
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       :       Optional[int]				   =			"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	)			->							Tuple:
				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 			a      :






		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase=13	,     __UpperCamelCase=7	,     __UpperCamelCase=True	,     __UpperCamelCase=False	,     __UpperCamelCase=99	,     __UpperCamelCase=16	,     __UpperCamelCase=2	,     __UpperCamelCase=4	,     __UpperCamelCase=4	,     __UpperCamelCase="gelu"	,     __UpperCamelCase=0.1	,     __UpperCamelCase=0.1	,     __UpperCamelCase=20	,     __UpperCamelCase=2	,     __UpperCamelCase=1	,     __UpperCamelCase=0	,     __UpperCamelCase=16	,     __UpperCamelCase=16	,     __UpperCamelCase=0.02	,     )->			Any:
						'''simple docstring'''


						A__     :							Union[str, Any]   				=parent
						A__     :							Any   				=batch_size
						A__     :							Optional[int]   				=seq_length
						A__     :							Tuple   				=is_training
						A__     :							List[Any]   				=use_labels
						A__     :							List[Any]   				=vocab_size
						A__     :							int   				=hidden_size
						A__     :							Union[str, Any]   				=num_hidden_layers
						A__     :							str   				=num_attention_heads
						A__     :							Tuple   				=intermediate_size
						A__     :							List[Any]   				=hidden_act
						A__     :							Union[str, Any]   				=hidden_dropout_prob
						A__     :							Dict   				=attention_probs_dropout_prob
						A__     :							Optional[int]   				=max_position_embeddings
						A__     :							int   				=eos_token_id
						A__     :							Optional[int]   				=pad_token_id
						A__     :							Tuple   				=bos_token_id
						A__     :							List[str]   				=embed_dim
						A__     :							Tuple   				=word_embed_proj_dim
						A__     :							Dict   				=initializer_range
						A__     :							Optional[int]   				=False






		def       lowerCAmelCase_  (  self							)->			Any:
						'''simple docstring'''


						A__     :							Union[str, Any]   				=np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,     self.vocab_size							)	,     3	,     self.vocab_size							)
						A__     :							List[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=__UpperCamelCase	,     word_embed_proj_dim=self.word_embed_proj_dim	,     initializer_range=self.initializer_range	,     use_cache=__UpperCamelCase	,     )
						A__     :							Union[str, Any]   				=prepare_opt_inputs_dict(__UpperCamelCase	,     __UpperCamelCase							)
						return config, inputs_dict






		def       lowerCAmelCase_  (  self							)->			List[Any]:
						'''simple docstring'''


						A__     :							Tuple   				=self.prepare_config_and_inputs()
						return config, inputs_dict






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			List[Any]:
						'''simple docstring'''


						A__     :							List[str]   				=20
						A__     :							Optional[Any]   				=model_class_name(__UpperCamelCase							)

						A__     :							List[Any]   				=inputs_dict['''input_ids''']
						A__     :							Any   				=inputs_dict['''attention_mask''']

						A__     :							List[str]   				=model.init_cache(input_ids.shape[0]	,     __UpperCamelCase							)
						A__     :							int   				=jnp.ones((input_ids.shape[0], max_length)	,     dtype='''i4'''							)

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

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

						A__     :							Dict   				=model(__UpperCamelCase							)

						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}'							)






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			Tuple:
						'''simple docstring'''


						A__     :							int   				=20
						A__     :							Dict   				=model_class_name(__UpperCamelCase							)

						A__     :							Union[str, Any]   				=(
						    inputs_dict['''input_ids'''],
						    inputs_dict['''attention_mask'''],
						)

						A__     :							Optional[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]	,     __UpperCamelCase							)
						A__     :							Union[str, Any]   				=jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1							)[None, :]	,     (input_ids.shape[0], input_ids.shape[-1] - 1)	,     )

						A__     :							Dict   				=model(
						    input_ids[:, :-1]	,     attention_mask=__UpperCamelCase	,     past_key_values=__UpperCamelCase	,     position_ids=__UpperCamelCase	,     )
						A__     :							Union[str, Any]   				=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,     dtype='''i4'''							)
						A__     :							str   				=model(
						    input_ids[:, -1:]	,     past_key_values=outputs_cache.past_key_values	,     attention_mask=__UpperCamelCase	,     position_ids=__UpperCamelCase	,     )

						A__     :							Optional[Any]   				=model(__UpperCamelCase	,     attention_mask=__UpperCamelCase							)

						A__     :							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}'							)







@require_flax
class 			a      (      UpperCamelCase_      ,unittest.TestCase      ,UpperCamelCase_   ):
		__lowercase													=		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
		__lowercase													=		() if is_flax_available() else ()






		def       lowerCAmelCase_  (  self							)->			str:
						'''simple docstring'''


						A__     :							Union[str, Any]   				=FlaxOPTModelTester(self							)






		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
						'''simple docstring'''


						A__     :							Optional[int]   				=self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
										self.model_tester.check_use_cache_forward(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)






		def       lowerCAmelCase_  (  self							)->			Tuple:
						'''simple docstring'''


						A__     :							Dict   				=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(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)






		@slow
		def       lowerCAmelCase_  (  self							)->			Any:
						'''simple docstring'''


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







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






		@slow
		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						A__     :							Dict   				=FlaxOPTModel.from_pretrained('''facebook/opt-350m'''							)
						A__     :							Optional[int]   				=jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]							)
						A__     :							List[str]   				=model(input_ids=__UpperCamelCase							).last_hidden_state
						A__     :							str   				=(1, 11, 5_12)
						self.assertEqual(output.shape	,     __UpperCamelCase							)
						A__     :							Any   				=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]	,     __UpperCamelCase	,     atol=4E-2							)							)







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






		def       lowerCAmelCase_  (  self							)->			Tuple:
						'''simple docstring'''


						super().setUp()
						A__     :							Union[str, Any]   				='''facebook/opt-350m'''






		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						A__     :							List[Any]   				=FlaxOPTForCausalLM.from_pretrained(self.path_model							)
						A__     :							Union[str, Any]   				=GPTaTokenizer.from_pretrained(self.path_model							)

						A__     :							Union[str, 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__     :							int   				=tokenizer(__UpperCamelCase	,     return_tensors='''jax'''	,     padding=__UpperCamelCase	,     add_special_tokens=__UpperCamelCase							)
						A__     :							Optional[Any]   				=model(inputs.input_ids	,     attention_mask=inputs.attention_mask							)[0].mean(axis=-1							)
						A__     :							Optional[Any]   				=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(__UpperCamelCase	,     __UpperCamelCase	,     atol=4E-2							)							)

						A__     :							Union[str, Any]   				=jax.jit(__UpperCamelCase							)
						A__     :							str   				=model(inputs.input_ids	,     attention_mask=inputs.attention_mask							)[0].mean(axis=-1							)
						self.assertTrue(jnp.allclose(__UpperCamelCase	,     __UpperCamelCase	,     atol=4E-2							)							)







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






		@property
		def       lowerCAmelCase_  (  self							)->			Optional[int]:
						'''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       lowerCAmelCase_  (  self							)->			Union[str, Any]:
						'''simple docstring'''


						A__     :							int   				='''facebook/opt-125m'''

						A__     :							Dict   				=[
						    '''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__     :							Union[str, Any]   				=[]

						A__     :							Optional[int]   				=FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase							)
						A__     :							Optional[int]   				=GPTaTokenizer.from_pretrained(__UpperCamelCase							)

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

										A__     :							Optional[int]   				=model.generate(__UpperCamelCase	,     max_length=10							)
										A__     :							Optional[int]   				=generated_ids[0]

										A__     :							int   				=tokenizer.batch_decode(__UpperCamelCase	,     skip_special_tokens=__UpperCamelCase							)
										predicted_outputs += generated_string

						self.assertListEqual(__UpperCamelCase	,     __UpperCamelCase							)






		def       lowerCAmelCase_  (  self							)->			int:
						'''simple docstring'''


						A__     :							Optional[int]   				='''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__     :							int   				=[]
						A__     :							Union[str, Any]   				=FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase							)
						A__     :							Tuple   				=GPTaTokenizer.from_pretrained(__UpperCamelCase							)

						for prompt in self.prompts:
										A__     :							Any   				=tokenizer(__UpperCamelCase	,     return_tensors='''jax'''							).input_ids

										A__     :							Union[str, Any]   				=model.generate(__UpperCamelCase	,     max_length=10							)
										A__     :							int   				=generated_ids[0]

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

						self.assertListEqual(__UpperCamelCase	,     __UpperCamelCase							)






		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						A__     :							List[str]   				='''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__     :							Optional[Any]   				=FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase							)
						A__     :							List[Any]   				=GPTaTokenizer.from_pretrained(__UpperCamelCase							)
						A__     :							Union[str, Any]   				=tokenizer(
						    [
						        '''Today is a beautiful day and I want to''',
						        '''In the city of''',
						    ]	,     return_tensors='''jax'''	,     padding=__UpperCamelCase	,     )

						A__     :							Dict   				=jax.jit(model.generate							)

						A__     :							Optional[int]   				=jit_generate(inputs['''input_ids''']	,     attention_mask=inputs['''attention_mask''']							).sequences

						A__     :							List[str]   				=tokenizer.batch_decode(__UpperCamelCase	,     skip_special_tokens=__UpperCamelCase							)

						self.assertIsNotNone(__UpperCamelCase	,     __UpperCamelCase							)






		def       lowerCAmelCase_  (  self							)->			Any:
						'''simple docstring'''


						A__     :							Tuple   				='''facebook/opt-350m'''

						A__     :							Union[str, Any]   				=GPTaTokenizer.from_pretrained(__UpperCamelCase							)
						A__     :							Tuple   				=FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase							)

						A__     :							Any   				='''left'''

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

						A__     :							Union[str, Any]   				=tokenizer(__UpperCamelCase	,     return_tensors='''jax'''	,     padding=__UpperCamelCase							)
						A__     :							str   				=inputs['''input_ids''']

						A__     :							int   				=model.generate(input_ids=__UpperCamelCase	,     attention_mask=inputs['''attention_mask''']	,     trace=__UpperCamelCase							)

						A__     :							int   				=tokenizer(sentences[0]	,     return_tensors='''jax'''							).input_ids
						A__     :							Union[str, Any]   				=model.generate(input_ids=__UpperCamelCase							)

						A__     :							int   				=inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
						A__     :							Optional[Any]   				=tokenizer(sentences[1]	,     return_tensors='''jax'''							).input_ids
						A__     :							Dict   				=model.generate(input_ids=__UpperCamelCase	,     max_length=model.config.max_length - num_paddings							)

						A__     :							Union[str, Any]   				=tokenizer.batch_decode(outputs[0]	,     skip_special_tokens=__UpperCamelCase							)
						A__     :							str   				=tokenizer.decode(output_non_padded[0][0]	,     skip_special_tokens=__UpperCamelCase							)
						A__     :							Union[str, Any]   				=tokenizer.decode(output_padded[0][0]	,     skip_special_tokens=__UpperCamelCase							)

						A__     :							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(__UpperCamelCase	,     __UpperCamelCase							)
						self.assertListEqual(__UpperCamelCase	,     [non_padded_sentence, padded_sentence]							)







def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
				barcode //= 1_0  # exclude the last digit
				A__     :							List[str]   				=False
				A__     :							Dict   				=0

				# extract and check each digit
				while barcode != 0:
								A__     :							Union[str, Any]   				=1 if checker else 3
								s += mult * (barcode % 1_0)
								barcode //= 1_0
								A__     :							Union[str, Any]   				=not checker

				return (1_0 - (s % 1_0)) % 1_0



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							bool:
				return len(str(snake_case_	)	) == 1_3 and get_check_digit(snake_case_	) == barcode % 1_0



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
				if str(snake_case_	).isalpha():
								A__     :							Tuple   				=f'Barcode \'{barcode}\' has alphabetic characters.'
								raise ValueError(snake_case_	)
				elif int(snake_case_	) < 0:
								raise ValueError('''The entered barcode has a negative value. Try again.'''	)
				else:
								return int(snake_case_	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCamelCase       :       List[Any]				   =			get_barcode(input("Barcode: ").strip())

						if is_valid(barcode):
												print(F"'{barcode}' is a valid barcode.")
						else:
												print(F"'{barcode}' is NOT a valid barcode.")



