"""simple docstring"""


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   ,    __lowerCAmelCase   ,    __lowerCAmelCase=None   ,    __lowerCAmelCase=None				)							->					Optional[Any]:




						'''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    __UpperCAmelCase						:

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase=13	, _lowerCamelCase=7	, _lowerCamelCase=True	, _lowerCamelCase=False	, _lowerCamelCase=99	, _lowerCamelCase=16	, _lowerCamelCase=2	, _lowerCamelCase=4	, _lowerCamelCase=4	, _lowerCamelCase="gelu"	, _lowerCamelCase=0.1	, _lowerCamelCase=0.1	, _lowerCamelCase=20	, _lowerCamelCase=2	, _lowerCamelCase=1	, _lowerCamelCase=0	, _lowerCamelCase=16	, _lowerCamelCase=16	, _lowerCamelCase=0.0_2	, ):
										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      ):
										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      ):
										lowerCamelCase__					,      lowerCamelCase__					=self.prepare_config_and_inputs()
										return config, inputs_dict

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ):
										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	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ):
										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    __UpperCAmelCase						(	__lowerCAmelCase      ,      unittest.TestCase      ,      __lowerCAmelCase	):
				A__      :   List[Any]  							=       (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
				A__      :   Union[str, Any]  							=       () if is_flax_available() else ()

				def 		_a	(      self      ):
										lowerCamelCase__					=FlaxOPTModelTester(self      )

				def 		_a	(      self      ):
										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      ):
										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      ):
										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    __UpperCAmelCase						(	unittest.TestCase	):

				@slow
				def 		_a	(      self      ):
										lowerCamelCase__					=FlaxOPTModel.from_pretrained("facebook/opt-350m"      )
										lowerCamelCase__					=jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]      )
										lowerCamelCase__					=model(input_ids=_lowerCamelCase      ).last_hidden_state
										lowerCamelCase__					=(1, 11, 512)
										self.assertEqual(output.shape	, _lowerCamelCase      )
										lowerCamelCase__					=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    __UpperCAmelCase						(	unittest.TestCase	):

				def 		_a	(      self      ):
										super().setUp()
										lowerCamelCase__					="facebook/opt-350m"






				def 		_a	(      self      ):
										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(
										    [
										        [1.3_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      )      )

										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    __UpperCAmelCase						(	unittest.TestCase	):

				@property
				def 		_a	(      self      ):
										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      ):
										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      ):
										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      ):
										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      ):
										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 __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any
class       __a					:


     """simple docstring"""


     _A				:    List[Any]									=  ("_front", "_back", "_len")
     @dataclass
     class       __a					:


          """simple docstring"""


          _A				:    Any									=  None
          _A				:    Deque._Node | None									=  None
          _A				:    Deque._Node | None									=  None






     class       __a					:


          """simple docstring"""


          _A				:    List[str]									=  ("_cur",)




          def __init__(	self			: int				,_UpperCamelCase			: Deque._Node | None						)		->   None:


              '''simple docstring'''





              SCREAMING_SNAKE_CASE__								=cur




          def __iter__(	self			: Optional[Any]						)		->   Deque._Iterator:


              '''simple docstring'''





              return self




          def   __A				(	self			: Union[str, Any]						)		->   Any:


              '''simple docstring'''





              if self._cur is None:
                  # finished iterating
                  raise StopIteration
              SCREAMING_SNAKE_CASE__								=self._cur.val
              SCREAMING_SNAKE_CASE__								=self._cur.next_node

              return val






     def __init__(	self			: Union[str, Any]				,_UpperCamelCase			: Iterable[Any] | None = None						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=0

         if iterable is not None:
             # append every value to the deque
             for val in iterable:
                 self.append(_UpperCamelCase						)




     def   __A				(	self			: Optional[Any]				,_UpperCamelCase			: Any						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self._Node(_UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase						)
         if self.is_empty():
             # front = back
             SCREAMING_SNAKE_CASE__								=SCREAMING_SNAKE_CASE__								=node
             SCREAMING_SNAKE_CASE__								=1
         else:
             # connect nodes
             SCREAMING_SNAKE_CASE__								=node
             SCREAMING_SNAKE_CASE__								=self._back
             SCREAMING_SNAKE_CASE__								=node  # assign new back to the new node

             self._len += 1

             # make sure there were no errors
             assert not self.is_empty(), "Error on appending value."




     def   __A				(	self			: Any				,_UpperCamelCase			: Any						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self._Node(_UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase						)
         if self.is_empty():
             # front = back
             SCREAMING_SNAKE_CASE__								=SCREAMING_SNAKE_CASE__								=node
             SCREAMING_SNAKE_CASE__								=1
         else:
             # connect nodes
             SCREAMING_SNAKE_CASE__								=self._front
             SCREAMING_SNAKE_CASE__								=node
             SCREAMING_SNAKE_CASE__								=node  # assign new front to the new node

             self._len += 1

             # make sure there were no errors
             assert not self.is_empty(), "Error on appending value."




     def   __A				(	self			: List[Any]				,_UpperCamelCase			: Iterable[Any]						)		->   None:


         '''simple docstring'''





         for val in iterable:
             self.append(_UpperCamelCase						)




     def   __A				(	self			: str				,_UpperCamelCase			: Iterable[Any]						)		->   None:


         '''simple docstring'''





         for val in iterable:
             self.appendleft(_UpperCamelCase						)




     def   __A				(	self			: str						)		->   Any:


         '''simple docstring'''





         assert not self.is_empty(), "Deque is empty."

         SCREAMING_SNAKE_CASE__								=self._back
         SCREAMING_SNAKE_CASE__								=self._back.prev_node  # set new back
         # drop the last node - python will deallocate memory automatically
         SCREAMING_SNAKE_CASE__								=None

         self._len -= 1

         return topop.val




     def   __A				(	self			: Union[str, Any]						)		->   Any:


         '''simple docstring'''





         assert not self.is_empty(), "Deque is empty."

         SCREAMING_SNAKE_CASE__								=self._front
         SCREAMING_SNAKE_CASE__								=self._front.next_node  # set new front and drop the first node
         SCREAMING_SNAKE_CASE__								=None

         self._len -= 1

         return topop.val




     def   __A				(	self			: Tuple						)		->   bool:


         '''simple docstring'''





         return self._front is None




     def __len__(	self			: Optional[int]						)		->   int:


         '''simple docstring'''





         return self._len




     def __eq__(	self			: Union[str, Any]				,_UpperCamelCase			: object						)		->   bool:


         '''simple docstring'''





         if not isinstance(_UpperCamelCase				,_UpperCamelCase						):
             return NotImplemented

         SCREAMING_SNAKE_CASE__								=self._front
         SCREAMING_SNAKE_CASE__								=other._front

         # if the length of the dequeues are not the same, they are not equal
         if len(self						) != len(_UpperCamelCase						):
             return False

         while me is not None and oth is not None:
             # compare every value
             if me.val != oth.val:
                 return False
             SCREAMING_SNAKE_CASE__								=me.next_node
             SCREAMING_SNAKE_CASE__								=oth.next_node

         return True




     def __iter__(	self			: Optional[Any]						)		->   Deque._Iterator:


         '''simple docstring'''





         return Deque._Iterator(self._front						)




     def __repr__(	self			: Optional[Any]						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=[]
         SCREAMING_SNAKE_CASE__								=self._front
         while aux is not None:
             # append the values in a list to display
             values_list.append(aux.val						)
             SCREAMING_SNAKE_CASE__								=aux.next_node

         return f"""[{", ".join(repr(_UpperCamelCase						) for val in values_list						)}]"""


if __name__ == "__main__":
    import doctest

    doctest.testmod()






