





import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map


def 							__snake_case							(    lowerCAmelCase_	)				->						List[Tuple[int, ...]]:
    SCREAMING_SNAKE_CASE__											=			[]
    if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):
        for v in tree.values():
            shapes.extend(_fetch_dims(lowerCAmelCase_	)	)
    elif isinstance(lowerCAmelCase_ ,							(list, tuple)	):
        for t in tree:
            shapes.extend(_fetch_dims(lowerCAmelCase_	)	)
    elif isinstance(lowerCAmelCase_ ,							torch.Tensor	):
        shapes.append(tree.shape	)
    else:
        raise ValueError('''Not supported'''	)

    return shapes


@torch.jit.ignore
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple[int, ...]:
    SCREAMING_SNAKE_CASE__											=			[]
    for d in reversed(lowerCAmelCase_	):
        idx.append(flat_idx % d	)
        SCREAMING_SNAKE_CASE__											=			flat_idx // d

    return tuple(reversed(lowerCAmelCase_	)	)


@torch.jit.ignore
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = None ,							lowerCAmelCase_ = None ,							)				->						List[Tuple[slice, ...]]:

    # start_edges and end_edges both indicate whether, starting from any given
    # dimension, the start/end index is at the top/bottom edge of the
    # corresponding tensor, modeled as a tree
    def reduce_edge_list(lowerCAmelCase_	) -> None:
        SCREAMING_SNAKE_CASE__											=			True
        for i in range(len(lowerCAmelCase_	)	):
            SCREAMING_SNAKE_CASE__											=			-1 * (i + 1)
            l[reversed_idx] &= tally
            SCREAMING_SNAKE_CASE__											=			l[reversed_idx]

    if start_edges is None:
        SCREAMING_SNAKE_CASE__											=			[s == 0 for s in start]
        reduce_edge_list(lowerCAmelCase_	)
    if end_edges is None:
        SCREAMING_SNAKE_CASE__											=			[e == (d - 1) for e, d in zip(lowerCAmelCase_ ,							lowerCAmelCase_	)]
        reduce_edge_list(lowerCAmelCase_	)

    # Base cases. Either start/end are empty and we're done, or the final,
    # one-dimensional tensor can be simply sliced
    if len(lowerCAmelCase_	) == 0:
        return [()]
    elif len(lowerCAmelCase_	) == 1:
        return [(slice(start[0] ,							end[0] + 1	),)]

    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]

    # Dimensions common to start and end can be selected directly
    for s, e in zip(lowerCAmelCase_ ,							lowerCAmelCase_	):
        if s == e:
            path_list.append(slice(lowerCAmelCase_ ,							s + 1	)	)
        else:
            break

    SCREAMING_SNAKE_CASE__											=			tuple(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			len(lowerCAmelCase_	)

    # start == end, and we're done
    if divergence_idx == len(lowerCAmelCase_	):
        return [path]

    def upper() -> Tuple[Tuple[slice, ...], ...]:
        assert start_edges is not None
        assert end_edges is not None

        SCREAMING_SNAKE_CASE__											=			start[divergence_idx]
        return tuple(
            path + (slice(lowerCAmelCase_ ,							sdi + 1	),) + s
            for s in _get_minimal_slice_set(
                start[divergence_idx + 1 :] ,							[d - 1 for d in dims[divergence_idx + 1 :]] ,							dims[divergence_idx + 1 :] ,							start_edges=start_edges[divergence_idx + 1 :] ,							end_edges=[True for _ in end_edges[divergence_idx + 1 :]] ,							)	)

    def lower() -> Tuple[Tuple[slice, ...], ...]:
        assert start_edges is not None
        assert end_edges is not None

        SCREAMING_SNAKE_CASE__											=			end[divergence_idx]
        return tuple(
            path + (slice(lowerCAmelCase_ ,							edi + 1	),) + s
            for s in _get_minimal_slice_set(
                [0 for _ in start[divergence_idx + 1 :]] ,							end[divergence_idx + 1 :] ,							dims[divergence_idx + 1 :] ,							start_edges=[True for _ in start_edges[divergence_idx + 1 :]] ,							end_edges=end_edges[divergence_idx + 1 :] ,							)	)

    # If both start and end are at the edges of the subtree rooted at
    # divergence_idx, we can just select the whole subtree at once
    if start_edges[divergence_idx] and end_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx] ,							end[divergence_idx] + 1	),)	)
    # If just start is at the edge, we can grab almost all of the subtree,
    # treating only the ragged bottom edge as an edge case
    elif start_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx] ,							end[divergence_idx]	),)	)
        slices.extend(lower()	)
    # Analogous to the previous case, but the top is ragged this time
    elif end_edges[divergence_idx]:
        slices.extend(upper()	)
        slices.append(path + (slice(start[divergence_idx] + 1 ,							end[divergence_idx] + 1	),)	)
    # If both sides of the range are ragged, we need to handle both sides
    # separately. If there's contiguous meat in between them, we can index it
    # in one big chunk
    else:
        slices.extend(upper()	)
        SCREAMING_SNAKE_CASE__											=			end[divergence_idx] - start[divergence_idx]
        if middle_ground > 1:
            slices.append(path + (slice(start[divergence_idx] + 1 ,							end[divergence_idx]	),)	)
        slices.extend(lower()	)

    return slices


@torch.jit.ignore
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						torch.Tensor:
    SCREAMING_SNAKE_CASE__											=			t.shape[:no_batch_dims]
    SCREAMING_SNAKE_CASE__											=			list(_flat_idx_to_idx(lowerCAmelCase_ ,							lowerCAmelCase_	)	)
    # _get_minimal_slice_set is inclusive
    SCREAMING_SNAKE_CASE__											=			list(_flat_idx_to_idx(flat_end - 1 ,							lowerCAmelCase_	)	)

    # Get an ordered list of slices to perform
    SCREAMING_SNAKE_CASE__											=			_get_minimal_slice_set(
        lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							)

    SCREAMING_SNAKE_CASE__											=			[t[s] for s in slices]

    return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]	) for s in sliced_tensors]	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = False ,							lowerCAmelCase_ = None ,							lowerCAmelCase_ = False ,							)				->						Any:
    if not (len(lowerCAmelCase_	) > 0):
        raise ValueError('''Must provide at least one input'''	)

    SCREAMING_SNAKE_CASE__											=			[shape[:no_batch_dims] for shape in _fetch_dims(lowerCAmelCase_	)]
    SCREAMING_SNAKE_CASE__											=			tuple([max(lowerCAmelCase_	) for s in zip(*lowerCAmelCase_	)]	)

    def _prep_inputs(lowerCAmelCase_	) -> torch.Tensor:
        if not low_mem:
            if not sum(t.shape[:no_batch_dims]	) == no_batch_dims:
                SCREAMING_SNAKE_CASE__											=			t.expand(orig_batch_dims + t.shape[no_batch_dims:]	)
            SCREAMING_SNAKE_CASE__											=			t.reshape(-1 ,							*t.shape[no_batch_dims:]	)
        else:
            SCREAMING_SNAKE_CASE__											=			t.expand(orig_batch_dims + t.shape[no_batch_dims:]	)
        return t

    SCREAMING_SNAKE_CASE__											=			tensor_tree_map(_prep_inputs ,							lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			None
    if _out is not None:
        SCREAMING_SNAKE_CASE__											=			tensor_tree_map(lambda lowerCAmelCase_	: t.view([-1] + list(t.shape[no_batch_dims:]	)	) ,							_out	)

    SCREAMING_SNAKE_CASE__											=			1
    for d in orig_batch_dims:
        flat_batch_dim *= d

    SCREAMING_SNAKE_CASE__											=			flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

    def _select_chunk(lowerCAmelCase_	) -> torch.Tensor:
        return t[i : i + chunk_size] if t.shape[0] != 1 else t

    SCREAMING_SNAKE_CASE__											=			0
    SCREAMING_SNAKE_CASE__											=			prepped_outputs
    for _ in range(lowerCAmelCase_	):
        # Chunk the input
        if not low_mem:
            SCREAMING_SNAKE_CASE__											=			_select_chunk
        else:
            SCREAMING_SNAKE_CASE__											=			partial(
                _chunk_slice ,							flat_start=lowerCAmelCase_ ,							flat_end=min(lowerCAmelCase_ ,							i + chunk_size	) ,							no_batch_dims=len(lowerCAmelCase_	) ,							)

        SCREAMING_SNAKE_CASE__											=			tensor_tree_map(lowerCAmelCase_ ,							lowerCAmelCase_	)

        # Run the layer on the chunk
        SCREAMING_SNAKE_CASE__											=			layer(**lowerCAmelCase_	)

        # Allocate space for the output
        if out is None:
            SCREAMING_SNAKE_CASE__											=			tensor_tree_map(lambda lowerCAmelCase_	: t.new_zeros((flat_batch_dim,) + t.shape[1:]	) ,							lowerCAmelCase_	)

        # Put the chunk in its pre-allocated space
        if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):

            def assign(lowerCAmelCase_ ,							lowerCAmelCase_	) -> None:
                for k, v in da.items():
                    if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):
                        assign(lowerCAmelCase_ ,							da[k]	)
                    else:
                        if _add_into_out:
                            v[i : i + chunk_size] += da[k]
                        else:
                            SCREAMING_SNAKE_CASE__											=			da[k]

            assign(lowerCAmelCase_ ,							lowerCAmelCase_	)
        elif isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):
            for xa, xa in zip(lowerCAmelCase_ ,							lowerCAmelCase_	):
                if _add_into_out:
                    xa[i : i + chunk_size] += xa
                else:
                    SCREAMING_SNAKE_CASE__											=			xa
        elif isinstance(lowerCAmelCase_ ,							torch.Tensor	):
            if _add_into_out:
                out[i : i + chunk_size] += output_chunk
            else:
                SCREAMING_SNAKE_CASE__											=			output_chunk
        else:
            raise ValueError('''Not supported'''	)

        i += chunk_size

    SCREAMING_SNAKE_CASE__											=			tensor_tree_map(lambda lowerCAmelCase_	: t.view(orig_batch_dims + t.shape[1:]	) ,							lowerCAmelCase_	)

    return out



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_ = 5_12   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			max_chunk_size
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        logging.info('''Tuning chunk size...'''     )

        if min_chunk_size >= self.max_chunk_size:
            return min_chunk_size

        SCREAMING_SNAKE_CASE__											=			[2**l for l in range(int(math.log(self.max_chunk_size   ,				2     )     ) + 1     )]
        SCREAMING_SNAKE_CASE__											=			[c for c in candidates if c > min_chunk_size]
        SCREAMING_SNAKE_CASE__											=			[min_chunk_size] + candidates
        candidates[-1] += 4

        def test_chunk_size(A_     ) -> bool:
            try:
                with torch.no_grad():
                    fn(*A_   ,				chunk_size=A_     )
                return True
            except RuntimeError:
                return False

        SCREAMING_SNAKE_CASE__											=			0
        SCREAMING_SNAKE_CASE__											=			len(A_     ) - 1
        while i > min_viable_chunk_size_index:
            SCREAMING_SNAKE_CASE__											=			test_chunk_size(candidates[i]     )
            if not viable:
                SCREAMING_SNAKE_CASE__											=			(min_viable_chunk_size_index + i) // 2
            else:
                SCREAMING_SNAKE_CASE__											=			i
                SCREAMING_SNAKE_CASE__											=			(i + len(A_     ) - 1) // 2

        return candidates[min_viable_chunk_size_index]



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			True
        for aa, aa in zip(A_   ,				A_     ):
            assert type(A_     ) == type(A_     )
            if isinstance(A_   ,				(list, tuple)     ):
                consistent &= self._compare_arg_caches(A_   ,				A_     )
            elif isinstance(A_   ,				A_     ):
                SCREAMING_SNAKE_CASE__											=			[v for _, v in sorted(aa.items()   ,				key=lambda A_     : x[0]     )]
                SCREAMING_SNAKE_CASE__											=			[v for _, v in sorted(aa.items()   ,				key=lambda A_     : x[0]     )]
                consistent &= self._compare_arg_caches(A_   ,				A_     )
            else:
                consistent &= aa == aa

        return consistent




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			tree_map(lambda A_     : a.shape if isinstance(A_   ,				torch.Tensor     ) else a   ,				A_   ,				A_     )
        if self.cached_arg_data is not None:
            # If args have changed shape/value, we need to re-tune
            assert len(self.cached_arg_data     ) == len(A_     )
            SCREAMING_SNAKE_CASE__											=			self._compare_arg_caches(self.cached_arg_data   ,				A_     )
        else:
            # Otherwise, we can reuse the precomputed value
            SCREAMING_SNAKE_CASE__											=			False

        if not consistent:
            SCREAMING_SNAKE_CASE__											=			self._determine_favorable_chunk_size(
                A_   ,				A_   ,				A_   ,				)
            SCREAMING_SNAKE_CASE__											=			arg_data

        assert self.cached_chunk_size is not None

        return self.cached_chunk_size







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 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_=14   ,				A_=7   ,				A_=True   ,				A_=True   ,				A_=True   ,				A_=True   ,				A_=True   ,				A_=99   ,				A_=32   ,				A_=5   ,				A_=4   ,				A_=37   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=5_12   ,				A_=16   ,				A_=2   ,				A_=0.02   ,				A_=3   ,				A_=4   ,				A_=None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			seq_length
        SCREAMING_SNAKE_CASE__											=			is_training
        SCREAMING_SNAKE_CASE__											=			use_token_type_ids
        SCREAMING_SNAKE_CASE__											=			use_input_mask
        SCREAMING_SNAKE_CASE__											=			use_labels
        SCREAMING_SNAKE_CASE__											=			use_mc_token_ids
        SCREAMING_SNAKE_CASE__											=			vocab_size
        SCREAMING_SNAKE_CASE__											=			hidden_size
        SCREAMING_SNAKE_CASE__											=			num_hidden_layers
        SCREAMING_SNAKE_CASE__											=			num_attention_heads
        SCREAMING_SNAKE_CASE__											=			intermediate_size
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			hidden_dropout_prob
        SCREAMING_SNAKE_CASE__											=			attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__											=			max_position_embeddings
        SCREAMING_SNAKE_CASE__											=			type_vocab_size
        SCREAMING_SNAKE_CASE__											=			type_sequence_label_size
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			num_labels
        SCREAMING_SNAKE_CASE__											=			num_choices
        SCREAMING_SNAKE_CASE__											=			scope
        SCREAMING_SNAKE_CASE__											=			self.vocab_size - 1



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )

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

        SCREAMING_SNAKE_CASE__											=			None
        if self.use_token_type_ids:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.type_vocab_size     )

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

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None
        if self.use_labels:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.type_sequence_label_size     )
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels     )
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.num_choices     )

        SCREAMING_SNAKE_CASE__											=			self.get_config()

        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self     ):





        '''simple docstring'''

        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  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLModel(config=A_     )
        model.to(A_     )
        model.eval()

        model(A_   ,				token_type_ids=A_   ,				head_mask=A_     )
        model(A_   ,				token_type_ids=A_     )
        SCREAMING_SNAKE_CASE__											=			model(A_     )
        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  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLLMHeadModel(A_     )
        model.to(A_     )
        model.eval()

        SCREAMING_SNAKE_CASE__											=			model(A_   ,				token_type_ids=A_   ,				labels=A_     )
        self.parent.assertEqual(result.loss.shape   ,				()     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.prepare_config_and_inputs()

        (
            (
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		
        )											=			config_and_inputs

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

        return config, inputs_dict




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.num_labels
        SCREAMING_SNAKE_CASE__											=			CTRLForSequenceClassification(A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.type_sequence_label_size     )
        SCREAMING_SNAKE_CASE__											=			model(A_   ,				token_type_ids=A_   ,				labels=A_     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)     )





@require_torch
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Union[str, Any]				=	(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
    lowerCamelCase__    :		List[Any]				=	(CTRLLMHeadModel,) if is_torch_available() else ()
    lowerCamelCase__    :		Tuple				=	(
        {
            """feature-extraction""": CTRLModel,
            """text-classification""": CTRLForSequenceClassification,
            """text-generation""": CTRLLMHeadModel,
            """zero-shot""": CTRLForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase__    :		List[Any]				=	True
    lowerCamelCase__    :		Optional[int]				=	False
    lowerCamelCase__    :		Any				=	False



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLModelTester(self     )
        SCREAMING_SNAKE_CASE__											=			ConfigTester(self   ,				config_class=A_   ,				n_embd=37     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.config_tester.run_common_tests()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_model(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*A_     )



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





        '''simple docstring'''

        pass



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__											=			CTRLModel.from_pretrained(A_     )
            self.assertIsNotNone(A_     )




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





        '''simple docstring'''

        pass





@require_torch
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

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




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CTRLLMHeadModel.from_pretrained('''ctrl'''     )
        model.to(A_     )
        SCREAMING_SNAKE_CASE__											=			torch.tensor(
            [[1_18_59, 0, 16_11, 8]]   ,				dtype=torch.long   ,				device=A_     )  # Legal the president is
        SCREAMING_SNAKE_CASE__											=			[
            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

        SCREAMING_SNAKE_CASE__											=			model.generate(A_   ,				do_sample=A_     )
        self.assertListEqual(output_ids[0].tolist()   ,				A_     )
