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				 =   """platform"""

  import jax
  import jax.numpy as jnp

  from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=None							)						->						Any:
     if attention_mask is None:
          a	:     Dict						      =    np.where(input_ids != config.pad_token_id    ,   1    ,   0							)
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }






@require_flax
class 				_SCREAMING_SNAKE_CASE:
   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_=0.02				,  )						->Dict:
        a	:     int						      =    parent
        a	:     List[str]						      =    batch_size
        a	:     str						      =    seq_length
        a	:     List[Any]						      =    is_training
        a	:     Any						      =    use_labels
        a	:     Optional[int]						      =    vocab_size
        a	:     Union[str, Any]						      =    hidden_size
        a	:     List[Any]						      =    num_hidden_layers
        a	:     Optional[Any]						      =    num_attention_heads
        a	:     Dict						      =    intermediate_size
        a	:     Dict						      =    hidden_act
        a	:     Union[str, Any]						      =    hidden_dropout_prob
        a	:     Optional[int]						      =    attention_probs_dropout_prob
        a	:     Optional[int]						      =    max_position_embeddings
        a	:     List[str]						      =    eos_token_id
        a	:     Tuple						      =    pad_token_id
        a	:     List[str]						      =    bos_token_id
        a	:     Dict						      =    embed_dim
        a	:     Any						      =    word_embed_proj_dim
        a	:     Any						      =    initializer_range
        a	:     Any						      =    False
   def _lowercase(     self     )						->Dict:
        a	:     int						      =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,  self.vocab_size     )				,  3				,  self.vocab_size     )
        a	:     List[str]						      =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,  dtype=np.intaa     ))				,  -1     )

        a	:     Dict						      =    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_				,  )
        a	:     Union[str, Any]						      =    prepare_opt_inputs_dict(lowerCAmelCase_				,  lowerCAmelCase_     )
        return config, inputs_dict
   def _lowercase(     self     )						->List[Any]:
        a      ,				a	:     List[str]						      =    self.prepare_config_and_inputs()
        return config, inputs_dict
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->Optional[int]:
        a	:     str						      =    20
        a	:     int						      =    model_class_name(lowerCAmelCase_     )

        a	:     Union[str, Any]						      =    inputs_dict['''input_ids''']
        a	:     str						      =    inputs_dict['''attention_mask''']

        a	:     Dict						      =    model.init_cache(input_ids.shape[0]				,  lowerCAmelCase_     )
        a	:     List[Any]						      =    jnp.ones((input_ids.shape[0], max_length)				,  dtype='''i4'''     )

        a	:     Dict						      =    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=lowerCAmelCase_				,  past_key_values=lowerCAmelCase_				,  position_ids=lowerCAmelCase_				,  )

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

        a	:     List[str]						      =    model(lowerCAmelCase_     )

        a	:     Dict						      =    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 _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->int:
        a	:     List[Any]						      =    20
        a	:     List[Any]						      =    model_class_name(lowerCAmelCase_     )

        a      ,				a	:     Optional[int]						      =    (
            inputs_dict['''input_ids'''],
            inputs_dict['''attention_mask'''],
        )

        a	:     List[str]						      =    jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])     ),
            ]				,  axis=-1				,  )

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

        a	:     List[str]						      =    model(
            input_ids[:, :-1]				,  attention_mask=lowerCAmelCase_				,  past_key_values=lowerCAmelCase_				,  position_ids=lowerCAmelCase_				,  )
        a	:     Any						      =    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=lowerCAmelCase_				,  position_ids=lowerCAmelCase_				,  )

        a	:     Dict						      =    model(lowerCAmelCase_				,  attention_mask=lowerCAmelCase_     )

        a	:     Dict						      =    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 				_SCREAMING_SNAKE_CASE( snake_case__							,   unittest.TestCase							,   snake_case__						):
   UpperCamelCase_					:    Optional[int]			      = (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
   UpperCamelCase_					:    int			      = () if is_flax_available() else ()
   def _lowercase(     self     )						->Any:
        a	:     List[Any]						      =    FlaxOPTModelTester(self     )
   def _lowercase(     self     )						->List[Any]:
        a      ,				a	:     List[Any]						      =    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 _lowercase(     self     )						->Any:
        a      ,				a	:     Any						      =    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 _lowercase(     self     )						->Tuple:
        for model_class_name in self.all_model_classes:
             a	:     str						      =    model_class_name.from_pretrained('''facebook/opt-125m'''     )
             a	:     Dict						      =    np.ones((1, 1)     ) * model.config.eos_token_id
             a	:     Union[str, Any]						      =    model(lowerCAmelCase_     )
             self.assertIsNotNone(lowerCAmelCase_     )






@require_sentencepiece
@require_flax
class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   @slow
   def _lowercase(     self     )						->str:
        a	:     Any						      =    FlaxOPTModel.from_pretrained('''facebook/opt-350m'''     )
        a	:     Tuple						      =    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	:     Optional[int]						      =    model(input_ids=lowerCAmelCase_     ).last_hidden_state
        a	:     Tuple						      =    (1, 11, 5_12)
        self.assertEqual(output.shape				,  lowerCAmelCase_     )
        a	:     Any						      =    jnp.array(
            [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]     )
        self.assertTrue(jnp.allclose(output[:, :3, :3]				,  lowerCAmelCase_				,  atol=4e-2     )     )






@require_flax
@slow
class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   def _lowercase(     self     )						->Dict:
        super().setUp()
        a	:     Tuple						      =    '''facebook/opt-350m'''


   def _lowercase(     self     )						->List[Any]:
        a	:     Optional[int]						      =    FlaxOPTForCausalLM.from_pretrained(self.path_model     )
        a	:     str						      =    GPTaTokenizer.from_pretrained(self.path_model     )

        a	:     Dict						      =    [
            '''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(lowerCAmelCase_				,  return_tensors='''jax'''				,  padding=lowerCAmelCase_				,  add_special_tokens=lowerCAmelCase_     )
        a	:     Optional[int]						      =    model(inputs.input_ids				,  attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
        a	:     int						      =    jnp.array(
            [
                [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
            ]     )
        self.assertTrue(jnp.allclose(lowerCAmelCase_				,  lowerCAmelCase_				,  atol=4e-2     )     )

        a	:     str						      =    jax.jit(lowerCAmelCase_     )
        a	:     Optional[int]						      =    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 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   @property
   def _lowercase(     self     )						->Union[str, 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 _lowercase(     self     )						->List[str]:
        a	:     List[str]						      =    '''facebook/opt-125m'''

        a	:     Union[str, 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	:     Optional[int]						      =    FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase_     )
        a	:     str						      =    GPTaTokenizer.from_pretrained(lowerCAmelCase_     )

        for prompt in self.prompts:
             a	:     Union[str, Any]						      =    tokenizer(lowerCAmelCase_				,  return_tensors='''jax'''     ).input_ids

             a	:     Optional[int]						      =    model.generate(lowerCAmelCase_				,  max_length=10     )
             a	:     Tuple						      =    generated_ids[0]

             a	:     Optional[int]						      =    tokenizer.batch_decode(lowerCAmelCase_				,  skip_special_tokens=lowerCAmelCase_     )
             predicted_outputs += generated_string

        self.assertListEqual(lowerCAmelCase_				,  lowerCAmelCase_     )
   def _lowercase(     self     )						->Tuple:
        a	:     int						      =    '''facebook/opt-350m'''

        a	:     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	:     Union[str, Any]						      =    []
        a	:     Dict						      =    FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase_     )
        a	:     Optional[int]						      =    GPTaTokenizer.from_pretrained(lowerCAmelCase_     )

        for prompt in self.prompts:
             a	:     Union[str, Any]						      =    tokenizer(lowerCAmelCase_				,  return_tensors='''jax'''     ).input_ids

             a	:     Union[str, Any]						      =    model.generate(lowerCAmelCase_				,  max_length=10     )
             a	:     Optional[Any]						      =    generated_ids[0]

             a	:     Optional[int]						      =    tokenizer.batch_decode(lowerCAmelCase_				,  skip_special_tokens=lowerCAmelCase_     )
             predicted_outputs += generated_string

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

        a	:     Any						      =    jax.jit(model.generate     )

        a	:     List[Any]						      =    jit_generate(inputs['''input_ids''']				,  attention_mask=inputs['''attention_mask''']     ).sequences

        a	:     List[str]						      =    tokenizer.batch_decode(lowerCAmelCase_				,  skip_special_tokens=lowerCAmelCase_     )

        self.assertIsNotNone(lowerCAmelCase_				,  lowerCAmelCase_     )


   def _lowercase(     self     )						->int:
        a	:     Any						      =    '''facebook/opt-350m'''

        a	:     str						      =    GPTaTokenizer.from_pretrained(lowerCAmelCase_     )
        a	:     int						      =    FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase_     )

        a	:     List[str]						      =    '''left'''

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

        a	:     Dict						      =    tokenizer(lowerCAmelCase_				,  return_tensors='''jax'''				,  padding=lowerCAmelCase_     )
        a	:     List[str]						      =    inputs['''input_ids''']

        a	:     Any						      =    model.generate(input_ids=lowerCAmelCase_				,  attention_mask=inputs['''attention_mask''']				,  trace=lowerCAmelCase_     )

        a	:     List[Any]						      =    tokenizer(sentences[0]				,  return_tensors='''jax'''     ).input_ids
        a	:     Optional[int]						      =    model.generate(input_ids=lowerCAmelCase_     )

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

        a	:     List[str]						      =    tokenizer.batch_decode(outputs[0]				,  skip_special_tokens=lowerCAmelCase_     )
        a	:     Optional[Any]						      =    tokenizer.decode(output_non_padded[0][0]				,  skip_special_tokens=lowerCAmelCase_     )
        a	:     int						      =    tokenizer.decode(output_padded[0][0]				,  skip_special_tokens=lowerCAmelCase_     )

        a	:     Tuple						      =    [
            '''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]     )
UpperCAmelCase				 =   {
    0: """underwear""",
    1: """pants""",
    2: """belt""",
    3: """suit""",
    4: """shoe""",
    5: """socks""",
    6: """shirt""",
    7: """tie""",
    8: """watch""",
}

UpperCAmelCase				 =   [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

UpperCAmelCase				 =   [0 for x in range(len(graph))]
UpperCAmelCase				 =   []







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						List[Any]:
     a	:     Tuple						      =    1
     while stack:
          a	:     Optional[Any]						      =    stack.pop()
          print(lowerCAmelCase_    ,   clothes[current_clothing]							)
          order += 1







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Any:
     a	:     Union[str, Any]						      =    1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)

     stack.append(lowerCAmelCase_							)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Optional[Any]:
     for v in range(len(lowerCAmelCase_							)							):
          if not visited[v]:
               depth_first_search(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)


if __name__ == "__main__":
  topological_sort(graph, visited)
  print(stack)
  print_stack(stack, clothes)