"""simple docstring"""



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, 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
   SCREAMING_SNAKE_CASE__:Union[str, Any]		       =       """platform"""

   import jax
   import jax.numpy as jnp

   from transformers import BlenderbotTokenizer
   from transformers.models.blenderbot.modeling_flax_blenderbot import (
       FlaxBlenderbotForConditionalGeneration,
       FlaxBlenderbotModel,
       shift_tokens_right,
   )



def      _lowerCamelCase(     a					,  a					,  a=None					,  a=None					,  a=None					,  a=None					,  a=None					,  a=None					,  ):
       if attention_mask is None:
              __a	      =  np.where(input_ids != config.pad_token_id					,  1					,  0							)
       if decoder_attention_mask is None:
              __a	      =  np.where(decoder_input_ids != config.pad_token_id					,  1					,  0							)
       if head_mask is None:
              __a	      =  np.ones((config.encoder_layers, config.encoder_attention_heads)							)
       if decoder_head_mask is None:
              __a	      =  np.ones((config.decoder_layers, config.decoder_attention_heads)							)
       if cross_attn_head_mask is None:
              __a	      =  np.ones((config.decoder_layers, config.decoder_attention_heads)							)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": attention_mask,
       }






class       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=32  ,						lowerCamelCase=2  ,						lowerCamelCase=1  ,						lowerCamelCase=0  ,						lowerCamelCase=0.02  ,						):
        __a	      =  parent
        __a	      =  batch_size
        __a	      =  seq_length
        __a	      =  is_training
        __a	      =  use_labels
        __a	      =  vocab_size
        __a	      =  hidden_size
        __a	      =  num_hidden_layers
        __a	      =  num_attention_heads
        __a	      =  intermediate_size
        __a	      =  hidden_act
        __a	      =  hidden_dropout_prob
        __a	      =  attention_probs_dropout_prob
        __a	      =  max_position_embeddings
        __a	      =  eos_token_id
        __a	      =  pad_token_id
        __a	      =  bos_token_id
        __a	      =  initializer_range


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

        __a	      =  shift_tokens_right(_a  ,						1  ,						2		)

        __a	      =  BlenderbotConfig(
            vocab_size=self.vocab_size  ,						d_model=self.hidden_size  ,						encoder_layers=self.num_hidden_layers  ,						decoder_layers=self.num_hidden_layers  ,						encoder_attention_heads=self.num_attention_heads  ,						decoder_attention_heads=self.num_attention_heads  ,						encoder_ffn_dim=self.intermediate_size  ,						decoder_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  ,						initializer_range=self.initializer_range  ,						use_cache=_a  ,						)
        __a	      =  prepare_blenderbot_inputs_dict(_a  ,						_a  ,						_a		)
        return config, inputs_dict


 def 		a__					(      self		):
        __a      ,					__a	      =  self.prepare_config_and_inputs()
        return config, inputs_dict


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a	      =  20
        __a	      =  model_class_name(_a		)

        __a	      =  model.encode(inputs_dict["input_ids"]		)

        __a      ,					__a	      =  (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        __a	      =  model.init_cache(decoder_input_ids.shape[0]  ,						_a  ,						_a		)
        __a	      =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,						dtype="i4"		)

        __a	      =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]  ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,						)
        __a	      =  model.decode(
            decoder_input_ids[:, :-1]  ,						_a  ,						decoder_attention_mask=_a  ,						past_key_values=_a  ,						decoder_position_ids=_a  ,						)

        __a	      =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,						dtype="i4"		)
        __a	      =  model.decode(
            decoder_input_ids[:, -1:]  ,						_a  ,						decoder_attention_mask=_a  ,						past_key_values=outputs_cache.past_key_values  ,						decoder_position_ids=_a  ,						)

        __a	      =  model.decode(_a  ,						_a		)

        __a	      =  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 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a	      =  20
        __a	      =  model_class_name(_a		)

        __a	      =  model.encode(inputs_dict["input_ids"]		)

        __a      ,					__a	      =  (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        __a	      =  jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
            ]  ,						axis=-1  ,						)

        __a	      =  model.init_cache(decoder_input_ids.shape[0]  ,						_a  ,						_a		)
        __a	      =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]  ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,						)

        __a	      =  model.decode(
            decoder_input_ids[:, :-1]  ,						_a  ,						decoder_attention_mask=_a  ,						past_key_values=_a  ,						decoder_position_ids=_a  ,						)
        __a	      =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,						dtype="i4"		)
        __a	      =  model.decode(
            decoder_input_ids[:, -1:]  ,						_a  ,						past_key_values=outputs_cache.past_key_values  ,						decoder_attention_mask=_a  ,						decoder_position_ids=_a  ,						)

        __a	      =  model.decode(_a  ,						_a  ,						decoder_attention_mask=_a		)

        __a	      =  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       snake_case__	(    unittest.TestCase    ):
 _snake_case    : str				   =		99


 def 		a__					(      self		):
        __a	      =  np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]  ,						dtype=np.intaa  ,						)

        __a	      =  input_ids.shape[0]
        __a	      =  BlenderbotConfig(
            vocab_size=self.vocab_size  ,						d_model=24  ,						encoder_layers=2  ,						decoder_layers=2  ,						encoder_attention_heads=2  ,						decoder_attention_heads=2  ,						encoder_ffn_dim=32  ,						decoder_ffn_dim=32  ,						max_position_embeddings=48  ,						eos_token_id=2  ,						pad_token_id=1  ,						bos_token_id=0  ,						)
        return config, input_ids, batch_size


 def 		a__					(      self		):
        __a      ,					__a      ,					__a	      =  self._get_config_and_data()
        __a	      =  FlaxBlenderbotForConditionalGeneration(_a		)
        __a	      =  lm_model(input_ids=_a		)
        __a	      =  (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["logits"].shape  ,						_a		)


 def 		a__					(      self		):
        __a	      =  BlenderbotConfig(
            vocab_size=self.vocab_size  ,						d_model=14  ,						encoder_layers=2  ,						decoder_layers=2  ,						encoder_attention_heads=2  ,						decoder_attention_heads=2  ,						encoder_ffn_dim=8  ,						decoder_ffn_dim=8  ,						max_position_embeddings=48  ,						)
        __a	      =  FlaxBlenderbotForConditionalGeneration(_a		)
        __a	      =  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]  ,						dtype=np.intaa		)
        __a	      =  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]  ,						dtype=np.intaa		)
        __a	      =  lm_model(input_ids=_a  ,						decoder_input_ids=_a		)
        __a	      =  (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["logits"].shape  ,						_a		)






 def 		a__					(      self		):
        __a	      =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]  ,						dtype=np.intaa		)
        __a	      =  shift_tokens_right(_a  ,						1  ,						2		)
        __a	      =  np.equal(_a  ,						1		).astype(np.floataa		).sum()
        __a	      =  np.equal(_a  ,						1		).astype(np.floataa		).sum()
        self.assertEqual(shifted.shape  ,						input_ids.shape		)
        self.assertEqual(_a  ,						n_pad_before - 1		)
        self.assertTrue(np.equal(shifted[:, 0]  ,						2		).all()		)






@require_flax
class       snake_case__	(    __SCREAMING_SNAKE_CASE,		unittest.TestCase,		__SCREAMING_SNAKE_CASE    ):
 _snake_case    : Optional[int]				   =		True
 _snake_case    : Dict				   =		(
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 _snake_case    : List[Any]				   =		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()


 def 		a__					(      self		):
        __a	      =  FlaxBlenderbotModelTester(self		)


 def 		a__					(      self		):
        __a      ,					__a	      =  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 		a__					(      self		):
        __a      ,					__a	      =  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		)


 def 		a__					(      self		):
        __a      ,					__a	      =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__		):
                      __a	      =  self._prepare_for_class(_a  ,						_a		)
                      __a	      =  model_class(_a		)

                      @jax.jit
                      def encode_jitted(lowerCamelCase  ,						lowerCamelCase=None  ,						**lowerCamelCase		):
                             return model.encode(input_ids=_a  ,						attention_mask=_a		)

                      with self.subTest("JIT Enabled"		):
                             __a	      =  encode_jitted(**_a		).to_tuple()

                      with self.subTest("JIT Disabled"		):
                             with jax.disable_jit():
                                    __a	      =  encode_jitted(**_a		).to_tuple()

                      self.assertEqual(len(_a		)  ,						len(_a		)		)
                      for jitted_output, output in zip(_a  ,						_a		):
                             self.assertEqual(jitted_output.shape  ,						output.shape		)


 def 		a__					(      self		):
        __a      ,					__a	      =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__		):
                      __a	      =  model_class(_a		)
                      __a	      =  model.encode(inputs_dict["input_ids"]  ,						inputs_dict["attention_mask"]		)

                      __a	      =  {
                          "decoder_input_ids": inputs_dict["decoder_input_ids"],
                          "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                          "encoder_outputs": encoder_outputs,
                      }

                      @jax.jit
                      def decode_jitted(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
                             return model.decode(
                                 decoder_input_ids=_a  ,						decoder_attention_mask=_a  ,						encoder_outputs=_a  ,						)

                      with self.subTest("JIT Enabled"		):
                             __a	      =  decode_jitted(**_a		).to_tuple()

                      with self.subTest("JIT Disabled"		):
                             with jax.disable_jit():
                                    __a	      =  decode_jitted(**_a		).to_tuple()

                      self.assertEqual(len(_a		)  ,						len(_a		)		)
                      for jitted_output, output in zip(_a  ,						_a		):
                             self.assertEqual(jitted_output.shape  ,						output.shape		)


 @slow
 def 		a__					(      self		):
        for model_class_name in self.all_model_classes:
               __a	      =  model_class_name.from_pretrained("facebook/blenderbot-400M-distill"		)
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               __a	      =  np.ones((1, 1)		) * model.config.eos_token_id
               __a	      =  model(_a		)
               self.assertIsNotNone(_a		)






 @unittest.skipUnless(jax_device != "cpu"  ,						"3B test too slow on CPU."		)
 @slow
 def 		a__					(      self		):
        __a	      =  {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
        __a	      =  {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

        __a	      =  FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"  ,						from_pt=_a		)
        __a	      =  BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"		)

        __a	      =  ["Sam"]
        __a	      =  tokenizer(_a  ,						return_tensors="jax"		)

        __a	      =  model.generate(**_a  ,						**_a		)
        __a	      =  "Sam is a great name. It means \"sun\" in Gaelic."

        __a	      =  tokenizer.batch_decode(_a  ,						**_a		)
        assert generated_txt[0].strip() == tgt_text




"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		__a           =			set()

		# Replace all the whitespace in our sentence
		__a           =			input_str.replace(''' '''    ,					''''''			)
		for alpha in input_str:
				if "a" <= alpha.lower() <= "z":
						frequency.add(alpha.lower()			)
		return len(lowerCAmelCase__			) == 26

def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		__a           =			[False] * 26
		for char in input_str:
				if char.islower():
						__a           =			True
				elif char.isupper():
						__a           =			True
		return all(lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		return len({char for char in input_str.lower() if char.isalpha()}			) == 26

def        lowercase							(		) ->      None:
		from timeit import timeit

		__a           =			'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
		print(timeit('''is_pangram()'''    ,					setup=lowerCAmelCase__			)			)
		print(timeit('''is_pangram_faster()'''    ,					setup=lowerCAmelCase__			)			)
		print(timeit('''is_pangram_fastest()'''    ,					setup=lowerCAmelCase__			)			)
		# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
		# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()






