




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
		__lowerCAmelCase		:    Dict	      =		'platform'

		import jax
		import jax.numpy as jnp

		from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def       __magic_name__    (			A		: List[str],  A		: Optional[int],  A		: Optional[Any]=None,  A		: Union[str, Any]=None       ):






			'''simple docstring'''







			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    snake_case__							:
				"""simple docstring"""



				def __init__(				self    :					str ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Tuple=13 ,				__lowerCamelCase    :					str=7 ,				__lowerCamelCase    :					Optional[Any]=True ,				__lowerCamelCase    :					List[str]=False ,				__lowerCamelCase    :					List[Any]=99 ,				__lowerCamelCase    :					Any=16 ,				__lowerCamelCase    :					Union[str, Any]=2 ,				__lowerCamelCase    :					Tuple=4 ,				__lowerCamelCase    :					List[Any]=4 ,				__lowerCamelCase    :					int="gelu" ,				__lowerCamelCase    :					Optional[Any]=0.1 ,				__lowerCamelCase    :					Dict=0.1 ,				__lowerCamelCase    :					List[Any]=20 ,				__lowerCamelCase    :					Optional[int]=2 ,				__lowerCamelCase    :					List[str]=1 ,				__lowerCamelCase    :					int=0 ,				__lowerCamelCase    :					Optional[int]=16 ,				__lowerCamelCase    :					Any=16 ,				__lowerCamelCase    :					Union[str, Any]=0.02 ,				) ->					Optional[Any]:
							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 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Optional[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=__lowerCamelCase ,				word_embed_proj_dim=self.word_embed_proj_dim ,				initializer_range=self.initializer_range ,				use_cache=__lowerCamelCase ,				)
							a   =      prepare_opt_inputs_dict(__lowerCamelCase ,				__lowerCamelCase							)
							return config, inputs_dict





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





				def 							__UpperCAmelCase							(				self    :					Tuple ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					List[str]							) ->					List[str]:
							a   =      20
							a   =      model_class_name(__lowerCamelCase							)

							a   =      inputs_dict["input_ids"]
							a   =      inputs_dict["attention_mask"]

							a   =      model.init_cache(input_ids.shape[0] ,				__lowerCamelCase							)
							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=__lowerCamelCase ,				past_key_values=__lowerCamelCase ,				position_ids=__lowerCamelCase ,				)

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

							a   =      model(__lowerCamelCase							)

							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 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					str							) ->					Union[str, Any]:
							a   =      20
							a   =      model_class_name(__lowerCamelCase							)

							a   ,		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] ,				__lowerCamelCase							)
							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=__lowerCamelCase ,				past_key_values=__lowerCamelCase ,				position_ids=__lowerCamelCase ,				)
							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=__lowerCamelCase ,				position_ids=__lowerCamelCase ,				)

							a   =      model(__lowerCamelCase ,				attention_mask=__lowerCamelCase							)

							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    snake_case__							(_UpperCamelCase   ,	unittest.TestCase   ,	_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
				SCREAMING_SNAKE_CASE_	:							int										= () if is_flax_available() else ()





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Union[str, Any]:
							a   =      FlaxOPTModelTester(self							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Optional[Any]:
							a   ,		a   =      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 							__UpperCAmelCase							(				self    :					Any							) ->					Union[str, Any]:
							a   ,		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(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(				self    :					int							) ->					str:
							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(__lowerCamelCase							)
										self.assertIsNotNone(__lowerCamelCase							)




@require_sentencepiece
@require_flax
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Union[str, Any]:
							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=__lowerCamelCase							).last_hidden_state
							a   =      (1, 11, 5_12)
							self.assertEqual(output.shape ,				__lowerCamelCase							)
							a   =      jnp.array(
							    [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]							)
							self.assertTrue(jnp.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=4e-2							)							)




@require_flax
@slow
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Tuple:
							super().setUp()
							a   =      "facebook/opt-350m"





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					List[str]:
							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(__lowerCamelCase ,				return_tensors="jax" ,				padding=__lowerCamelCase ,				add_special_tokens=__lowerCamelCase							)
							a   =      model(inputs.input_ids ,				attention_mask=inputs.attention_mask							)[0].mean(axis=-1							)
							a   =      jnp.array(
							    [
							        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
							        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
							        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
							        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
							    ]							)
							self.assertTrue(jnp.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=4e-2							)							)

							a   =      jax.jit(__lowerCamelCase							)
							a   =      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    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					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 							__UpperCAmelCase							(				self    :					Union[str, 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(__lowerCamelCase							)
							a   =      GPTaTokenizer.from_pretrained(__lowerCamelCase							)

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

										a   =      model.generate(__lowerCamelCase ,				max_length=10							)
										a   =      generated_ids[0]

										a   =      tokenizer.batch_decode(__lowerCamelCase ,				skip_special_tokens=__lowerCamelCase							)
										predicted_outputs += generated_string

							self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					int							) ->					Dict:
							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(__lowerCamelCase							)
							a   =      GPTaTokenizer.from_pretrained(__lowerCamelCase							)

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

										a   =      model.generate(__lowerCamelCase ,				max_length=10							)
										a   =      generated_ids[0]

										a   =      tokenizer.batch_decode(__lowerCamelCase ,				skip_special_tokens=__lowerCamelCase							)
										predicted_outputs += generated_string

							self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Any:
							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(__lowerCamelCase							)
							a   =      GPTaTokenizer.from_pretrained(__lowerCamelCase							)
							a   =      tokenizer(
							    [
							        "Today is a beautiful day and I want to",
							        "In the city of",
							    ] ,				return_tensors="jax" ,				padding=__lowerCamelCase ,				)

							a   =      jax.jit(model.generate							)

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

							a   =      tokenizer.batch_decode(__lowerCamelCase ,				skip_special_tokens=__lowerCamelCase							)

							self.assertIsNotNone(__lowerCamelCase ,				__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					int							) ->					List[Any]:
							a   =      "facebook/opt-350m"

							a   =      GPTaTokenizer.from_pretrained(__lowerCamelCase							)
							a   =      FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase							)

							a   =      "left"

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

							a   =      tokenizer(__lowerCamelCase ,				return_tensors="jax" ,				padding=__lowerCamelCase							)
							a   =      inputs["input_ids"]

							a   =      model.generate(input_ids=__lowerCamelCase ,				attention_mask=inputs["attention_mask"] ,				trace=__lowerCamelCase							)

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

							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=__lowerCamelCase ,				max_length=model.config.max_length - num_paddings							)

							a   =      tokenizer.batch_decode(outputs[0] ,				skip_special_tokens=__lowerCamelCase							)
							a   =      tokenizer.decode(output_non_padded[0][0] ,				skip_special_tokens=__lowerCamelCase							)
							a   =      tokenizer.decode(output_padded[0][0] ,				skip_special_tokens=__lowerCamelCase							)

							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(__lowerCamelCase ,				__lowerCamelCase							)
							self.assertListEqual(__lowerCamelCase ,				[non_padded_sentence, padded_sentence]							)












from ..utils import DummyObject, requires_backends




class    snake_case__							(metaclass=_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= ["""torch""", """transformers""", """k_diffusion"""]





				def __init__(				self    :					Optional[int] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					List[Any]							) ->					Optional[Any]:
							requires_backends(self ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					str ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Tuple							) ->					List[Any]:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					Optional[Any] ,				*__lowerCamelCase    :					List[str] ,				**__lowerCamelCase    :					int							) ->					Dict:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)






