


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__						=			'platform'

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def 							_UpperCAmelCase    (UpperCamelCase__					:  Tuple    ,							UpperCamelCase__					:  Optional[int]    ,							UpperCamelCase__					:  Dict=None    ,							UpperCamelCase__					:  List[Any]=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__	:




     '''simple docstring'''


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


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

            _A				:	Union[str, 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=__lowerCamelCase			,						word_embed_proj_dim=self.word_embed_proj_dim			,						initializer_range=self.initializer_range			,						use_cache=__lowerCamelCase			,						)
            _A				:	Tuple       =			prepare_opt_inputs_dict(__lowerCamelCase			,						__lowerCamelCase)
            return config, inputs_dict


     def 			_lowerCamelCase			(						self)      ->    int:
            _A   ,      _A				:	int       =			self.prepare_config_and_inputs()
            return config, inputs_dict


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    int:
            _A				:	Dict       =			2_0
            _A				:	Dict       =			model_class_name(__lowerCamelCase)

            _A				:	Tuple       =			inputs_dict["input_ids"]
            _A				:	Optional[Any]       =			inputs_dict["attention_mask"]

            _A				:	Dict       =			model.init_cache(input_ids.shape[0]			,						__lowerCamelCase)
            _A				:	Union[str, Any]       =			jnp.ones((input_ids.shape[0], max_length)			,						dtype="i4")

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

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

            _A				:	Union[str, Any]       =			model(__lowerCamelCase)

            _A				:	Optional[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			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Optional[int]:
            _A				:	str       =			2_0
            _A				:	Optional[Any]       =			model_class_name(__lowerCamelCase)

            _A   ,      _A				:	List[Any]       =			(
                inputs_dict["input_ids"],
                inputs_dict["attention_mask"],
            )

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

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

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

            _A				:	List[str]       =			model(__lowerCamelCase			,						attention_mask=__lowerCamelCase)

            _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):




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


     def 			_lowerCamelCase			(						self)      ->    List[str]:
            _A				:	Union[str, Any]       =			FlaxOPTModelTester(self)


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







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




     '''simple docstring'''


     @slow
     def 			_lowerCamelCase			(						self)      ->    List[Any]:
            _A				:	Dict       =			FlaxOPTModel.from_pretrained("facebook/opt-350m")
            _A				:	Optional[int]       =			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				:	Optional[Any]       =			model(input_ids=__lowerCamelCase).last_hidden_state
            _A				:	Union[str, Any]       =			(1, 1_1, 5_1_2)
            self.assertEqual(output.shape			,						__lowerCamelCase)
            _A				:	Union[str, Any]       =			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]			,						__lowerCamelCase			,						atol=4e-2))







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




     '''simple docstring'''


     def 			_lowerCamelCase			(						self)      ->    Tuple:
            super().setUp()
            _A				:	Any       =			"facebook/opt-350m"


     def 			_lowerCamelCase			(						self)      ->    Dict:
            _A				:	int       =			FlaxOPTForCausalLM.from_pretrained(self.path_model)
            _A				:	Any       =			GPTaTokenizer.from_pretrained(self.path_model)

            _A				:	List[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				:	Tuple       =			tokenizer(__lowerCamelCase			,						return_tensors="jax"			,						padding=__lowerCamelCase			,						add_special_tokens=__lowerCamelCase)
            _A				:	Tuple       =			model(inputs.input_ids			,						attention_mask=inputs.attention_mask)[0].mean(axis=-1)
            _A				:	Optional[Any]       =			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(__lowerCamelCase			,						__lowerCamelCase			,						atol=4e-2))

            _A				:	List[str]       =			jax.jit(__lowerCamelCase)
            _A				:	List[Any]       =			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):




     '''simple docstring'''


     @property
     def 			_lowerCamelCase			(						self)      ->    str:
            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)      ->    str:
            _A				:	Union[str, Any]       =			"facebook/opt-125m"

            _A				:	Optional[Any]       =			[
                "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				:	Dict       =			[]

            _A				:	str       =			FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase)
            _A				:	Tuple       =			GPTaTokenizer.from_pretrained(__lowerCamelCase)

            for prompt in self.prompts:
                   _A				:	List[Any]       =			tokenizer(__lowerCamelCase			,						return_tensors="jax").input_ids

                   _A				:	Dict       =			model.generate(__lowerCamelCase			,						max_length=1_0)
                   _A				:	List[Any]       =			generated_ids[0]

                   _A				:	List[Any]       =			tokenizer.batch_decode(__lowerCamelCase			,						skip_special_tokens=__lowerCamelCase)
                   predicted_outputs += generated_string

            self.assertListEqual(__lowerCamelCase			,						__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    List[str]:
            _A				:	Dict       =			"facebook/opt-350m"

            _A				:	Optional[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				:	Union[str, Any]       =			[]
            _A				:	Optional[int]       =			FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase)
            _A				:	List[str]       =			GPTaTokenizer.from_pretrained(__lowerCamelCase)

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

                   _A				:	Union[str, Any]       =			model.generate(__lowerCamelCase			,						max_length=1_0)
                   _A				:	Optional[Any]       =			generated_ids[0]

                   _A				:	List[str]       =			tokenizer.batch_decode(__lowerCamelCase			,						skip_special_tokens=__lowerCamelCase)
                   predicted_outputs += generated_string

            self.assertListEqual(__lowerCamelCase			,						__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Tuple:
            _A				:	Optional[int]       =			"facebook/opt-125m"
            _A				:	Dict       =			[
                "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(__lowerCamelCase)
            _A				:	Optional[Any]       =			GPTaTokenizer.from_pretrained(__lowerCamelCase)
            _A				:	Dict       =			tokenizer(
                [
                    "Today is a beautiful day and I want to",
                    "In the city of",
                ]			,						return_tensors="jax"			,						padding=__lowerCamelCase			,						)

            _A				:	Optional[Any]       =			jax.jit(model.generate)

            _A				:	Any       =			jit_generate(inputs["input_ids"]			,						attention_mask=inputs["attention_mask"]).sequences

            _A				:	Optional[Any]       =			tokenizer.batch_decode(__lowerCamelCase			,						skip_special_tokens=__lowerCamelCase)

            self.assertIsNotNone(__lowerCamelCase			,						__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    str:
            _A				:	Optional[int]       =			"facebook/opt-350m"

            _A				:	int       =			GPTaTokenizer.from_pretrained(__lowerCamelCase)
            _A				:	Tuple       =			FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase)

            _A				:	Dict       =			"left"

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

            _A				:	List[Any]       =			tokenizer(__lowerCamelCase			,						return_tensors="jax"			,						padding=__lowerCamelCase)
            _A				:	Optional[Any]       =			inputs["input_ids"]

            _A				:	Any       =			model.generate(input_ids=__lowerCamelCase			,						attention_mask=inputs["attention_mask"]			,						trace=__lowerCamelCase)

            _A				:	Tuple       =			tokenizer(sentences[0]			,						return_tensors="jax").input_ids
            _A				:	List[str]       =			model.generate(input_ids=__lowerCamelCase)

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

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

            _A				:	Union[str, Any]       =			[
                "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 __future__ import annotations

from typing import Any







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    None:  # noqa: A002
            _A				:	List[str]       =			item
            _A				:	List[Any]       =			next







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self)      ->    None:
            _A				:	Node | None       =			None
            _A				:	List[Any]       =			0


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    None:
            _A				:	Union[str, Any]       =			Node(__lowerCamelCase			,						self.head)
            self.size += 1


     def 			_lowerCamelCase			(						self)      ->    Any:
            # Switched 'self.is_empty()' to 'self.head is None'
            # because mypy was considering the possibility that 'self.head'
            # can be None in below else part and giving error
            if self.head is None:
                   return None
            else:
                   _A				:	str       =			self.head.item
                   _A				:	List[Any]       =			self.head.next
                   self.size -= 1
                   return item


     def 			_lowerCamelCase			(						self)      ->    bool:
            return self.head is None


     def __str__(						self)      ->    str:
            if self.is_empty():
                   return ""
            else:
                   _A				:	List[str]       =			self.head
                   _A				:	Optional[int]       =			""
                   _A				:	list[str]       =			[]
                   while iterate:
                          item_list.append(str(iterate.item))
                          _A				:	Optional[Any]       =			iterate.next

                   _A				:	List[str]       =			" --> ".join(__lowerCamelCase)

                   return item_str


     def __len__(						self)      ->    int:
            return self.size
