





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
				UpperCAmelCase						:       Optional[Any]        =					'''platform'''

				import jax
				import jax.numpy as jnp

				from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a=None ,						a=None  )				->				Optional[Any]:
					if attention_mask is None:
										__A     :						List[str]          =    np.where(input_ids != config.pad_token_id ,						1 ,						0  )
					return {
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					}





@require_flax
class 						_A:


				"""simple docstring"""


				def __init__(      self					, _A					, _A=13					, _A=7					, _A=True					, _A=False					, _A=99					, _A=16					, _A=2					, _A=4					, _A=4					, _A="gelu"					, _A=0.1					, _A=0.1					, _A=20					, _A=2					, _A=1					, _A=0					, _A=16					, _A=16					, _A=0.0_2					, ):
									__A     :						int          =    parent
									__A     :						List[str]          =    batch_size
									__A     :						Dict          =    seq_length
									__A     :						Dict          =    is_training
									__A     :						List[Any]          =    use_labels
									__A     :						str          =    vocab_size
									__A     :						Union[str, Any]          =    hidden_size
									__A     :						Optional[Any]          =    num_hidden_layers
									__A     :						Tuple          =    num_attention_heads
									__A     :						Dict          =    intermediate_size
									__A     :						List[str]          =    hidden_act
									__A     :						str          =    hidden_dropout_prob
									__A     :						Union[str, Any]          =    attention_probs_dropout_prob
									__A     :						str          =    max_position_embeddings
									__A     :						Tuple          =    eos_token_id
									__A     :						Dict          =    pad_token_id
									__A     :						Tuple          =    bos_token_id
									__A     :						Any          =    embed_dim
									__A     :						Any          =    word_embed_proj_dim
									__A     :						Optional[Any]          =    initializer_range
									__A     :						List[str]          =    False



				def  UpperCAmelCase_		(      self					):
									__A     :						int          =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					, self.vocab_size					)					, 3					, self.vocab_size					)
									__A     :						Union[str, 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=_A					, word_embed_proj_dim=self.word_embed_proj_dim					, initializer_range=self.initializer_range					, use_cache=_A					, )
									__A     :						int          =    prepare_opt_inputs_dict(_A					, _A					)
									return config, inputs_dict



				def  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						Dict          =    self.prepare_config_and_inputs()
									return config, inputs_dict



				def  UpperCAmelCase_		(      self					, _A					, _A					, _A					):
									__A     :						Union[str, Any]          =    20
									__A     :						str          =    model_class_name(_A					)

									__A     :						Dict          =    inputs_dict['input_ids']
									__A     :						Optional[int]          =    inputs_dict['attention_mask']

									__A     :						int          =    model.init_cache(input_ids.shape[0]					, _A					)
									__A     :						Tuple          =    jnp.ones((input_ids.shape[0], max_length)					, dtype='i4'					)

									__A     :						Optional[Any]          =    jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1					)[None, :]					, (input_ids.shape[0], input_ids.shape[-1] - 1)					, )
									__A     :						Tuple          =    model(
									    input_ids[:, :-1]					, attention_mask=_A					, past_key_values=_A					, position_ids=_A					, )

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

									__A     :						List[str]          =    model(_A					)

									__A     :						Tuple          =    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					, _A					, _A					, _A					):
									__A     :						Tuple          =    20
									__A     :						Optional[Any]          =    model_class_name(_A					)

									__A   ,    __A     :						Tuple          =    (
									    inputs_dict['input_ids'],
									    inputs_dict['attention_mask'],
									)

									__A     :						List[Any]          =    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]					, _A					)
									__A     :						Any          =    jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1					)[None, :]					, (input_ids.shape[0], input_ids.shape[-1] - 1)					, )

									__A     :						Tuple          =    model(
									    input_ids[:, :-1]					, attention_mask=_A					, past_key_values=_A					, position_ids=_A					, )
									__A     :						str          =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					, dtype='i4'					)
									__A     :						int          =    model(
									    input_ids[:, -1:]					, past_key_values=outputs_cache.past_key_values					, attention_mask=_A					, position_ids=_A					, )

									__A     :						Optional[Any]          =    model(_A					, attention_mask=_A					)

									__A     :						str          =    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( snake_case__    ,			unittest.TestCase    ,			snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  str    =     (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
				UpperCamelCase   :  Union[str, Any]    =     () if is_flax_available() else ()



				def  UpperCAmelCase_		(      self					):
									__A     :						Union[str, Any]          =    FlaxOPTModelTester(self					)



				def  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						Union[str, Any]          =    self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward(_A					, _A					, _A					)



				def  UpperCAmelCase_		(      self					):
									__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_with_attn_mask(_A					, _A					, _A					)





				@slow
				def  UpperCAmelCase_		(      self					):
									for model_class_name in self.all_model_classes:
														__A     :						Tuple          =    model_class_name.from_pretrained('facebook/opt-125m'					)
														__A     :						List[str]          =    np.ones((1, 1)					) * model.config.eos_token_id
														__A     :						Any          =    model(_A					)
														self.assertIsNotNone(_A					)





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


				"""simple docstring"""


				@slow
				def  UpperCAmelCase_		(      self					):
									__A     :						Dict          =    FlaxOPTModel.from_pretrained('facebook/opt-350m'					)
									__A     :						Any          =    jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					)
									__A     :						int          =    model(input_ids=_A					).last_hidden_state
									__A     :						str          =    (1, 11, 512)
									self.assertEqual(output.shape					, _A					)
									__A     :						Optional[int]          =    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]					, _A					, atol=4e-2					)					)





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


				"""simple docstring"""


				def  UpperCAmelCase_		(      self					):
									super().setUp()
									__A     :						int          =    'facebook/opt-350m'





				def  UpperCAmelCase_		(      self					):
									__A     :						int          =    FlaxOPTForCausalLM.from_pretrained(self.path_model					)
									__A     :						Dict          =    GPTaTokenizer.from_pretrained(self.path_model					)

									__A     :						List[str]          =    [
									    '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(_A					, return_tensors='jax'					, padding=_A					, add_special_tokens=_A					)
									__A     :						List[Any]          =    model(inputs.input_ids					, attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
									__A     :						Optional[int]          =    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(_A					, _A					, atol=4e-2					)					)

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





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


				"""simple docstring"""


				@property
				def  UpperCAmelCase_		(      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  UpperCAmelCase_		(      self					):
									__A     :						Optional[int]          =    '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     :						List[str]          =    []

									__A     :						List[Any]          =    FlaxOPTForCausalLM.from_pretrained(_A					)
									__A     :						Optional[int]          =    GPTaTokenizer.from_pretrained(_A					)

									for prompt in self.prompts:
														__A     :						Dict          =    tokenizer(_A					, return_tensors='jax'					).input_ids

														__A     :						Any          =    model.generate(_A					, max_length=10					)
														__A     :						Any          =    generated_ids[0]

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

									self.assertListEqual(_A					, _A					)



				def  UpperCAmelCase_		(      self					):
									__A     :						Dict          =    'facebook/opt-350m'

									__A     :						List[str]          =    [
									    '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     :						Dict          =    []
									__A     :						int          =    FlaxOPTForCausalLM.from_pretrained(_A					)
									__A     :						str          =    GPTaTokenizer.from_pretrained(_A					)

									for prompt in self.prompts:
														__A     :						str          =    tokenizer(_A					, return_tensors='jax'					).input_ids

														__A     :						int          =    model.generate(_A					, max_length=10					)
														__A     :						List[Any]          =    generated_ids[0]

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

									self.assertListEqual(_A					, _A					)



				def  UpperCAmelCase_		(      self					):
									__A     :						Union[str, Any]          =    'facebook/opt-125m'
									__A     :						int          =    [
									    'Today is a beautiful day and I want to thank',
									    'In the city of Rome Canaver Canaver Canaver Canaver',
									]
									__A     :						Dict          =    FlaxOPTForCausalLM.from_pretrained(_A					)
									__A     :						Dict          =    GPTaTokenizer.from_pretrained(_A					)
									__A     :						Dict          =    tokenizer(
									    [
									        'Today is a beautiful day and I want to',
									        'In the city of',
									    ]					, return_tensors='jax'					, padding=_A					, )

									__A     :						str          =    jax.jit(model.generate					)

									__A     :						Union[str, Any]          =    jit_generate(inputs['input_ids']					, attention_mask=inputs['attention_mask']					).sequences

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

									self.assertIsNotNone(_A					, _A					)





				def  UpperCAmelCase_		(      self					):
									__A     :						Tuple          =    'facebook/opt-350m'

									__A     :						Optional[Any]          =    GPTaTokenizer.from_pretrained(_A					)
									__A     :						Dict          =    FlaxOPTForCausalLM.from_pretrained(_A					)

									__A     :						int          =    'left'

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

									__A     :						List[str]          =    tokenizer(_A					, return_tensors='jax'					, padding=_A					)
									__A     :						Tuple          =    inputs['input_ids']

									__A     :						Tuple          =    model.generate(input_ids=_A					, attention_mask=inputs['attention_mask']					, trace=_A					)

									__A     :						Any          =    tokenizer(sentences[0]					, return_tensors='jax'					).input_ids
									__A     :						List[str]          =    model.generate(input_ids=_A					)

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

									__A     :						Tuple          =    tokenizer.batch_decode(outputs[0]					, skip_special_tokens=_A					)
									__A     :						List[Any]          =    tokenizer.decode(output_non_padded[0][0]					, skip_special_tokens=_A					)
									__A     :						Tuple          =    tokenizer.decode(output_padded[0][0]					, skip_special_tokens=_A					)

									__A     :						int          =    [
									    '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(_A					, _A					)
									self.assertListEqual(_A					, [non_padded_sentence, padded_sentence]					)











from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase						:       str        =					logging.get_logger(__name__)

UpperCAmelCase						:       Optional[Any]        =					{
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  Union[str, Any]    =     '''swiftformer'''



				def __init__(      self					, _A=3					, _A=[3, 3, 6, 4]					, _A=[48, 56, 112, 220]					, _A=4					, _A=[True, True, True, True]					, _A="gelu"					, _A=3					, _A=2					, _A=1					, _A=0.0					, _A=True					, _A=1e-5					, _A=1e-5					, **_A					, ):
									super().__init__(**_A					)
									__A     :						List[Any]          =    num_channels
									__A     :						List[Any]          =    depths
									__A     :						int          =    embed_dims
									__A     :						Union[str, Any]          =    mlp_ratio
									__A     :						List[str]          =    downsamples
									__A     :						str          =    hidden_act
									__A     :						Any          =    down_patch_size
									__A     :						Any          =    down_stride
									__A     :						Any          =    down_pad
									__A     :						List[Any]          =    drop_path_rate
									__A     :						str          =    use_layer_scale
									__A     :						Dict          =    layer_scale_init_value
									__A     :						Union[str, Any]          =    batch_norm_eps





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  Optional[Any]    =     version.parse('''1.11'''       )



				@property
				def  UpperCAmelCase_		(      self					):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]					)





				@property
				def  UpperCAmelCase_		(      self					):
									return 1e-4




