				Optional[int] ,	__lowerCAmelCase				:				Dict ,	__lowerCAmelCase				:				Dict ,	__lowerCAmelCase				:				List[Any]			)      ->  Optional[Any]:
				_A						     =					20
				_A						     =					model_class_name(lowercase_			)

				_A						     =					inputs_dict['''input_ids''']
				_A						     =					inputs_dict['''attention_mask''']

				_A						     =					model.init_cache(input_ids.shape[0] ,	lowercase_			)
				_A						     =					jnp.ones((input_ids.shape[0], max_length) ,	dtype='''i4'''			)

				_A						     =					jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1			)[None, :] ,	(input_ids.shape[0], input_ids.shape[-1] - 1) ,	)
				_A						     =					model(
				    input_ids[:, :-1] ,	attention_mask=lowercase_ ,	past_key_values=lowercase_ ,	position_ids=lowercase_ ,	)

				_A						     =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype='''i4'''			)
				_A						     =					model(
				    input_ids[:, -1:] ,	attention_mask=lowercase_ ,	past_key_values=outputs_cache.past_key_values ,	position_ids=lowercase_ ,	)

				_A						     =					model(lowercase_			)

				_A						     =					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 						snake_case_			(						self				:				Optional[Any] ,	__lowerCAmelCase				:				Optional[Any] ,	__lowerCAmelCase				:				Optional[int] ,	__lowerCAmelCase				:				Any			)      ->  Tuple:
				_A						     =					20
				_A						     =					model_class_name(lowercase_			)

				_A						     =					(
				    inputs_dict['''input_ids'''],
				    inputs_dict['''attention_mask'''],
				)

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

				_A						     =					model.init_cache(input_ids.shape[0] ,	lowercase_			)
				_A						     =					jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1			)[None, :] ,	(input_ids.shape[0], input_ids.shape[-1] - 1) ,	)

				_A						     =					model(
				    input_ids[:, :-1] ,	attention_mask=lowercase_ ,	past_key_values=lowercase_ ,	position_ids=lowercase_ ,	)
				_A						     =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype='''i4'''			)
				_A						     =					model(
				    input_ids[:, -1:] ,	past_key_values=outputs_cache.past_key_values ,	attention_mask=lowercase_ ,	position_ids=lowercase_ ,	)

				_A						     =					model(lowercase_ ,	attention_mask=lowercase_			)

				_A						     =					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__  (					__UpperCAmelCase       ,     unittest.TestCase       ,     __UpperCAmelCase):



	"""simple docstring"""

	a__				:   Any   =     (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
	a__				:   Optional[int]   =     () if is_flax_available() else ()

	def 						snake_case_			(						self				:				List[str]			)      ->  Optional[Any]:
				_A						     =					FlaxOPTModelTester(self			)

	def 						snake_case_			(						self				:				List[str]			)      ->  Dict:
				_A						     =					self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward(lowercase_ ,	lowercase_ ,	lowercase_			)

	def 						snake_case_			(						self				:				str			)      ->  Union[str, Any]:
				_A						     =					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(lowercase_ ,	lowercase_ ,	lowercase_			)

	@slow
	def 						snake_case_			(						self				:				Union[str, Any]			)      ->  Optional[Any]:

				for model_class_name in self.all_model_classes:
							_A						     =					model_class_name.from_pretrained('''facebook/opt-125m'''			)
							_A						     =					np.ones((1, 1)			) * model.config.eos_token_id
							_A						     =					model(lowercase_			)
							self.assertIsNotNone(lowercase_			)


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



	"""simple docstring"""

	@slow
	def 						snake_case_			(						self				:				Dict			)      ->  List[str]:
				_A						     =					FlaxOPTModel.from_pretrained('''facebook/opt-350m'''			)
				_A						     =					jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]			)
				_A						     =					model(input_ids=lowercase_			).last_hidden_state
				_A						     =					(1, 11, 5_12)
				self.assertEqual(output.shape ,	lowercase_			)
				_A						     =					jnp.array(
				    [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]			)
				self.assertTrue(jnp.allclose(output[:, :3, :3] ,	lowercase_ ,	atol=4E-2			)			)


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



	"""simple docstring"""

	def 						snake_case_			(						self				:				List[str]			)      ->  Union[str, Any]:
				super().setUp()
				_A						     =					'''facebook/opt-350m'''

	def 						snake_case_			(						self				:				int			)      ->  Union[str, Any]:
				_A						     =					FlaxOPTForCausalLM.from_pretrained(self.path_model			)
				_A						     =					GPTaTokenizer.from_pretrained(self.path_model			)

				_A						     =					[
				    '''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						     =					tokenizer(lowercase_ ,	return_tensors='''jax''' ,	padding=lowercase_ ,	add_special_tokens=lowercase_			)
				_A						     =					model(inputs.input_ids ,	attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
				_A						     =					jnp.array(
				    [
				        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
				        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
				        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
				        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
				    ]			)
				self.assertTrue(jnp.allclose(lowercase_ ,	lowercase_ ,	atol=4E-2			)			)

				_A						     =					jax.jit(lowercase_			)
				_A						     =					model(inputs.input_ids ,	attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
				self.assertTrue(jnp.allclose(lowercase_ ,	lowercase_ ,	atol=4E-2			)			)


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



	"""simple docstring"""

	@property
	def 						snake_case_			(						self				:				Dict			)      ->  Optional[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 						snake_case_			(						self				:				Any			)      ->  Any:
				_A						     =					'''facebook/opt-125m'''

				_A						     =					[
				    '''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						     =					[]

				_A						     =					FlaxOPTForCausalLM.from_pretrained(lowercase_			)
				_A						     =					GPTaTokenizer.from_pretrained(lowercase_			)

				for prompt in self.prompts:
							_A						     =					tokenizer(lowercase_ ,	return_tensors='''jax'''			).input_ids

							_A						     =					model.generate(lowercase_ ,	max_length=10			)
							_A						     =					generated_ids[0]

							_A						     =					tokenizer.batch_decode(lowercase_ ,	skip_special_tokens=lowercase_			)
							predicted_outputs += generated_string

				self.assertListEqual(lowercase_ ,	lowercase_			)

	def 						snake_case_			(						self				:				Any			)      ->  int:
				_A						     =					'''facebook/opt-350m'''

				_A						     =					[
				    '''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						     =					[]
				_A						     =					FlaxOPTForCausalLM.from_pretrained(lowercase_			)
				_A						     =					GPTaTokenizer.from_pretrained(lowercase_			)

				for prompt in self.prompts:
							_A						     =					tokenizer(lowercase_ ,	return_tensors='''jax'''			).input_ids

							_A						     =					model.generate(lowercase_ ,	max_length=10			)
							_A						     =					generated_ids[0]

							_A						     =					tokenizer.batch_decode(lowercase_ ,	skip_special_tokens=lowercase_			)
							predicted_outputs += generated_string

				self.assertListEqual(lowercase_ ,	lowercase_			)

	def 						snake_case_			(						self				:				Optional[int]			)      ->  int:
				_A						     =					'''facebook/opt-125m'''
				_A						     =					[
				    '''Today is a beautiful day and I want to thank''',
				    '''In the city of Rome Canaver Canaver Canaver Canaver''',
				]
				_A						     =					FlaxOPTForCausalLM.from_pretrained(lowercase_			)
				_A						     =					GPTaTokenizer.from_pretrained(lowercase_			)
				_A						     =					tokenizer(
				    [
				        '''Today is a beautiful day and I want to''',
				        '''In the city of''',
				    ] ,	return_tensors='''jax''' ,	padding=lowercase_ ,	)

				_A						     =					jax.jit(model.generate			)

				_A						     =					jit_generate(inputs['''input_ids'''] ,	attention_mask=inputs['''attention_mask''']			).sequences

				_A						     =					tokenizer.batch_decode(lowercase_ ,	skip_special_tokens=lowercase_			)

				self.assertIsNotNone(lowercase_ ,	lowercase_			)

	def 						snake_case_			(						self				:				Optional[Any]			)      ->  Tuple:
				_A						     =					'''facebook/opt-350m'''

				_A						     =					GPTaTokenizer.from_pretrained(lowercase_			)
				_A						     =					FlaxOPTForCausalLM.from_pretrained(lowercase_			)

				_A						     =					'''left'''

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

				_A						     =					tokenizer(lowercase_ ,	return_tensors='''jax''' ,	padding=lowercase_			)
				_A						     =					inputs['''input_ids''']

				_A						     =					model.generate(input_ids=lowercase_ ,	attention_mask=inputs['''attention_mask'''] ,	trace=lowercase_			)

				_A						     =					tokenizer(sentences[0] ,	return_tensors='''jax'''			).input_ids
				_A						     =					model.generate(input_ids=lowercase_			)

				_A						     =					inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
				_A						     =					tokenizer(sentences[1] ,	return_tensors='''jax'''			).input_ids
				_A						     =					model.generate(input_ids=lowercase_ ,	max_length=model.config.max_length - num_paddings			)

				_A						     =					tokenizer.batch_decode(outputs[0] ,	skip_special_tokens=lowercase_			)
				_A						     =					tokenizer.decode(output_non_padded[0][0] ,	skip_special_tokens=lowercase_			)
				_A						     =					tokenizer.decode(output_padded[0][0] ,	skip_special_tokens=lowercase_			)

				_A						     =					[
				    '''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(lowercase_ ,	lowercase_			)
				self.assertListEqual(lowercase_ ,	[non_padded_sentence, padded_sentence]			)








from typing import TYPE_CHECKING

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


UpperCAmelCase_		   =				{
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_		   =				["""XLMProphetNetTokenizer"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_		   =				[
       """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XLMProphetNetDecoder""",
       """XLMProphetNetEncoder""",
       """XLMProphetNetForCausalLM""",
       """XLMProphetNetForConditionalGeneration""",
       """XLMProphetNetModel""",
       """XLMProphetNetPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm_prophetnet import (
          XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMProphetNetDecoder,
          XLMProphetNetEncoder,
          XLMProphetNetForCausalLM,
          XLMProphetNetForConditionalGeneration,
          XLMProphetNetModel,
          XLMProphetNetPreTrainedModel,
      )

else:
   import sys

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



