
'''simple docstring'''
import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      CTRLForSequenceClassification,
      CTRLLMHeadModel,
      CTRLModel,
  )






class    UpperCAmelCase_				:





 def __init__(    self ,       lowercase_ ,       lowercase_=14 ,       lowercase_=7 ,       lowercase_=True ,       lowercase_=True ,       lowercase_=True ,       lowercase_=True ,       lowercase_=True ,       lowercase_=99 ,       lowercase_=32 ,       lowercase_=5 ,       lowercase_=4 ,       lowercase_=37 ,       lowercase_="gelu" ,       lowercase_=0.1 ,       lowercase_=0.1 ,       lowercase_=5_12 ,       lowercase_=16 ,       lowercase_=2 ,       lowercase_=0.02 ,       lowercase_=3 ,       lowercase_=4 ,       lowercase_=None ,       ):
     snake_case_ :    str     			=						parent
     snake_case_ :    Optional[Any]     			=						batch_size
     snake_case_ :    Optional[int]     			=						seq_length
     snake_case_ :    Any     			=						is_training
     snake_case_ :    List[str]     			=						use_token_type_ids
     snake_case_ :    Any     			=						use_input_mask
     snake_case_ :    Any     			=						use_labels
     snake_case_ :    Tuple     			=						use_mc_token_ids
     snake_case_ :    Union[str, Any]     			=						vocab_size
     snake_case_ :    int     			=						hidden_size
     snake_case_ :    Any     			=						num_hidden_layers
     snake_case_ :    Union[str, Any]     			=						num_attention_heads
     snake_case_ :    List[Any]     			=						intermediate_size
     snake_case_ :    List[str]     			=						hidden_act
     snake_case_ :    Tuple     			=						hidden_dropout_prob
     snake_case_ :    str     			=						attention_probs_dropout_prob
     snake_case_ :    str     			=						max_position_embeddings
     snake_case_ :    int     			=						type_vocab_size
     snake_case_ :    int     			=						type_sequence_label_size
     snake_case_ :    Optional[Any]     			=						initializer_range
     snake_case_ :    Optional[int]     			=						num_labels
     snake_case_ :    List[Any]     			=						num_choices
     snake_case_ :    List[Any]     			=						scope
     snake_case_ :    int     			=						self.vocab_size - 1





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size)

     snake_case_ :    Tuple     			=						None
     if self.use_input_mask:
         snake_case_ :    Tuple     			=						random_attention_mask([self.batch_size, self.seq_length])

     snake_case_ :    str     			=						None
     if self.use_token_type_ids:
         snake_case_ :    Union[str, Any]     			=						ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size)

     snake_case_ :    Any     			=						None
     if self.use_mc_token_ids:
         snake_case_ :    Tuple     			=						ids_tensor([self.batch_size, self.num_choices] ,       self.seq_length)

     snake_case_ :    int     			=						None
     snake_case_ :    Optional[Any]     			=						None
     snake_case_ :    Union[str, Any]     			=						None
     if self.use_labels:
         snake_case_ :    Any     			=						ids_tensor([self.batch_size] ,       self.type_sequence_label_size)
         snake_case_ :    List[Any]     			=						ids_tensor([self.batch_size, self.seq_length] ,       self.num_labels)
         snake_case_ :    Union[str, Any]     			=						ids_tensor([self.batch_size] ,       self.num_choices)

     snake_case_ :    Tuple     			=						self.get_config()

     snake_case_ :    Union[str, Any]     			=						ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,       2)

     return (
         config,
         input_ids,
         input_mask,
         head_mask,
         token_type_ids,
         mc_token_ids,
         sequence_labels,
         token_labels,
         choice_labels,
     )





 def        snake_case__		(    self):
     return CTRLConfig(
         vocab_size=self.vocab_size ,       n_embd=self.hidden_size ,       n_layer=self.num_hidden_layers ,       n_head=self.num_attention_heads ,       n_positions=self.max_position_embeddings ,       pad_token_id=self.pad_token_id ,       )





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       *lowercase_):
     snake_case_ :    List[str]     			=						CTRLModel(config=lowercase_)
     model.to(lowercase_)
     model.eval()

     model(lowercase_ ,       token_type_ids=lowercase_ ,       head_mask=lowercase_)
     model(lowercase_ ,       token_type_ids=lowercase_)
     snake_case_ :    Any     			=						model(lowercase_)
     self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size))
     self.parent.assertEqual(len(result.past_key_values) ,       config.n_layer)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       *lowercase_):
     snake_case_ :    Tuple     			=						CTRLLMHeadModel(lowercase_)
     model.to(lowercase_)
     model.eval()

     snake_case_ :    int     			=						model(lowercase_ ,       token_type_ids=lowercase_ ,       labels=lowercase_)
     self.parent.assertEqual(result.loss.shape ,       ())
     self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.vocab_size))





 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						self.prepare_config_and_inputs()

     (
         (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, (
         snake_case_
     )			, 
     ) :    Optional[Any]     			=						config_and_inputs

     snake_case_ :    Tuple     			=						{"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

     return config, inputs_dict





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       *lowercase_):
     snake_case_ :    int     			=						self.num_labels
     snake_case_ :    Optional[int]     			=						CTRLForSequenceClassification(lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_ :    int     			=						ids_tensor([self.batch_size] ,       self.type_sequence_label_size)
     snake_case_ :    Optional[Any]     			=						model(lowercase_ ,       token_type_ids=lowercase_ ,       labels=lowercase_)
     self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels))






@require_torch
class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
 UpperCAmelCase_		    =						(CTRLLMHeadModel,) if is_torch_available() else ()
 UpperCAmelCase_		    =						(
     {
         """feature-extraction""": CTRLModel,
         """text-classification""": CTRLForSequenceClassification,
         """text-generation""": CTRLLMHeadModel,
         """zero-shot""": CTRLForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 UpperCAmelCase_		    =						True
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_):
     if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
         # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
         # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
         # config could not be created.
         return True

     return False





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						CTRLModelTester(self)
     snake_case_ :    str     			=						ConfigTester(self ,       config_class=lowercase_ ,       n_embd=37)





 def        snake_case__		(    self):
     super().tearDown()
     # clean-up as much as possible GPU memory occupied by PyTorch
     gc.collect()
     torch.cuda.empty_cache()





 def        snake_case__		(    self):
     self.config_tester.run_common_tests()





 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_ctrl_model(*lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_lm_head_model(*lowercase_)





 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
 def        snake_case__		(    self):
     pass





 @slow
 def        snake_case__		(    self):
     for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_ :    Dict     			=						CTRLModel.from_pretrained(lowercase_)
         self.assertIsNotNone(lowercase_)





 @unittest.skip("The model doesn't support left padding")  # and it's not used enough to be worth fixing :)
 def        snake_case__		(    self):
     pass






@require_torch
class    UpperCAmelCase_				(	unittest.TestCase					):





 def        snake_case__		(    self):
     super().tearDown()
     # clean-up as much as possible GPU memory occupied by PyTorch
     gc.collect()
     torch.cuda.empty_cache()





 @slow
 def        snake_case__		(    self):
     snake_case_ :    str     			=						CTRLLMHeadModel.from_pretrained("ctrl")
     model.to(lowercase_)
     snake_case_ :    Union[str, Any]     			=						torch.tensor(
         [[1_18_59, 0, 16_11, 8]] ,       dtype=torch.long ,       device=lowercase_)  # Legal the president is
     snake_case_ :    Tuple     			=						[
         1_18_59,
         0,
         16_11,
         8,
         5,
         1_50,
         2_64_49,
         2,
         19,
         3_48,
         4_69,
         3,
         25_95,
         48,
         2_07_40,
         24_65_33,
         24_65_33,
         19,
         30,
         5,
     ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

     snake_case_ :    Optional[Any]     			=						model.generate(lowercase_ ,       do_sample=lowercase_)
     self.assertListEqual(output_ids[0].tolist() ,       lowercase_)






'''simple docstring'''
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


a_											=						logging.get_logger(__name__)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						r"\w+[.]\d+"
    snake_case_ :    Tuple     			=						re.findall(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    for pat in pats:
        snake_case_ :    Union[str, Any]     			=						key.replace(__SCREAMING_SNAKE_CASE,		"_".join(pat.split("."       )       )       )
    return key

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Tuple     			=						pt_tuple_key[:-1] + ("scale",)
    if (
        any("norm" in str_ for str_ in pt_tuple_key       )
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        snake_case_ :    int     			=						pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        snake_case_ :    Dict     			=						pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        snake_case_ :    List[Any]     			=						pt_tuple_key[:-1] + ("embedding",)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    snake_case_ :    List[str]     			=						pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        snake_case_ :    str     			=						pt_tensor.transpose(2,		3,		1,		0       )
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    snake_case_ :    str     			=						pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight":
        snake_case_ :    Any     			=						pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    snake_case_ :    Union[str, Any]     			=						pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    snake_case_ :    Union[str, Any]     			=						pt_tuple_key[:-1] + ("bias",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor


def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=4_2       ):
    """simple docstring"""






    snake_case_ :    int     			=						{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    snake_case_ :    Optional[int]     			=						flax_model.init_weights(PRNGKey(__SCREAMING_SNAKE_CASE       )       )

    snake_case_ :    Union[str, Any]     			=						flatten_dict(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        snake_case_ :    List[str]     			=						rename_key(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Tuple     			=						tuple(renamed_pt_key.split("."       )       )

        # Correctly rename weight parameters
        snake_case_			, snake_case_ :    Dict     			=						rename_key_and_reshape_tensor(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                    f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'       )

        # also add unexpected weight so that warning is thrown
        snake_case_ :    Any     			=						jnp.asarray(__SCREAMING_SNAKE_CASE       )

    return unflatten_dict(__SCREAMING_SNAKE_CASE       )




