'''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
     _UpperCamelCase				:		Tuple       			=		'platform'

     import jax
     import jax.numpy as jnp

     from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def 						__snake_case (       lowerCAmelCase       :     Tuple      ,  lowerCAmelCase       :     Tuple      ,  lowerCAmelCase       :     Tuple=None      ,  lowerCAmelCase       :     List[Any]=None			):
 if attention_mask is None:
  __UpperCAmelCase         =					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[int]			,a:    List[str]			,a:    Any=13			,a:    Any=7			,a:    Optional[Any]=True			,a:    List[str]=False			,a:    Union[str, Any]=99			,a:    int=16			,a:    Union[str, Any]=2			,a:    List[Any]=4			,a:    Union[str, Any]=4			,a:    int="gelu"			,a:    Tuple=0.1			,a:    List[Any]=0.1			,a:    List[str]=20			,a:    Optional[int]=2			,a:    Any=1			,a:    List[Any]=0			,a:    Optional[Any]=16			,a:    Optional[int]=16			,a:    List[str]=0.02			,):
    __UpperCAmelCase         =					parent
    __UpperCAmelCase         =					batch_size
    __UpperCAmelCase         =					seq_length
    __UpperCAmelCase         =					is_training
    __UpperCAmelCase         =					use_labels
    __UpperCAmelCase         =					vocab_size
    __UpperCAmelCase         =					hidden_size
    __UpperCAmelCase         =					num_hidden_layers
    __UpperCAmelCase         =					num_attention_heads
    __UpperCAmelCase         =					intermediate_size
    __UpperCAmelCase         =					hidden_act
    __UpperCAmelCase         =					hidden_dropout_prob
    __UpperCAmelCase         =					attention_probs_dropout_prob
    __UpperCAmelCase         =					max_position_embeddings
    __UpperCAmelCase         =					eos_token_id
    __UpperCAmelCase         =					pad_token_id
    __UpperCAmelCase         =					bos_token_id
    __UpperCAmelCase         =					embed_dim
    __UpperCAmelCase         =					word_embed_proj_dim
    __UpperCAmelCase         =					initializer_range
    __UpperCAmelCase         =					False





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

    __UpperCAmelCase         =					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			,)
    __UpperCAmelCase         =					prepare_opt_inputs_dict(a			,a	)
    return config, inputs_dict





   def 			snake_case	(							self:    int	):
    __UpperCAmelCase  ,			__UpperCAmelCase         =					self.prepare_config_and_inputs()
    return config, inputs_dict





   def 			snake_case	(							self:    Optional[int]			,a:    int			,a:    Optional[int]			,a:    List[str]	):
    __UpperCAmelCase         =					20
    __UpperCAmelCase         =					model_class_name(a	)

    __UpperCAmelCase         =					inputs_dict['input_ids']
    __UpperCAmelCase         =					inputs_dict['attention_mask']

    __UpperCAmelCase         =					model.init_cache(input_ids.shape[0]			,a	)
    __UpperCAmelCase         =					jnp.ones((input_ids.shape[0], max_length)			,dtype='i4'	)

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

    __UpperCAmelCase         =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,dtype='i4'	)
    __UpperCAmelCase         =					model(
        input_ids[:, -1:]			,attention_mask=a			,past_key_values=outputs_cache.past_key_values			,position_ids=a			,)

    __UpperCAmelCase         =					model(a	)

    __UpperCAmelCase         =					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 			snake_case	(							self:    int			,a:    Any			,a:    List[str]			,a:    Dict	):
    __UpperCAmelCase         =					20
    __UpperCAmelCase         =					model_class_name(a	)

    __UpperCAmelCase  ,			__UpperCAmelCase         =					(
        inputs_dict['input_ids'],
        inputs_dict['attention_mask'],
    )

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

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

    __UpperCAmelCase         =					model(
        input_ids[:, :-1]			,attention_mask=a			,past_key_values=a			,position_ids=a			,)
    __UpperCAmelCase         =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,dtype='i4'	)
    __UpperCAmelCase         =					model(
        input_ids[:, -1:]			,past_key_values=outputs_cache.past_key_values			,attention_mask=a			,position_ids=a			,)

    __UpperCAmelCase         =					model(a			,attention_mask=a	)

    __UpperCAmelCase         =					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(     _lowerCamelCase   ,unittest.TestCase   ,_lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
   __lowerCamelCase    =  () if is_flax_available() else ()





   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					FlaxOPTModelTester(self	)





   def 			snake_case	(							self:    int	):
    __UpperCAmelCase  ,			__UpperCAmelCase         =					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 			snake_case	(							self:    Union[str, Any]	):
    __UpperCAmelCase  ,			__UpperCAmelCase         =					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 			snake_case	(							self:    List[Any]	):
    for model_class_name in self.all_model_classes:
     __UpperCAmelCase         =					model_class_name.from_pretrained('facebook/opt-125m'	)
     __UpperCAmelCase         =					np.ones((1, 1)	) * model.config.eos_token_id
     __UpperCAmelCase         =					model(a	)
     self.assertIsNotNone(a	)




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



   """simple docstring"""





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




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



   """simple docstring"""





   def 			snake_case	(							self:    List[str]	):
    super().setUp()
    __UpperCAmelCase         =					'facebook/opt-350m'





   def 			snake_case	(							self:    Union[str, Any]	):
    __UpperCAmelCase         =					FlaxOPTForCausalLM.from_pretrained(self.path_model	)
    __UpperCAmelCase         =					GPTaTokenizer.from_pretrained(self.path_model	)

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

    __UpperCAmelCase         =					jax.jit(a	)
    __UpperCAmelCase         =					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   _lowercase(     unittest.TestCase				):



   """simple docstring"""





   @property
   def 			snake_case	(							self:    List[Any]	):
    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 			snake_case	(							self:    Optional[Any]	):
    __UpperCAmelCase         =					'facebook/opt-125m'

    __UpperCAmelCase         =					[
        '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',
    ]

    __UpperCAmelCase         =					[]

    __UpperCAmelCase         =					FlaxOPTForCausalLM.from_pretrained(a	)
    __UpperCAmelCase         =					GPTaTokenizer.from_pretrained(a	)

    for prompt in self.prompts:
     __UpperCAmelCase         =					tokenizer(a			,return_tensors='jax'	).input_ids

     __UpperCAmelCase         =					model.generate(a			,max_length=10	)
     __UpperCAmelCase         =					generated_ids[0]

     __UpperCAmelCase         =					tokenizer.batch_decode(a			,skip_special_tokens=a	)
     predicted_outputs += generated_string

    self.assertListEqual(a			,a	)





   def 			snake_case	(							self:    Any	):
    __UpperCAmelCase         =					'facebook/opt-350m'

    __UpperCAmelCase         =					[
        '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',
    ]

    __UpperCAmelCase         =					[]
    __UpperCAmelCase         =					FlaxOPTForCausalLM.from_pretrained(a	)
    __UpperCAmelCase         =					GPTaTokenizer.from_pretrained(a	)

    for prompt in self.prompts:
     __UpperCAmelCase         =					tokenizer(a			,return_tensors='jax'	).input_ids

     __UpperCAmelCase         =					model.generate(a			,max_length=10	)
     __UpperCAmelCase         =					generated_ids[0]

     __UpperCAmelCase         =					tokenizer.batch_decode(a			,skip_special_tokens=a	)
     predicted_outputs += generated_string

    self.assertListEqual(a			,a	)





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

    __UpperCAmelCase         =					jax.jit(model.generate	)

    __UpperCAmelCase         =					jit_generate(inputs['input_ids']			,attention_mask=inputs['attention_mask']	).sequences

    __UpperCAmelCase         =					tokenizer.batch_decode(a			,skip_special_tokens=a	)

    self.assertIsNotNone(a			,a	)





   def 			snake_case	(							self:    Any	):
    __UpperCAmelCase         =					'facebook/opt-350m'

    __UpperCAmelCase         =					GPTaTokenizer.from_pretrained(a	)
    __UpperCAmelCase         =					FlaxOPTForCausalLM.from_pretrained(a	)

    __UpperCAmelCase         =					'left'

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

    __UpperCAmelCase         =					tokenizer(a			,return_tensors='jax'			,padding=a	)
    __UpperCAmelCase         =					inputs['input_ids']

    __UpperCAmelCase         =					model.generate(input_ids=a			,attention_mask=inputs['attention_mask']			,trace=a	)

    __UpperCAmelCase         =					tokenizer(sentences[0]			,return_tensors='jax'	).input_ids
    __UpperCAmelCase         =					model.generate(input_ids=a	)

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

    __UpperCAmelCase         =					tokenizer.batch_decode(outputs[0]			,skip_special_tokens=a	)
    __UpperCAmelCase         =					tokenizer.decode(output_non_padded[0][0]			,skip_special_tokens=a	)
    __UpperCAmelCase         =					tokenizer.decode(output_padded[0][0]			,skip_special_tokens=a	)

    __UpperCAmelCase         =					[
        '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]	)




'''simple docstring'''




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image

class   _lowercase(     unittest.TestCase   ,_lowerCamelCase				):



   """simple docstring"""





   def 			snake_case	(							self:    Optional[int]	):
    __UpperCAmelCase         =					load_tool('image-captioning'	)
    self.tool.setup()
    __UpperCAmelCase         =					load_tool('image-captioning'			,remote=a	)





   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.tool(a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)





   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.remote_tool(a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)





   def 			snake_case	(							self:    Optional[Any]	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.tool(image=a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)





   def 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.remote_tool(image=a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)



