
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
			_A       =					"""platform"""

			import jax
			import jax.numpy as jnp

			from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def        lowerCamelCase__    (    __lowerCAmelCase  :  Tuple   ,							__lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  Any=None   ,							__lowerCAmelCase  :  int=None    ):




			"""simple docstring"""


			if attention_mask is None:
						lowerCAmelCase_      =     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    ,    _UpperCamelCase    ,    _UpperCamelCase=13    ,    _UpperCamelCase=7    ,    _UpperCamelCase=True    ,    _UpperCamelCase=False    ,    _UpperCamelCase=99    ,    _UpperCamelCase=16    ,    _UpperCamelCase=2    ,    _UpperCamelCase=4    ,    _UpperCamelCase=4    ,    _UpperCamelCase="gelu"    ,    _UpperCamelCase=0.1    ,    _UpperCamelCase=0.1    ,    _UpperCamelCase=20    ,    _UpperCamelCase=2    ,    _UpperCamelCase=1    ,    _UpperCamelCase=0    ,    _UpperCamelCase=16    ,    _UpperCamelCase=16    ,    _UpperCamelCase=0.02    ,    )    ->				Any:
							lowerCAmelCase_      =     parent
							lowerCAmelCase_      =     batch_size
							lowerCAmelCase_      =     seq_length
							lowerCAmelCase_      =     is_training
							lowerCAmelCase_      =     use_labels
							lowerCAmelCase_      =     vocab_size
							lowerCAmelCase_      =     hidden_size
							lowerCAmelCase_      =     num_hidden_layers
							lowerCAmelCase_      =     num_attention_heads
							lowerCAmelCase_      =     intermediate_size
							lowerCAmelCase_      =     hidden_act
							lowerCAmelCase_      =     hidden_dropout_prob
							lowerCAmelCase_      =     attention_probs_dropout_prob
							lowerCAmelCase_      =     max_position_embeddings
							lowerCAmelCase_      =     eos_token_id
							lowerCAmelCase_      =     pad_token_id
							lowerCAmelCase_      =     bos_token_id
							lowerCAmelCase_      =     embed_dim
							lowerCAmelCase_      =     word_embed_proj_dim
							lowerCAmelCase_      =     initializer_range
							lowerCAmelCase_      =     False




				def 					__a						( self					)    ->				List[Any]:
							lowerCAmelCase_      =     np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,    self.vocab_size					)    ,    3    ,    self.vocab_size					)
							lowerCAmelCase_      =     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,    dtype=np.intaa					))    ,    -1					)

							lowerCAmelCase_      =     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=lowerCAmelCase__    ,    word_embed_proj_dim=self.word_embed_proj_dim    ,    initializer_range=self.initializer_range    ,    use_cache=lowerCAmelCase__    ,    )
							lowerCAmelCase_      =     prepare_opt_inputs_dict(lowerCAmelCase__    ,    lowerCAmelCase__					)
							return config, inputs_dict




				def 					__a						( self					)    ->				Optional[int]:
							lowerCAmelCase_     ,  lowerCAmelCase_      =     self.prepare_config_and_inputs()
							return config, inputs_dict




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     20
							lowerCAmelCase_      =     model_class_name(lowerCAmelCase__					)

							lowerCAmelCase_      =     inputs_dict["input_ids"]
							lowerCAmelCase_      =     inputs_dict["attention_mask"]

							lowerCAmelCase_      =     model.init_cache(input_ids.shape[0]    ,    lowerCAmelCase__					)
							lowerCAmelCase_      =     jnp.ones((input_ids.shape[0], max_length)    ,    dtype="i4"					)

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

							lowerCAmelCase_      =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,    dtype="i4"					)
							lowerCAmelCase_      =     model(
							    input_ids[:, -1:]    ,    attention_mask=lowerCAmelCase__    ,    past_key_values=outputs_cache.past_key_values    ,    position_ids=lowerCAmelCase__    ,    )

							lowerCAmelCase_      =     model(lowerCAmelCase__					)

							lowerCAmelCase_      =     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 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Union[str, Any]:
							lowerCAmelCase_      =     20
							lowerCAmelCase_      =     model_class_name(lowerCAmelCase__					)

							lowerCAmelCase_     ,  lowerCAmelCase_      =     (
							    inputs_dict["input_ids"],
							    inputs_dict["attention_mask"],
							)

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

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

							lowerCAmelCase_      =     model(
							    input_ids[:, :-1]    ,    attention_mask=lowerCAmelCase__    ,    past_key_values=lowerCAmelCase__    ,    position_ids=lowerCAmelCase__    ,    )
							lowerCAmelCase_      =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,    dtype="i4"					)
							lowerCAmelCase_      =     model(
							    input_ids[:, -1:]    ,    past_key_values=outputs_cache.past_key_values    ,    attention_mask=lowerCAmelCase__    ,    position_ids=lowerCAmelCase__    ,    )

							lowerCAmelCase_      =     model(lowerCAmelCase__    ,    attention_mask=lowerCAmelCase__					)

							lowerCAmelCase_      =     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	):
				_lowercase      		=(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
				_lowercase      		=() if is_flax_available() else ()




				def 					__a						( self					)    ->				List[str]:
							lowerCAmelCase_      =     FlaxOPTModelTester(self					)




				def 					__a						( self					)    ->				List[Any]:
							lowerCAmelCase_     ,  lowerCAmelCase_      =     self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
										self.model_tester.check_use_cache_forward(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__					)




				def 					__a						( self					)    ->				Optional[Any]:
							lowerCAmelCase_     ,  lowerCAmelCase_      =     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(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__					)




				@slow
				def 					__a						( self					)    ->				str:


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





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




				@slow
				def 					__a						( self					)    ->				Optional[Any]:
							lowerCAmelCase_      =     FlaxOPTModel.from_pretrained("facebook/opt-350m"					)
							lowerCAmelCase_      =     jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					)
							lowerCAmelCase_      =     model(input_ids=lowerCAmelCase__					).last_hidden_state
							lowerCAmelCase_      =     (1, 11, 512)
							self.assertEqual(output.shape    ,    lowerCAmelCase__					)
							lowerCAmelCase_      =     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]    ,    lowerCAmelCase__    ,    atol=4e-2					)					)





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




				def 					__a						( self					)    ->				Dict:
							super().setUp()
							lowerCAmelCase_      =     "facebook/opt-350m"




				def 					__a						( self					)    ->				List[Any]:
							lowerCAmelCase_      =     FlaxOPTForCausalLM.from_pretrained(self.path_model					)
							lowerCAmelCase_      =     GPTaTokenizer.from_pretrained(self.path_model					)

							lowerCAmelCase_      =     [
							    "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
							lowerCAmelCase_      =     tokenizer(lowerCAmelCase__    ,    return_tensors="jax"    ,    padding=lowerCAmelCase__    ,    add_special_tokens=lowerCAmelCase__					)
							lowerCAmelCase_      =     model(inputs.input_ids    ,    attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
							lowerCAmelCase_      =     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(lowerCAmelCase__    ,    lowerCAmelCase__    ,    atol=4e-2					)					)

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





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




				@property
				def 					__a						( self					)    ->				List[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 					__a						( self					)    ->				Optional[int]:
							lowerCAmelCase_      =     "facebook/opt-125m"

							lowerCAmelCase_      =     [
							    "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",
							]

							lowerCAmelCase_      =     []

							lowerCAmelCase_      =     FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__					)
							lowerCAmelCase_      =     GPTaTokenizer.from_pretrained(lowerCAmelCase__					)

							for prompt in self.prompts:
										lowerCAmelCase_      =     tokenizer(lowerCAmelCase__    ,    return_tensors="jax"					).input_ids

										lowerCAmelCase_      =     model.generate(lowerCAmelCase__    ,    max_length=10					)
										lowerCAmelCase_      =     generated_ids[0]

										lowerCAmelCase_      =     tokenizer.batch_decode(lowerCAmelCase__    ,    skip_special_tokens=lowerCAmelCase__					)
										predicted_outputs += generated_string

							self.assertListEqual(lowerCAmelCase__    ,    lowerCAmelCase__					)




				def 					__a						( self					)    ->				int:
							lowerCAmelCase_      =     "facebook/opt-350m"

							lowerCAmelCase_      =     [
							    "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",
							]

							lowerCAmelCase_      =     []
							lowerCAmelCase_      =     FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__					)
							lowerCAmelCase_      =     GPTaTokenizer.from_pretrained(lowerCAmelCase__					)

							for prompt in self.prompts:
										lowerCAmelCase_      =     tokenizer(lowerCAmelCase__    ,    return_tensors="jax"					).input_ids

										lowerCAmelCase_      =     model.generate(lowerCAmelCase__    ,    max_length=10					)
										lowerCAmelCase_      =     generated_ids[0]

										lowerCAmelCase_      =     tokenizer.batch_decode(lowerCAmelCase__    ,    skip_special_tokens=lowerCAmelCase__					)
										predicted_outputs += generated_string

							self.assertListEqual(lowerCAmelCase__    ,    lowerCAmelCase__					)




				def 					__a						( self					)    ->				List[str]:
							lowerCAmelCase_      =     "facebook/opt-125m"
							lowerCAmelCase_      =     [
							    "Today is a beautiful day and I want to thank",
							    "In the city of Rome Canaver Canaver Canaver Canaver",
							]
							lowerCAmelCase_      =     FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__					)
							lowerCAmelCase_      =     GPTaTokenizer.from_pretrained(lowerCAmelCase__					)
							lowerCAmelCase_      =     tokenizer(
							    [
							        "Today is a beautiful day and I want to",
							        "In the city of",
							    ]    ,    return_tensors="jax"    ,    padding=lowerCAmelCase__    ,    )

							lowerCAmelCase_      =     jax.jit(model.generate					)

							lowerCAmelCase_      =     jit_generate(inputs["input_ids"]    ,    attention_mask=inputs["attention_mask"]					).sequences

							lowerCAmelCase_      =     tokenizer.batch_decode(lowerCAmelCase__    ,    skip_special_tokens=lowerCAmelCase__					)

							self.assertIsNotNone(lowerCAmelCase__    ,    lowerCAmelCase__					)




				def 					__a						( self					)    ->				Dict:
							lowerCAmelCase_      =     "facebook/opt-350m"

							lowerCAmelCase_      =     GPTaTokenizer.from_pretrained(lowerCAmelCase__					)
							lowerCAmelCase_      =     FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__					)

							lowerCAmelCase_      =     "left"

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

							lowerCAmelCase_      =     tokenizer(lowerCAmelCase__    ,    return_tensors="jax"    ,    padding=lowerCAmelCase__					)
							lowerCAmelCase_      =     inputs["input_ids"]

							lowerCAmelCase_      =     model.generate(input_ids=lowerCAmelCase__    ,    attention_mask=inputs["attention_mask"]    ,    trace=lowerCAmelCase__					)

							lowerCAmelCase_      =     tokenizer(sentences[0]    ,    return_tensors="jax"					).input_ids
							lowerCAmelCase_      =     model.generate(input_ids=lowerCAmelCase__					)

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

							lowerCAmelCase_      =     tokenizer.batch_decode(outputs[0]    ,    skip_special_tokens=lowerCAmelCase__					)
							lowerCAmelCase_      =     tokenizer.decode(output_non_padded[0][0]    ,    skip_special_tokens=lowerCAmelCase__					)
							lowerCAmelCase_      =     tokenizer.decode(output_padded[0][0]    ,    skip_special_tokens=lowerCAmelCase__					)

							lowerCAmelCase_      =     [
							    "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(lowerCAmelCase__    ,    lowerCAmelCase__					)
							self.assertListEqual(lowerCAmelCase__    ,    [non_padded_sentence, padded_sentence]					)





from typing import TYPE_CHECKING

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


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

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "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

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



