

'''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
							_lowerCamelCase    =  'platform'

							import jax
							import jax.numpy as jnp

							from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_=None							,       snake_case_=None	):
	if attention_mask is None:
		_lowercase							=     np.where(input_ids != config.pad_token_id							,       1							,       0	)
	return {
	    "input_ids": input_ids,
	    "attention_mask": attention_mask,
	}
@require_flax
class 					__a   :

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

							"""simple docstring"""



							_lowercase							=     parent
							_lowercase							=     batch_size
							_lowercase							=     seq_length
							_lowercase							=     is_training
							_lowercase							=     use_labels
							_lowercase							=     vocab_size
							_lowercase							=     hidden_size
							_lowercase							=     num_hidden_layers
							_lowercase							=     num_attention_heads
							_lowercase							=     intermediate_size
							_lowercase							=     hidden_act
							_lowercase							=     hidden_dropout_prob
							_lowercase							=     attention_probs_dropout_prob
							_lowercase							=     max_position_embeddings
							_lowercase							=     eos_token_id
							_lowercase							=     pad_token_id
							_lowercase							=     bos_token_id
							_lowercase							=     embed_dim
							_lowercase							=     word_embed_proj_dim
							_lowercase							=     initializer_range
							_lowercase							=     False

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

							"""simple docstring"""



							_lowercase							=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,			self.vocab_size)    ,			3    ,			self.vocab_size)
							_lowercase							=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,			dtype=np.intaa))    ,			-1)

							_lowercase							=     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=lowercase_    ,			word_embed_proj_dim=self.word_embed_proj_dim    ,			initializer_range=self.initializer_range    ,			use_cache=lowercase_    ,			)
							_lowercase							=     prepare_opt_inputs_dict(lowercase_    ,			lowercase_)
							return config, inputs_dict

						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->int:

							"""simple docstring"""



							_lowercase							=     self.prepare_config_and_inputs()
							return config, inputs_dict

						def 	_UpperCAmelCase							(		self	:						Dict    ,			lowercase__	:						Optional[Any]    ,			lowercase__	:						List[Any]    ,			lowercase__	:						Union[str, Any])						->List[str]:

							"""simple docstring"""



							_lowercase							=     20
							_lowercase							=     model_class_name(lowercase_)

							_lowercase							=     inputs_dict["input_ids"]
							_lowercase							=     inputs_dict["attention_mask"]

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

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

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

							_lowercase							=     model(lowercase_)

							_lowercase							=     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	:						Union[str, Any]    ,			lowercase__	:						List[str]    ,			lowercase__	:						str    ,			lowercase__	:						List[Any])						->Tuple:

							"""simple docstring"""



							_lowercase							=     20
							_lowercase							=     model_class_name(lowercase_)

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

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

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

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

							_lowercase							=     model(lowercase_    ,			attention_mask=lowercase_)

							_lowercase							=     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 					__a   (		lowercase__							,unittest.TestCase							,lowercase__  ):
						__SCREAMING_SNAKE_CASE     :					Dict  =      (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
						__SCREAMING_SNAKE_CASE     :					Optional[Any]  =      () if is_flax_available() else ()

						def 	_UpperCAmelCase							(		self	:						int)						->Any:

							"""simple docstring"""



							_lowercase							=     FlaxOPTModelTester(self)

						def 	_UpperCAmelCase							(		self	:						List[str])						->Any:

							"""simple docstring"""



							_lowercase							=     self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
								self.model_tester.check_use_cache_forward(lowercase_    ,			lowercase_    ,			lowercase_)

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

							"""simple docstring"""



							_lowercase							=     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(lowercase_    ,			lowercase_    ,			lowercase_)


						@slow
						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Optional[Any]:

							"""simple docstring"""



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






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

						@slow
						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->Union[str, Any]:

							"""simple docstring"""



							_lowercase							=     FlaxOPTModel.from_pretrained("""facebook/opt-350m""")
							_lowercase							=     jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
							_lowercase							=     model(input_ids=lowercase_).last_hidden_state
							_lowercase							=     (1, 11, 5_12)
							self.assertEqual(output.shape    ,			lowercase_)
							_lowercase							=     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]    ,			lowercase_    ,			atol=4e-2))






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

						def 	_UpperCAmelCase							(		self	:						Dict)						->Optional[int]:

							"""simple docstring"""



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


						def 	_UpperCAmelCase							(		self	:						List[str])						->str:

							"""simple docstring"""



							_lowercase							=     FlaxOPTForCausalLM.from_pretrained(self.path_model)
							_lowercase							=     GPTaTokenizer.from_pretrained(self.path_model)

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

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






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

						@property
						def 	_UpperCAmelCase							(		self	:						str)						->Union[str, Any]:

							"""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 	_UpperCAmelCase							(		self	:						Union[str, Any])						->Tuple:

							"""simple docstring"""



							_lowercase							=     "facebook/opt-125m"

							_lowercase							=     [
							    "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",
							]

							_lowercase							=     []

							_lowercase							=     FlaxOPTForCausalLM.from_pretrained(lowercase_)
							_lowercase							=     GPTaTokenizer.from_pretrained(lowercase_)

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

								_lowercase							=     model.generate(lowercase_    ,			max_length=10)
								_lowercase							=     generated_ids[0]

								_lowercase							=     tokenizer.batch_decode(lowercase_    ,			skip_special_tokens=lowercase_)
								predicted_outputs += generated_string

							self.assertListEqual(lowercase_    ,			lowercase_)

						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Dict:

							"""simple docstring"""



							_lowercase							=     "facebook/opt-350m"

							_lowercase							=     [
							    "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",
							]

							_lowercase							=     []
							_lowercase							=     FlaxOPTForCausalLM.from_pretrained(lowercase_)
							_lowercase							=     GPTaTokenizer.from_pretrained(lowercase_)

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

								_lowercase							=     model.generate(lowercase_    ,			max_length=10)
								_lowercase							=     generated_ids[0]

								_lowercase							=     tokenizer.batch_decode(lowercase_    ,			skip_special_tokens=lowercase_)
								predicted_outputs += generated_string

							self.assertListEqual(lowercase_    ,			lowercase_)

						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Union[str, Any]:

							"""simple docstring"""



							_lowercase							=     "facebook/opt-125m"
							_lowercase							=     [
							    "Today is a beautiful day and I want to thank",
							    "In the city of Rome Canaver Canaver Canaver Canaver",
							]
							_lowercase							=     FlaxOPTForCausalLM.from_pretrained(lowercase_)
							_lowercase							=     GPTaTokenizer.from_pretrained(lowercase_)
							_lowercase							=     tokenizer(
							    [
							        """Today is a beautiful day and I want to""",
							        """In the city of""",
							    ]    ,			return_tensors="""jax"""    ,			padding=lowercase_    ,			)

							_lowercase							=     jax.jit(model.generate)

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

							_lowercase							=     tokenizer.batch_decode(lowercase_    ,			skip_special_tokens=lowercase_)

							self.assertIsNotNone(lowercase_    ,			lowercase_)


						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Dict:

							"""simple docstring"""



							_lowercase							=     "facebook/opt-350m"

							_lowercase							=     GPTaTokenizer.from_pretrained(lowercase_)
							_lowercase							=     FlaxOPTForCausalLM.from_pretrained(lowercase_)

							_lowercase							=     "left"

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

							_lowercase							=     tokenizer(lowercase_    ,			return_tensors="""jax"""    ,			padding=lowercase_)
							_lowercase							=     inputs["input_ids"]

							_lowercase							=     model.generate(input_ids=lowercase_    ,			attention_mask=inputs["""attention_mask"""]    ,			trace=lowercase_)

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

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

							_lowercase							=     tokenizer.batch_decode(outputs[0]    ,			skip_special_tokens=lowercase_)
							_lowercase							=     tokenizer.decode(output_non_padded[0][0]    ,			skip_special_tokens=lowercase_)
							_lowercase							=     tokenizer.decode(output_padded[0][0]    ,			skip_special_tokens=lowercase_)

							_lowercase							=     [
							    "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(lowercase_    ,			lowercase_)
							self.assertListEqual(lowercase_    ,			[non_padded_sentence, padded_sentence])






"""simple docstring"""





class A_      :




  '''simple docstring'''







  def __init__(						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       data
     UpperCAmelCase_   :   str     	=       None
     UpperCAmelCase_   :   List[Any]     	=       None







  def __str__(						self    ):

     """simple docstring"""
     return F"""{self.data}"""




class A_      :




  '''simple docstring'''







  def __init__(						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       None
     UpperCAmelCase_   :   List[str]     	=       None







  def __iter__(						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       self.head
     while node:
        yield node.data
        UpperCAmelCase_   :   Optional[Any]     	=       node.next







  def __str__(						self    ):

     """simple docstring"""
     return "->".join([str(lowercase_    ) for item in self]    )







  def __len__(						self    ):

     """simple docstring"""
     return sum(1 for _ in self    )







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     self.insert_at_nth(0							,  lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     self.insert_at_nth(len(self    )							,  lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       len(self    )

     if not 0 <= index <= length:
        raise IndexError("list index out of range"    )
     UpperCAmelCase_   :   Tuple     	=       Node(lowercase_    )
     if self.head is None:
        UpperCAmelCase_   :   List[str]     	=       new_node
     elif index == 0:
        UpperCAmelCase_   :   int     	=       new_node
        UpperCAmelCase_   :   Optional[Any]     	=       self.head
        UpperCAmelCase_   :   Optional[int]     	=       new_node
     elif index == length:
        UpperCAmelCase_   :   Union[str, Any]     	=       new_node
        UpperCAmelCase_   :   Optional[Any]     	=       self.tail
        UpperCAmelCase_   :   Tuple     	=       new_node
     else:
        UpperCAmelCase_   :   Dict     	=       self.head
        for _ in range(0							,  lowercase_    ):
           UpperCAmelCase_   :   str     	=       temp.next
        UpperCAmelCase_   :   int     	=       new_node
        UpperCAmelCase_   :   Tuple     	=       temp.previous
        UpperCAmelCase_   :   Union[str, Any]     	=       temp
        UpperCAmelCase_   :   int     	=       new_node







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.delete_at_nth(0    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.delete_at_nth(len(self    ) - 1    )







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       len(self    )

     if not 0 <= index <= length - 1:
        raise IndexError("list index out of range"    )
     UpperCAmelCase_   :   Optional[int]     	=       self.head  # default first node
     if length == 1:
        UpperCAmelCase_   :   Union[str, Any]     	=       None
     elif index == 0:
        UpperCAmelCase_   :   List[Any]     	=       self.head.next
        UpperCAmelCase_   :   str     	=       None
     elif index == length - 1:
        UpperCAmelCase_   :   str     	=       self.tail
        UpperCAmelCase_   :   Optional[Any]     	=       self.tail.previous
        UpperCAmelCase_   :   Any     	=       None
     else:
        UpperCAmelCase_   :   List[Any]     	=       self.head
        for _ in range(0							,  lowercase_    ):
           UpperCAmelCase_   :   str     	=       temp.next
        UpperCAmelCase_   :   Dict     	=       temp
        UpperCAmelCase_   :   Optional[Any]     	=       temp.previous
        UpperCAmelCase_   :   int     	=       temp.next
     return delete_node.data







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       self.head

     while current.data != data:  # Find the position to delete
        if current.next:
           UpperCAmelCase_   :   List[str]     	=       current.next
        else:  # We have reached the end an no value matches
           raise ValueError("No data matching given value"    )

     if current == self.head:
        self.delete_head()

     elif current == self.tail:
        self.delete_tail()

     else:  # Before: 1 <--> 2(current) <--> 3
        UpperCAmelCase_   :   str     	=       current.next  # 1 --> 3
        UpperCAmelCase_   :   Optional[Any]     	=       current.previous  # 1 <--> 3
     return data







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return len(self    ) == 0







def       __a	(						):
   UpperCAmelCase_   :   Dict     	=       DoublyLinkedList()
   assert linked_list.is_empty() is True
   assert str(__lowerCamelCase    ) == ""

   try:
      linked_list.delete_head()
      raise AssertionError  # This should not happen.
   except IndexError:
      assert True  # This should happen.

   try:
      linked_list.delete_tail()
      raise AssertionError  # This should not happen.
   except IndexError:
      assert True  # This should happen.

   for i in range(10    ):
      assert len(__lowerCamelCase    ) == i
      linked_list.insert_at_nth(__lowerCamelCase,						i + 1    )
   assert str(__lowerCamelCase    ) == "->".join(str(__lowerCamelCase    ) for i in range(1,						11    )    )

   linked_list.insert_at_head(0    )
   linked_list.insert_at_tail(11    )
   assert str(__lowerCamelCase    ) == "->".join(str(__lowerCamelCase    ) for i in range(0,						12    )    )

   assert linked_list.delete_head() == 0
   assert linked_list.delete_at_nth(9    ) == 10
   assert linked_list.delete_tail() == 11
   assert len(__lowerCamelCase    ) == 9
   assert str(__lowerCamelCase    ) == "->".join(str(__lowerCamelCase    ) for i in range(1,						10    )    )


if __name__ == "__main__":
    from doctest import testmod

    testmod()


