





'''simple docstring'''




import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
 import os

 import jax.numpy as jnp
 from jax import jit

 from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
 from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

 _SCREAMING_SNAKE_CASE							     =			"0.12"  # assumed parallelism: 8


if is_torch_available():
 import torch





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Tuple=None		)		->  Optional[Any]:
   if rng is None:
      snake_case 						=   random.Random()

   snake_case 						=   1
   for dim in shape:
      total_dims *= dim

   snake_case 						=   []
   for _ in range(__lowerCAmelCase		):
      values.append(rng.randint(0      ,    vocab_size - 1		)		)

   snake_case 						=   np.array(__lowerCAmelCase      ,    dtype=jnp.intaa		).reshape(__lowerCAmelCase		)

   return output





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Optional[int]=None		)		->  List[Any]:
   snake_case 						=   ids_tensor(__lowerCAmelCase      ,    vocab_size=2      ,    rng=__lowerCAmelCase		)
   # make sure that at least one token is attended to for each batch
   snake_case 						=   1
   return attn_mask
@require_flax
class 			_lowerCAmelCase		:






     """simple docstring"""






     snake_case_						      =   None
     snake_case_						      =   ()



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	int:
        snake_case						,    snake_case 						=   self.model_tester.prepare_config_and_inputs_for_common()

        # cut to half length & take max batch_size 3
        snake_case 						=   2
        snake_case 						=   inputs["""input_ids"""].shape[-1] // 2
        snake_case 						=   inputs["""input_ids"""][:max_batch_size, :sequence_length]

        snake_case 						=   jnp.ones_like(__snake_case						)
        snake_case 						=   attention_mask[:max_batch_size, :sequence_length]

        # generate max 5 tokens
        snake_case 						=   input_ids.shape[-1] + 5
        if config.eos_token_id is not None and config.pad_token_id is None:
           # hack to allow generate for models such as GPT2 as is done in `generate()`
           snake_case 						=   config.eos_token_id
        return config, input_ids, attention_mask, max_length



     @is_pt_flax_cross_test
     def      lowerCAmelCase			(   self							:			int						)->	str:
        snake_case						,    snake_case						,    snake_case						,    snake_case 						=   self._get_input_ids_and_config()
        snake_case 						=   False
        snake_case 						=   max_length
        snake_case 						=   0

        for model_class in self.all_generative_model_classes:
           snake_case 						=   model_class(__snake_case						)

           snake_case 						=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
           snake_case 						=   getattr(__snake_case   ,       __snake_case						)
           snake_case 						=   pt_model_class(__snake_case						).eval()
           snake_case 						=   load_flax_weights_in_pytorch_model(__snake_case   ,       flax_model.params						)

           snake_case 						=   flax_model.generate(__snake_case						).sequences
           snake_case 						=   pt_model.generate(torch.tensor(__snake_case   ,       dtype=torch.long						)						)

           if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
              snake_case 						=   flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

           self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,       flax_generation_outputs.tolist()						)



     def      lowerCAmelCase			(   self							:			int						)->	List[str]:
        snake_case						,    snake_case						,    snake_case						,    snake_case 						=   self._get_input_ids_and_config()
        snake_case 						=   False
        snake_case 						=   max_length

        for model_class in self.all_generative_model_classes:
           snake_case 						=   model_class(__snake_case						)

           snake_case 						=   model.generate(__snake_case						).sequences
           self.assertEqual(generation_outputs.shape[-1]   ,       __snake_case						)

           snake_case 						=   jit(model.generate						)
           snake_case 						=   jit_generate(__snake_case						).sequences

           self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()						)



     def      lowerCAmelCase			(   self							:			Any						)->	Union[str, Any]:
        snake_case						,    snake_case						,    snake_case						,    snake_case 						=   self._get_input_ids_and_config()
        snake_case 						=   True
        snake_case 						=   max_length

        for model_class in self.all_generative_model_classes:
           snake_case 						=   model_class(__snake_case						)

           snake_case 						=   model.generate(__snake_case						).sequences
           self.assertEqual(generation_outputs.shape[-1]   ,       __snake_case						)

           snake_case 						=   jit(model.generate						)
           snake_case 						=   jit_generate(__snake_case						).sequences

           self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()						)



     def      lowerCAmelCase			(   self							:			List[str]						)->	int:
        snake_case						,    snake_case						,    snake_case						,    snake_case 						=   self._get_input_ids_and_config()
        snake_case 						=   False
        snake_case 						=   max_length
        snake_case 						=   2

        for model_class in self.all_generative_model_classes:
           snake_case 						=   model_class(__snake_case						)

           snake_case 						=   model.generate(__snake_case						).sequences
           self.assertEqual(generation_outputs.shape[-1]   ,       __snake_case						)

           snake_case 						=   jit(model.generate						)
           snake_case 						=   jit_generate(__snake_case						).sequences

           self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()						)



     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	str:
        snake_case						,    snake_case						,    snake_case						,    snake_case 						=   self._get_input_ids_and_config()
        snake_case 						=   False
        snake_case 						=   max_length
        snake_case 						=   2
        snake_case 						=   2

        for model_class in self.all_generative_model_classes:
           snake_case 						=   model_class(__snake_case						)

           snake_case 						=   model.generate(__snake_case						).sequences
           self.assertEqual(generation_outputs.shape[0]   ,       input_ids.shape[0] * config.num_return_sequences						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	List[Any]:
        snake_case						,    snake_case						,    snake_case						,    snake_case 						=   self._get_input_ids_and_config()
        snake_case 						=   True
        snake_case 						=   max_length
        snake_case 						=   0.8
        snake_case 						=   10
        snake_case 						=   0.3
        snake_case 						=   1
        snake_case 						=   8
        snake_case 						=   9

        for model_class in self.all_generative_model_classes:
           snake_case 						=   model_class(__snake_case						)

           snake_case 						=   model.generate(__snake_case						).sequences
           self.assertEqual(generation_outputs.shape[-1]   ,       __snake_case						)

           snake_case 						=   jit(model.generate						)
           snake_case 						=   jit_generate(__snake_case						).sequences

           self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()						)



     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Union[str, Any]:
        snake_case						,    snake_case						,    snake_case						,    snake_case 						=   self._get_input_ids_and_config()
        snake_case 						=   max_length
        snake_case 						=   1
        snake_case 						=   8
        snake_case 						=   9

        for model_class in self.all_generative_model_classes:
           snake_case 						=   model_class(__snake_case						)

           snake_case 						=   model.generate(__snake_case						).sequences
           self.assertEqual(generation_outputs.shape[-1]   ,       __snake_case						)

           snake_case 						=   jit(model.generate						)
           snake_case 						=   jit_generate(__snake_case						).sequences

           self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	Optional[int]:
        snake_case						,    snake_case						,    snake_case						,    snake_case 						=   self._get_input_ids_and_config()
        snake_case 						=   max_length
        snake_case 						=   2
        snake_case 						=   1
        snake_case 						=   8
        snake_case 						=   9

        for model_class in self.all_generative_model_classes:
           snake_case 						=   model_class(__snake_case						)

           snake_case 						=   model.generate(__snake_case						).sequences
           self.assertEqual(generation_outputs.shape[-1]   ,       __snake_case						)

           snake_case 						=   jit(model.generate						)
           snake_case 						=   jit_generate(__snake_case						).sequences

           self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()						)



     def      lowerCAmelCase			(   self							:			Tuple						)->	Union[str, Any]:
        snake_case						,    snake_case						,    snake_case						,    snake_case 						=   self._get_input_ids_and_config()

        # pad attention mask on the left
        snake_case 						=   attention_mask.at[(0, 0)].set(0						)

        snake_case 						=   False
        snake_case 						=   max_length

        for model_class in self.all_generative_model_classes:
           snake_case 						=   model_class(__snake_case						)

           snake_case 						=   model.generate(__snake_case   ,       attention_mask=__snake_case						).sequences
           self.assertEqual(generation_outputs.shape[-1]   ,       __snake_case						)

           snake_case 						=   jit(model.generate						)
           snake_case 						=   jit_generate(__snake_case   ,       attention_mask=__snake_case						).sequences

           self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	str:
        snake_case						,    snake_case						,    snake_case						,    snake_case 						=   self._get_input_ids_and_config()

        # pad attention mask on the left
        snake_case 						=   attention_mask.at[(0, 0)].set(0						)

        snake_case 						=   True
        snake_case 						=   max_length

        for model_class in self.all_generative_model_classes:
           snake_case 						=   model_class(__snake_case						)

           snake_case 						=   model.generate(__snake_case   ,       attention_mask=__snake_case						).sequences
           self.assertEqual(generation_outputs.shape[-1]   ,       __snake_case						)

           snake_case 						=   jit(model.generate						)
           snake_case 						=   jit_generate(__snake_case   ,       attention_mask=__snake_case						).sequences

           self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()						)



     def      lowerCAmelCase			(   self							:			int						)->	Tuple:
        snake_case						,    snake_case						,    snake_case						,    snake_case 						=   self._get_input_ids_and_config()

        # pad attention mask on the left
        snake_case 						=   attention_mask.at[(0, 0)].set(0						)

        snake_case 						=   2
        snake_case 						=   max_length

        for model_class in self.all_generative_model_classes:
           snake_case 						=   model_class(__snake_case						)

           snake_case 						=   model.generate(__snake_case   ,       attention_mask=__snake_case						).sequences
           self.assertEqual(generation_outputs.shape[-1]   ,       __snake_case						)

           snake_case 						=   jit(model.generate						)
           snake_case 						=   jit_generate(__snake_case   ,       attention_mask=__snake_case						).sequences

           self.assertListEqual(generation_outputs.tolist()   ,       jit_generation_outputs.tolist()						)




@require_flax
class 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     def      lowerCAmelCase			(   self							:			Tuple						)->	str:
        snake_case 						=   AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""						)
        snake_case 						=   FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""						)

        snake_case 						=   """Hello world"""
        snake_case 						=   tokenizer(__snake_case   ,       return_tensors="""np"""						).input_ids

        # typos are quickly detected (the correct argument is `do_sample`)
        with self.assertRaisesRegex(__snake_case   ,       """do_samples"""						):
           model.generate(__snake_case   ,       do_samples=__snake_case						)

        # arbitrary arguments that will not be used anywhere are also not accepted
        with self.assertRaisesRegex(__snake_case   ,       """foo"""						):
           snake_case 						=   {"""foo""": """bar"""}
           model.generate(__snake_case   ,       **__snake_case						)








'''simple docstring'''




from ....configuration_utils import PretrainedConfig
from ....utils import logging


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "trajectory_transformer"
     snake_case_						      =   ["past_key_values"]
     snake_case_						      =   {
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }



     def __init__(   self							:			Optional[Any]   ,       __snake_case							:			int=1_00   ,       __snake_case							:			List[str]=5   ,       __snake_case							:			str=1   ,       __snake_case							:			Union[str, Any]=1   ,       __snake_case							:			Union[str, Any]=2_49   ,       __snake_case							:			List[Any]=6   ,       __snake_case							:			Optional[int]=17   ,       __snake_case							:			Optional[Any]=25   ,       __snake_case							:			Union[str, Any]=4   ,       __snake_case							:			List[Any]=4   ,       __snake_case							:			Optional[int]=1_28   ,       __snake_case							:			Optional[Any]=0.1   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			int=0.1   ,       __snake_case							:			Union[str, Any]=0.00_06   ,       __snake_case							:			Tuple=5_12   ,       __snake_case							:			int=0.02   ,       __snake_case							:			Any=1e-12   ,       __snake_case							:			Optional[Any]=1   ,       __snake_case							:			List[Any]=True   ,       __snake_case							:			Dict=1   ,       __snake_case							:			Dict=5_02_56   ,       __snake_case							:			Union[str, Any]=5_02_56   ,       **__snake_case							:			Tuple   ,       )->	Optional[int]:
        snake_case 						=   vocab_size
        snake_case 						=   action_weight
        snake_case 						=   reward_weight
        snake_case 						=   value_weight
        snake_case 						=   max_position_embeddings
        snake_case 						=   block_size
        snake_case 						=   action_dim
        snake_case 						=   observation_dim
        snake_case 						=   transition_dim
        snake_case 						=   learning_rate
        snake_case 						=   n_layer
        snake_case 						=   n_head
        snake_case 						=   n_embd
        snake_case 						=   embd_pdrop
        snake_case 						=   attn_pdrop
        snake_case 						=   resid_pdrop
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   kaiming_initializer_range
        snake_case 						=   use_cache
        super().__init__(pad_token_id=__snake_case   ,       bos_token_id=__snake_case   ,       eos_token_id=__snake_case   ,       **__snake_case						)

