






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 :    List[Any]								=				"platform"

  import jax
  import jax.numpy as jnp

  from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Union[str, Any]     ,			__lowercase				:     Any     ,			__lowercase				:     Union[str, Any]=None     ,			__lowercase				:     Optional[Any]=None      )  -> Any:





    """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  __lowercase :







     '''simple docstring'''




     def __init__(			self    :     Optional[Any]	,		UpperCamelCase_    :     Union[str, Any]	,		UpperCamelCase_    :     Dict=13	,		UpperCamelCase_    :     List[Any]=7	,		UpperCamelCase_    :     List[str]=True	,		UpperCamelCase_    :     Any=False	,		UpperCamelCase_    :     Dict=99	,		UpperCamelCase_    :     Union[str, Any]=16	,		UpperCamelCase_    :     Dict=2	,		UpperCamelCase_    :     Dict=4	,		UpperCamelCase_    :     str=4	,		UpperCamelCase_    :     Optional[Any]="gelu"	,		UpperCamelCase_    :     List[str]=0.1	,		UpperCamelCase_    :     Dict=0.1	,		UpperCamelCase_    :     str=20	,		UpperCamelCase_    :     Tuple=2	,		UpperCamelCase_    :     Union[str, Any]=1	,		UpperCamelCase_    :     List[Any]=0	,		UpperCamelCase_    :     List[Any]=16	,		UpperCamelCase_    :     List[Any]=16	,		UpperCamelCase_    :     List[Any]=0.02	,		):


         """simple docstring"""



         __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 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



         __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=UpperCamelCase_	,		word_embed_proj_dim=self.word_embed_proj_dim	,		initializer_range=self.initializer_range	,		use_cache=UpperCamelCase_	,		)
         __A             =		prepare_opt_inputs_dict(UpperCamelCase_	,		UpperCamelCase_			)
         return config, inputs_dict




     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __A      ,		__A             =		self.prepare_config_and_inputs()
         return config, inputs_dict




     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     str	,		UpperCamelCase_    :     Dict	,		UpperCamelCase_    :     List[Any]			):


         """simple docstring"""



         __A             =		20
         __A             =		model_class_name(UpperCamelCase_			)

         __A             =		inputs_dict["""input_ids"""]
         __A             =		inputs_dict["""attention_mask"""]

         __A             =		model.init_cache(input_ids.shape[0]	,		UpperCamelCase_			)
         __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=UpperCamelCase_	,		past_key_values=UpperCamelCase_	,		position_ids=UpperCamelCase_	,		)

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

         __A             =		model(UpperCamelCase_			)

         __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 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     Any	,		UpperCamelCase_    :     Any	,		UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



         __A             =		20
         __A             =		model_class_name(UpperCamelCase_			)

         __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]	,		UpperCamelCase_			)
         __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=UpperCamelCase_	,		past_key_values=UpperCamelCase_	,		position_ids=UpperCamelCase_	,		)
         __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=UpperCamelCase_	,		position_ids=UpperCamelCase_	,		)

         __A             =		model(UpperCamelCase_	,		attention_mask=UpperCamelCase_			)

         __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  __lowercase (		lowercase_     , unittest.TestCase     , lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
     SCREAMING_SNAKE_CASE				  =			() if is_flax_available() else ()




     def 						lowerCAmelCase_       (			self    :     str			):


         """simple docstring"""



         __A             =		FlaxOPTModelTester(self			)




     def 						lowerCAmelCase_       (			self    :     Dict			):


         """simple docstring"""



         __A      ,		__A             =		self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


         """simple docstring"""



         __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(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_			)





     @slow
     def 						lowerCAmelCase_       (			self    :     Any			):


         """simple docstring"""



         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(UpperCamelCase_			)
             self.assertIsNotNone(UpperCamelCase_			)


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







     '''simple docstring'''




     @slow
     def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


         """simple docstring"""



         __A             =		FlaxOPTModel.from_pretrained("""facebook/opt-350m"""			)
         __A             =		jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]			)
         __A             =		model(input_ids=UpperCamelCase_			).last_hidden_state
         __A             =		(1, 11, 512)
         self.assertEqual(output.shape	,		UpperCamelCase_			)
         __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]	,		UpperCamelCase_	,		atol=4e-2			)			)


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







     '''simple docstring'''




     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         super().setUp()
         __A             =		"""facebook/opt-350m"""





     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         __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(UpperCamelCase_	,		return_tensors="""jax"""	,		padding=UpperCamelCase_	,		add_special_tokens=UpperCamelCase_			)
         __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(UpperCamelCase_	,		UpperCamelCase_	,		atol=4e-2			)			)

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


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







     '''simple docstring'''




     @property
     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """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 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         __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(UpperCamelCase_			)
         __A             =		GPTaTokenizer.from_pretrained(UpperCamelCase_			)

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

             __A             =		model.generate(UpperCamelCase_	,		max_length=10			)
             __A             =		generated_ids[0]

             __A             =		tokenizer.batch_decode(UpperCamelCase_	,		skip_special_tokens=UpperCamelCase_			)
             predicted_outputs += generated_string

         self.assertListEqual(UpperCamelCase_	,		UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


         """simple docstring"""



         __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(UpperCamelCase_			)
         __A             =		GPTaTokenizer.from_pretrained(UpperCamelCase_			)

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

             __A             =		model.generate(UpperCamelCase_	,		max_length=10			)
             __A             =		generated_ids[0]

             __A             =		tokenizer.batch_decode(UpperCamelCase_	,		skip_special_tokens=UpperCamelCase_			)
             predicted_outputs += generated_string

         self.assertListEqual(UpperCamelCase_	,		UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         __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(UpperCamelCase_			)
         __A             =		GPTaTokenizer.from_pretrained(UpperCamelCase_			)
         __A             =		tokenizer(
             [
                 """Today is a beautiful day and I want to""",
                 """In the city of""",
             ]	,		return_tensors="""jax"""	,		padding=UpperCamelCase_	,		)

         __A             =		jax.jit(model.generate			)

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

         __A             =		tokenizer.batch_decode(UpperCamelCase_	,		skip_special_tokens=UpperCamelCase_			)

         self.assertIsNotNone(UpperCamelCase_	,		UpperCamelCase_			)





     def 						lowerCAmelCase_       (			self    :     Dict			):


         """simple docstring"""



         __A             =		"""facebook/opt-350m"""

         __A             =		GPTaTokenizer.from_pretrained(UpperCamelCase_			)
         __A             =		FlaxOPTForCausalLM.from_pretrained(UpperCamelCase_			)

         __A             =		"""left"""

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

         __A             =		tokenizer(UpperCamelCase_	,		return_tensors="""jax"""	,		padding=UpperCamelCase_			)
         __A             =		inputs["""input_ids"""]

         __A             =		model.generate(input_ids=UpperCamelCase_	,		attention_mask=inputs["""attention_mask"""]	,		trace=UpperCamelCase_			)

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

         __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=UpperCamelCase_	,		max_length=model.config.max_length - num_paddings			)

         __A             =		tokenizer.batch_decode(outputs[0]	,		skip_special_tokens=UpperCamelCase_			)
         __A             =		tokenizer.decode(output_non_padded[0][0]	,		skip_special_tokens=UpperCamelCase_			)
         __A             =		tokenizer.decode(output_padded[0][0]	,		skip_special_tokens=UpperCamelCase_			)

         __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(UpperCamelCase_	,		UpperCamelCase_			)
         self.assertListEqual(UpperCamelCase_	,		[non_padded_sentence, padded_sentence]			)












import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


__a :    Tuple								=				logging.get_logger(__name__)


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"hybrid-clip"
     SCREAMING_SNAKE_CASE				  =			True




     def __init__(			self    :     Optional[Any]	,		UpperCamelCase_    :     Optional[Any]=512	,		**UpperCamelCase_    :     List[Any]			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)

         if "text_config" not in kwargs:
             raise ValueError("""`text_config` can not be `None`."""			)

         if "vision_config" not in kwargs:
             raise ValueError("""`vision_config` can not be `None`."""			)

         __A             =		kwargs.pop("""text_config"""			)
         __A             =		kwargs.pop("""vision_config"""			)

         __A             =		text_config.pop("""model_type"""			)
         __A             =		vision_config.pop("""model_type"""			)

         from transformers import AutoConfig

         __A             =		AutoConfig.for_model(UpperCamelCase_	,		**UpperCamelCase_			)

         if vision_model_type == "clip":
             __A             =		AutoConfig.for_model(UpperCamelCase_	,		**UpperCamelCase_			).vision_config
         elif vision_model_type == "clip_vision_model":
             from transformers import CLIPVisionConfig

             __A             =		CLIPVisionConfig(**UpperCamelCase_			)
         else:
             __A             =		AutoConfig.for_model(UpperCamelCase_	,		**UpperCamelCase_			)

         __A             =		projection_dim
         __A             =		1.0




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     int	,		UpperCamelCase_    :     PretrainedConfig	,		UpperCamelCase_    :     PretrainedConfig	,		**UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



         return cls(text_config=text_config.to_dict()	,		vision_config=vision_config.to_dict()	,		**UpperCamelCase_			)





     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         __A             =		copy.deepcopy(self.__dict__			)
         __A             =		self.text_config.to_dict()
         __A             =		self.vision_config.to_dict()
         __A             =		self.__class__.model_type
         return output




